IP Library › Granted Patent US 12,591,780
Granted Patent B2
US 12,591,780 · App. 17/179,395 · Granted Mar 31, 2026

Data compression for artificial intelligence-based base calling

Inventors: Gery Vessere (Oakland, CA); Gavin Derek Parnaby (Laguna Niguel, CA); Anindita Dutta (San Francisco, CA); Dorna Kashefhaghighi (Menlo Park, CA); Kishore Jaganathan (San Francisco, CA); Amirali Kia (San Mateo, CA)
Assignee: Illumina, Inc.
G06N3/084G06F18/23G06N3/063G06V10/454G06V10/762G06V10/764G06V10/7715G06V10/82G16B30/20C12Q1/6869
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Quick Facts
Patent No.
US 12,591,780
App. No.
17/179,395
Filed
Feb 18, 2021
Granted
Mar 31, 2026
Kind
B2
Art Unit
1687
USPC
702/20
Abstract

The technology disclosed relates to an artificial intelligence-based method of base calling. In particular, it relates to processing, through a spatial network of a neural network-based base caller, a first window of per-cycle analyte channel sets in for a first window of sequencing cycles of a sequencing run, and generating respective sequences of spatial output sets for respective sequencing cycles in the first window of sequencing cycles, processing, through a compression network of the neural network-based base caller, respective final spatial output sets in the respective sequences of spatial output sets, and generating respective compressed spatial output sets for the respective sequencing cycles in the first window of sequencing cycles, and generating, based on the respective compressed spatial output sets, base call predictions for one or more sequencing cycles in the first window of sequencing cycles.

Claims (58)

1 . An artificial intelligence-based method of base calling, the method including:

accessing a series of per-cycle analyte channel sets generated for sequencing cycles of a sequencing run;

processing, through a spatial network of a neural network-based base caller, a first window of per-cycle analyte channel sets in the series for a first window of sequencing cycles of the sequencing run, and generating respective sequences of spatial output sets for respective sequencing cycles in the first window of sequencing cycles;

processing, through a compression network of the neural network-based base caller, respective final spatial output sets in the respective sequences of spatial output sets by applying a number of convolution filters based on a number of channels in the per-cycle analyte channel sets, and generating respective compressed spatial output sets for the respective sequencing cycles in the first window of sequencing cycles;

storing, in memory, the respective compressed spatial output sets for the respective sequencing cycles; and

generating, based on the respective compressed spatial output sets stored in memory, base call predictions for one or more sequencing cycles in the first window of sequencing cycles.

2 . The artificial intelligence-based method of claim 1 , wherein the channels correspond to at least a filter wavelength, an imaging event, or illumination of a specific laser through a specific optical filter utilized during the sequencing run.

3 . The artificial intelligence-based method of claim 1 , further including:

for a second window of sequencing cycles of the sequencing run that shares, with the first window of sequencing cycles, one or more overlapping sequencing cycles for which the spatial network previously generated spatial output sets, and at least one non-overlapping sequencing cycle,

processing, through the spatial network, a per-cycle analyte channel set only for the at least one non-overlapping sequencing cycle, and generating a sequence of spatial output sets for the at least one non-overlapping sequencing cycle;

processing, through the compression network, a final spatial output set in the sequence of spatial output sets, and generating a compressed spatial output set for the at least one non-overlapping sequencing cycle, wherein the final spatial output set has M channels, wherein the compressed spatial output set has N channels, and wherein M>N; and

generating, based on respective compressed spatial output sets for the overlapping sequencing cycles previously generated for the first window of sequencing cycles and on the compressed spatial output set for the at least one non-overlapping sequencing cycle, base call predictions for one or more sequencing cycles in the second window of sequencing cycles.

4 . The artificial intelligence-based method of claim 3 , further including:

for a third window of sequencing cycles of the sequencing run that shares, with the first and second windows of sequencing cycles, one or more overlapping sequencing cycles for which the spatial network previously generated spatial output sets, and at least one non-overlapping sequencing cycle for the third window,

processing, through the spatial network, a per-cycle analyte channel set only for the at least one non-overlapping sequencing cycle for the third window, and generating a sequence of spatial output sets for the at least one non-overlapping sequencing cycle for the third window;

processing, through the compression network, a final spatial output set in the sequence of spatial output sets, and generating a compressed spatial output set for the at least one non-overlapping sequencing cycle for the third window, wherein the final spatial output set has M channels, wherein the compressed spatial output set has N channels, and wherein M>N; and

generating, based on respective compressed spatial output sets for the overlapping sequencing cycles previously generated for the first and second windows of sequencing cycles and on the compressed spatial output set for the at least one non-overlapping sequencing cycle of the third window, base call predictions for one or more sequencing cycles in the third window of sequencing cycles.

5 . The artificial intelligence-based method of claim 1 , wherein each per-cycle analyte channel set in the series depicts intensities registered in response to nucleotide incorporation in analytes at a corresponding sequencing cycle in the sequencing run.

6 . The artificial intelligence-based method of claim 5 , wherein the spatial network has a sequence of spatial convolution layers that separately processes each per-cycle analyte channel set in a particular window of per-cycle analyte channel sets in the series for a particular window of sequencing cycles of the sequencing run, and produces a sequence of spatial output sets for each sequencing cycle in the particular window of sequencing cycles, including beginning with a first spatial convolution layer that combines intensities only within a per-cycle analyte channel set of a subject sequencing cycle and not between per-cycle analyte channel sets of different sequencing cycles in the particular window of sequencing cycles, and continuing with successive spatial convolution layers that combine spatial outputs of preceding spatial convolution layers only within a subject sequencing cycle and not between the different sequencing cycles in the particular window of sequencing cycles.

7 . The artificial intelligence-based method of claim 6 , wherein the neural network-based base caller has a temporal network, wherein the temporal network has a sequence of temporal convolution layers that groupwise processes respective compressed spatial output sets for windows of successive sequencing cycles in the particular window of sequencing cycles, and produces a sequence of temporal output sets for the particular window of sequencing cycles, including beginning with a first temporal convolution layer that combines compressed spatial output sets between the different sequencing cycles in the particular window of sequencing cycles, and continuing with successive temporal convolution layers that combine successive temporal outputs of preceding temporal convolution layers.

8 . The artificial intelligence-based method of claim 7 , further including:

for the first window of sequencing cycles,

processing, through a first temporal convolution layer in the sequence of temporal convolution layers of the temporal network, respective compressed spatial output sets for windows of successive sequencing cycles in the first window of sequencing cycles, and generating a plurality of temporal output sets for the first window of sequencing cycles;

processing, through the compression network, the plurality of temporal output sets, and generating respective compressed temporal output sets for respective temporal output sets in the plurality of temporal output sets, wherein the respective temporal output sets have M channels, wherein the respective compressed temporal output sets have N channels, and wherein M>N;

processing, through a final temporal convolution layer in the sequence of temporal convolution layers of the temporal network, the respective compressed temporal output sets, and generating a final temporal output set for the first window of sequencing cycles; and

generating, based on the final temporal output set, the base call predictions for one or more sequencing cycles in the first window of sequencing cycles,

wherein an output layer processes the final temporal output set and produces a final output for the first window of sequencing cycles, wherein the base call predictions are generated based on the final output.

9 . The artificial intelligence-based method of claim 8 , further including:

for a second window of sequencing cycles that shares, with the first window of sequencing cycles, one or more overlapping windows of successive sequencing cycles for which the first temporal convolution layer previously generated temporal output sets, and at least one non-overlapping window of successive sequencing cycles for which the first temporal convolution layer is yet to generate a temporal output set,

processing, through the first temporal convolution layer, respective compressed spatial output sets only for respective sequencing cycles in the at least one non-overlapping window of successive sequencing cycles, and generating a temporal output set for the at least one non-overlapping window of successive sequencing cycles;

processing, through the compression network, the temporal output set, and generating a compressed temporal output set for the at least one non-overlapping window of successive sequencing cycles, wherein the temporal output set has M channels, wherein the compressed temporal output set has N channels, and wherein M>N;

processing, through the final temporal convolution layer, respective compressed temporal output sets for the overlapping windows of successive sequencing cycles previously generated for the first window of sequencing cycles and on the compressed temporal output set, and generating a final temporal output set for the second window of sequencing cycles; and

generating, based on the final temporal output set for the second window of sequencing cycles, base call predictions for one or more sequencing cycles in the second window of sequencing cycles,

wherein an output layer processes the final temporal output set for the second window of sequencing cycles and produces a final output for the second window of sequencing cycles, wherein the base call predictions are generated based on the final output for the second window of sequencing cycles.

10 . The artificial intelligence-based method of claim 9 , further including:

for a third window of sequencing cycles that shares, with the first and second windows of sequencing cycles, one or more overlapping windows of successive sequencing cycles for which the first temporal convolution layer previously generated temporal output sets, and at least one non-overlapping window of successive sequencing cycles for the third window,

processing, through the first temporal convolution layer, respective compressed spatial output sets only for respective sequencing cycles in the at least one non-overlapping window of successive sequencing cycles for the third window, and generating a temporal output set for the at least one non-overlapping window of successive sequencing cycles for the third window;

processing, through the compression network, the temporal output set for the at least one non-overlapping window of successive sequencing cycles for the third window, and generating a compressed temporal output set for the at least one non-overlapping window of successive sequencing cycles for the third window, wherein the temporal output set has M channels, wherein the compressed temporal output set has N channels, and wherein M>N; and

processing, through the final temporal convolution layer, respective compressed temporal output sets for the overlapping windows of successive sequencing cycles previously generated for the first and second windows of sequencing cycles and on the compressed temporal output set for the at least one non-overlapping window of successive sequencing cycles for the third window, and generating a final temporal output set for the third window of sequencing cycles; and

generating, based on the final temporal output set for the third window of sequencing cycles, base call predictions for one or more sequencing cycles in the third window of sequencing cycles,

wherein an output layer processes the final temporal output set for the third window of sequencing cycles and produces a final output for the third window of sequencing cycles, wherein the base call predictions are generated based on the final output for the third window of sequencing cycles.

11 . The artificial intelligence-based method of claim 1 , wherein the spatial network has a sequence of spatial convolution layers having convolution filters that use two-dimensional (2D) convolutions.

12 . The artificial intelligence-based method of claim 1 , wherein the neural network-based base caller has a temporal network comprising convolution filters that use one-dimensional (1D) convolutions.

13 . The artificial intelligence-based method of claim 1 , wherein the compression network uses 1×1 convolutions to control a number of compressed spatial outputs in a compressed spatial output set, wherein the compression network has N convolution filters, and wherein N is an integer equal to or less than four.

14 . The artificial intelligence-based method of claim 1 , further including using data identifying unreliable analytes to remove portions of compressed spatial outputs in a compressed spatial output set corresponding to the unreliable analytes, and generating a compressed, filtered spatial output set to substitute the compressed spatial output set and generate base call predictions only for those analytes that are not the unreliable analytes.

15 . The artificial intelligence-based method of claim 14 , further including processing through a temporal network compressed, filtered spatial output sets instead of corresponding compressed spatial output sets.

16 . The artificial intelligence-based method of claim 15 , wherein the compressed spatial output sets have four to nine times as many total pixels as the corresponding compressed, filtered spatial output sets.

17 . The artificial intelligence-based method of claim 14 , wherein the data identifying the unreliable analytes identifies pixels that depict intensities of unreliable clusters.

18 . The artificial intelligence-based method of claim 1 , further comprising:

generating, based on the respective compressed spatial output sets, base call predictions for one or more sequencing cycles in the first window of sequencing cycles.

19 . A system, comprising:

at least one processor; and

a non-transitory computer readable medium comprising instructions that, when executed by the at least one processor, cause the system to:

execute a first iteration of a base caller to process an input and generate intermediate representations of the input for a sliding window of sequencing cycles;

process, utilizing compression logic, the intermediate representations by applying a number of convolution filters based on a number of channels in the input and generate compressed intermediate representations of the input;

store, in memory, the compressed intermediate representations of the input; and

use the compressed intermediate representations in lieu of the input in a subsequent iteration of the base caller.

20 . The system of claim 19 , wherein channels from the number of channels correspond to at least a filter wavelength, an imaging event, or illumination of a specific laser through a specific optical filter utilized during a sequencing run.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jun 10, 2021
From: VESSERE, GERY; DUTTA, ANINDITA; KASHEFHAGHIGHI, DORNA; JAGANATHAN, KISHORE; KIA, AMIRALI; PARNABY, GAVIN DEREK
To: ILLUMINA, INC.
Reel/Frame 056503/0827 →
Continuity (3)
Provisional Application 62979399 · Feb 20, 2020
Provisional Application 62979411 · Feb 20, 2020
Related Publication 20210265016A1 · Aug 26, 2021
References Cited (400)
US 5641658A · Adams et al. · 1997 [cited by applicant]
US 6090592A · Adams et al. · 2000 [cited by applicant]
US 7057026B2 · Barnes et al. · 2006 [cited by applicant]
US 7115400B1 · Adessi et al. · 2006 [cited by applicant]
US 7211414B2 · Hardin et al. · 2007 [cited by applicant]
US 7315019B2 · Turner et al. · 2008 [cited by applicant]
US 7329492B2 · Hardin et al. · 2008 [cited by applicant]
US 7405281B2 · Xu et al. · 2008 [cited by applicant]
US 7427673B2 · Balasubramanian et al. · 2008 [cited by applicant]
US 7541444B2 · Milton et al. · 2009 [cited by applicant]
US 7566537B2 · Balasubramanian et al. · 2009 [cited by applicant]
US 7592435B2 · Milton et al. · 2009 [cited by applicant]
US 8182993B2 · Tomaney et al. · 2012 [cited by applicant]
US 8241573B2 · Banerjee et al. · 2012 [cited by applicant]
US 8392126B2 · Mann · 2013 [cited by applicant]
US 8401258B2 · Hargrove et al. · 2013 [cited by applicant]
US 8407012B2 · Erlich et al. · 2013 [cited by applicant]
US 8594439B2 · Staelin et al. · 2013 [cited by applicant]
US 8725425B2 · Heiner et al. · 2014 [cited by applicant]
US 8795971B2 · Kersey et al. · 2014 [cited by applicant]
US 8965076B2 · Garcia et al. · 2015 [cited by applicant]
US 9279154B2 · Previte et al. · 2016 [cited by applicant]
US 9453258B2 · Kain et al. · 2016 [cited by applicant]
US 9708656B2 · Turner et al. · 2017 [cited by applicant]
US 10023911B2 · Tomaney et al. · 2018 [cited by applicant]
US 10068053B2 · Kermani · 2018 [cited by examiner]
US 10068054B2 · Van Rooyen et al. · 2018 [cited by applicant]
US 10152776B2 · Langlois et al. · 2018 [cited by applicant]
US 10168438B2 · Dennis et al. · 2019 [cited by applicant]
US 10241075B2 · Davey et al. · 2019 [cited by applicant]
US 10354747B1 · DePristo et al. · 2019 [cited by applicant]
US 10423861B2 · Gao et al. · 2019 [cited by applicant]
US 10527549B2 · Rebetez et al. · 2020 [cited by applicant]
US 10540591B2 · Gao et al. · 2020 [cited by applicant]
US 10619195B2 · Lamb et al. · 2020 [cited by applicant]
US 10648027B2 · Mannion et al. · 2020 [cited by applicant]
US 10711299B2 · Rothberg et al. · 2020 [cited by applicant]
US 10713794B1 · He et al. · 2020 [cited by applicant]
US 10740880B2 · Paik et al. · 2020 [cited by applicant]
US 10740883B2 · Zerfass et al. · 2020 [cited by applicant]
US 10755810B2 · Buckler et al. · 2020 [cited by applicant]
US 10963673B2 · Schaumberg et al. · 2021 [cited by applicant]
US 11138496B2 · Seth · 2021 [cited by applicant]
US 20020055100A1 · Kawashima et al. · 2002 [cited by applicant]
US 20030062485A1 · Fernandez et al. · 2003 [cited by applicant]
US 20040002090A1 · Mayer et al. · 2004 [cited by applicant]
US 20040096853A1 · Mayer · 2004 [cited by applicant]
US 20060014151A1 · Ogura et al. · 2006 [cited by applicant]
US 20060040297A1 · Leamon et al. · 2006 [cited by applicant]
US 20060064248A1 · Saidi et al. · 2006 [cited by applicant]
US 20060188901A1 · Barnes et al. · 2006 [cited by applicant]
US 20060240439A1 · Smith et al. · 2006 [cited by applicant]
US 20060269130A1 · Maroy et al. · 2006 [cited by applicant]
US 20070128624A1 · Gormley et al. · 2007 [cited by applicant]
US 20070166705A1 · Milton et al. · 2007 [cited by applicant]
US 20080009420A1 · Schroth et al. · 2008 [cited by applicant]
US 20080234136A1 · Drmanac et al. · 2008 [cited by applicant]
US 20080242560A1 · Gunderson et al. · 2008 [cited by applicant]
US 20090081775A1 · Hodneland et al. · 2009 [cited by applicant]
US 20100046830A1 · Wang et al. · 2010 [cited by applicant]
US 20100111370A1 · Black et al. · 2010 [cited by applicant]
US 20100157086A1 · Segale et al. · 2010 [cited by applicant]
US 20110059865A1 · Smith et al. · 2011 [cited by applicant]
US 20110065607A1 · Kersey et al. · 2011 [cited by applicant]
US 20110281736A1 · Drmanac et al. · 2011 [cited by applicant]
US 20110286628A1 · Goncalves et al. · 2011 [cited by applicant]
US 20110295902A1 · Mande et al. · 2011 [cited by applicant]
US 20120015825A1 · Zhong et al. · 2012 [cited by applicant]
US 20120020537A1 · Garcia et al. · 2012 [cited by applicant]
US 20130059740A1 · Drmanac et al. · 2013 [cited by applicant]
US 20130079232A1 · Kain et al. · 2013 [cited by applicant]
US 20130124100A1 · Drmanac et al. · 2013 [cited by applicant]
US 20130188866A1 · Obrador et al. · 2013 [cited by applicant]
US 20130250407A1 · Schaffer et al. · 2013 [cited by applicant]
US 20140051588A9 · Drmanac et al. · 2014 [cited by applicant]
US 20140152801A1 · Fine et al. · 2014 [cited by applicant]
US 20150079596A1 · Eltoukhy et al. · 2015 [cited by applicant]
US 20150117784A1 · Lin et al. · 2015 [cited by applicant]
US 20150169824A1 · Kermani et al. · 2015 [cited by applicant]
US 20160042511A1 · Chukka et al. · 2016 [cited by applicant]
US 20160078272A1 · Hammoud · 2016 [cited by applicant]
US 20160110498A1 · Bruand et al. · 2016 [cited by applicant]
US 20160196479A1 · Chertok et al. · 2016 [cited by applicant]
US 20160350914A1 · Champlin et al. · 2016 [cited by applicant]
US 20160356715A1 · Zhong et al. · 2016 [cited by applicant]
US 20160357903A1 · Shendure et al. · 2016 [cited by applicant]
US 20160371431A1 · Haque et al. · 2016 [cited by applicant]
US 20170044601A1 · Crnogorac et al. · 2017 [cited by applicant]
US 20170098032A1 · Desai et al. · 2017 [cited by applicant]
US 20170116520A1 · Min et al. · 2017 [cited by applicant]
US 20170161545A1 · Champlin et al. · 2017 [cited by applicant]
US 20170169313A1 · Choi et al. · 2017 [cited by applicant]
US 20170249421A1 · Eberle et al. · 2017 [cited by applicant]
US 20170249744A1 · Wang et al. · 2017 [cited by applicant]
US 20170362634A1 · Ota et al. · 2017 [cited by applicant]
US 20180075279A1 · Gertych et al. · 2018 [cited by applicant]
US 20180107927A1 · Frey · 2018 [cited by applicant]
US 20180114337A1 · Li et al. · 2018 [cited by applicant]
US 20180189613A1 · Wolf et al. · 2018 [cited by applicant]
US 20180195953A1 · Langlois et al. · 2018 [cited by applicant]
US 20180201992A1 · Wu et al. · 2018 [cited by applicant]
US 20180211001A1 · Gopalan et al. · 2018 [cited by applicant]
US 20180274023A1 · Belitz et al. · 2018 [cited by applicant]
US 20180305751A1 · Vermaas et al. · 2018 [cited by applicant]
US 20180322327A1 · Smith et al. · 2018 [cited by applicant]
US 20180330824A1 · Athey · 2018 [cited by applicant]
US 20180334711A1 · Kelley et al. · 2018 [cited by applicant]
US 20180334712A1 · Singer et al. · 2018 [cited by applicant]
US 20180340234A1 · Scafe et al. · 2018 [cited by applicant]
US 20190034586A1 · Pirrotte et al. · 2019 [cited by applicant]
US 20190080450A1 · Arar et al. · 2019 [cited by applicant]
US 20190107642A1 · Farhadi Nia et al. · 2019 [cited by applicant]
US 20190114544A1 · Sundaram et al. · 2019 [cited by applicant]
US 20190156915A1 · Zhang et al. · 2019 [cited by applicant]
US 20190164010A1 · Ma et al. · 2019 [cited by applicant]
US 20190170680A1 · Sikora et al. · 2019 [cited by applicant]
US 20190180153A1 · Buckler et al. · 2019 [cited by applicant]
US 20190213473A1 · Dutta et al. · 2019 [cited by applicant]
US 20190236454A1 · Fok et al. · 2019 [cited by applicant]
US 20190237160A1 · Rothberg et al. · 2019 [cited by applicant]
US 20190237163A1 · Wang et al. · 2019 [cited by applicant]
US 20190244348A1 · Buckler et al. · 2019 [cited by applicant]
US 20190266491A1 · Gao et al. · 2019 [cited by applicant]
US 20190272638A1 · Mouton et al. · 2019 [cited by applicant]
US 20190332118A1 · Wang et al. · 2019 [cited by applicant]
US 20190377930A1 · Chen et al. · 2019 [cited by applicant]
US 20190392578A1 · Chukka et al. · 2019 [cited by applicant]
US 20200027002A1 · Hickson et al. · 2020 [cited by applicant]
US 20200054306A1 · Mehanian et al. · 2020 [cited by applicant]
US 20200057838A1 · Yekhanin et al. · 2020 [cited by applicant]
US 20200065675A1 · Sundaram et al. · 2020 [cited by applicant]
US 20200176082A1 · Massingham · 2020 [cited by applicant]
US 20200193597A1 · Fan et al. · 2020 [cited by applicant]
US 20200226368A1 · Bakalo et al. · 2020 [cited by applicant]
US 20200256856A1 · Chou et al. · 2020 [cited by applicant]
US 20200302223A1 · Dutta et al. · 2020 [cited by applicant]
US 20200302224A1 · Jaganathan et al. · 2020 [cited by applicant]
US 20200302297A1 · Jaganathan et al. · 2020 [cited by applicant]
US 20200302603A1 · Barnes et al. · 2020 [cited by applicant]
US 20200320294A1 · Mangal et al. · 2020 [cited by applicant]
US 20200342955A1 · Guo et al. · 2020 [cited by applicant]
US 20200364565A1 · Kostem · 2020 [cited by applicant]
US 20200388029A1 · Saltz et al. · 2020 [cited by applicant]
US 20210027462A1 · Bredno et al. · 2021 [cited by applicant]
US 20210056287A1 · Schaumburg et al. · 2021 [cited by applicant]
US 20210072391A1 · Li et al. · 2021 [cited by applicant]
US 20210089827A1 · Kumagai et al. · 2021 [cited by applicant]
US 20210115490A1 · Embree et al. · 2021 [cited by applicant]
US 20210390278A1 · Van Leeuwen et al. · 2021 [cited by applicant]
CA 2894317A1 · 2016 [cited by applicant]
CA 3104851A1 · 2020 [cited by applicant]
CN 110245685A · 2019 [cited by applicant]
EP 3130681A1 · 2017 [cited by applicant]
EP 3373238A1 · 2018 [cited by applicant]
JP 2007199397A · 2007 [cited by applicant]
JP 6712344B2 · 2020 [cited by applicant]
RU 2428734C2 · 2011 [cited by applicant]
RU 2688485C2 · 2019 [cited by applicant]
WO 9106678A1 · 1991 [cited by applicant]
WO 2004018497A2 · 2004 [cited by applicant]
WO 2005065814A1 · 2005 [cited by applicant]
WO 2006064199A1 · 2006 [cited by applicant]
WO 2007010251A2 · 2007 [cited by applicant]
WO 2007123744A2 · 2007 [cited by applicant]
WO 2008154317A1 · 2008 [cited by applicant]
WO 2012058096A1 · 2012 [cited by applicant]
WO 2014077276A1 · 2014 [cited by applicant]
WO 2014142921A1 · 2014 [cited by applicant]
WO 2015084985A2 · 2015 [cited by applicant]
WO 2016145516A1 · 2016 [cited by applicant]
WO 2016201564A1 · 2016 [cited by applicant]
WO 2017184997A1 · 2017 [cited by applicant]
WO 2018129314A1 · 2018 [cited by applicant]
WO 2018165099A1 · 2018 [cited by applicant]
WO 2018203084A1 · 2018 [cited by applicant]
WO 2019027767A1 · 2019 [cited by applicant]
WO 2019028047A1 · 2019 [cited by applicant]
WO 2019055856A1 · 2019 [cited by applicant]
WO 2019079182A1 · 2019 [cited by applicant]
WO 2019079202A1 · 2019 [cited by applicant]
WO 2019090251A2 · 2019 [cited by applicant]
WO 2019136284A1 · 2019 [cited by applicant]
WO 2019136388A1 · 2019 [cited by applicant]
WO 2019140402A1 · 2019 [cited by applicant]
WO 2019147904A1 · 2019 [cited by applicant]
WO 2020014280A1 · 2020 [cited by applicant]
WO 2020123552A1 · 2020 [cited by applicant]
Shujian Liu. (Dec. 9, 2018). Temporal Convolutional Network. Kaggle.com; Kaggle. https://www.kaggle.com/code/christofhenkel/temporal-convolutional-network (Year: 2018). [cited by examiner]
Jason Brownlee. (Jul. 5, 2019). A Gentle Introduction to 1x1 Convolutions to Manage Model Complexity. Machine Learning Mastery. https://machinelearningmastery.com/introduction-to-1x1-convolutions-to-reduce-the-complexit… [cited by examiner]
LaPierre, N., Egan, R., Wang, W., & Wang, Z. (2019). De novo Nanopore read quality improvement using deep learning. BMC bioinformatics, 20, 1-9. (Year: 2019). [cited by examiner]
NL 2023311 NL Search Report, dated Mar. 24, 2020, 15 pages. [cited by applicant]
NL 2023312, NL Search Report, dated Mar. 24, 2020, 22 pages. [cited by applicant]
NL 2023317, NL Search Report, dated Mar. 24, 2020, 16 pages. [cited by applicant]
NL 2023316, NL Search Report, dated Mar. 23, 2020, 15 pages. [cited by applicant]
U.S. Appl. No. 16/825,991—Notice of Allowance dated Aug. 5, 2021, 10 pages. [cited by applicant]
Krishnakumar et. al., Systematic and stochastic influences on the performance of the MinION nanopore sequencer across a range of nucleotide bias, Scientific Reports, published Feb. 16, 2018, 13 pages. [cited by applicant]
Tegfalk, Application of Machine Learning techniques to perform base-calling in next-generation DNA sequencing, KTH Royal Institue of Technology, dated 2020, 53 pages. [cited by applicant]
U.S. Appl. No. 16/826,168—Office Action dated Aug. 31, 2021, 55 pages. [cited by applicant]
Kircher-etal_Improved-base-calling-for-the-Illumina-Genome-Analyzer-using-machine-learning-strategies_Aug. 14, 2009_10pages. [cited by applicant]
Albrecht et al., Deep learning for single molecule science, Nanotechnology, dated Sep. 18, 2017, 11 pages. [cited by applicant]
U.S. Appl. No. 16/825,987—Office Action (Quayle) dated Oct. 19, 2021, 85 pages. [cited by applicant]
PCT/US2021047763—International Search Report and Written Opinion, dated Dec. 20, 2021, 11 pages. [cited by applicant]
PCT/US2021/018422 Second Written Opinion, dated Feb. 4, 2022, 8 pages. [cited by applicant]
Adriana Romero et. al., FitNets: Hints for Thin Deep Nets, published Mar. 27, 2015, 13 pages. [cited by applicant]
U.S. Appl. No. 16/874,599—Notice of Allowance dated Dec. 3, 2021, 12 pages. [cited by applicant]
U.S. Appl. No. 16/825,987—Response to Office Action (Quayle) dated Oct. 19, 2021, filed Jan. 13, 2022, 11 pages . [cited by applicant]
U.S. Appl. No. 16/825,987—Notice of Allowance, dated Jan. 28, 2022, 12 pages. [cited by applicant]
U.S. Appl. No. 16/825,987—Supplemental Notice of Allowance, dated Feb. 7, 2022, 8 pages. [cited by applicant]
U.S. Appl. No. 16/826,168—Response to Office Action dated Aug. 31, 2021, filed Jan. 31, 2022, 15 pages. [cited by applicant]
CN 2020800036223—Voluntary Amendments, filed May 20, 2021, 26 pages. [cited by applicant]
EP 20719053.9—Rules 161(2) and 162 Communication, dated Oct. 28, 2021, 3 pages. [cited by applicant]
IL 279522—Response to Notice Before Acceptance dated Aug. 1, 2021, filed Nov. 28, 2021, 3 pages. [cited by applicant]
KR 10-2020-7037712—Voluntary Amendments with translation, dated Nov. 9, 2021, 7 pages. [cited by applicant]
EP 20719052.1—Rules 161(1) and 162 Communication, dated Oct. 28, 2021. 3 pages. [cited by applicant]
IL 279525—Response to Notice Before Acceptance dated Aug. 1, 2021, filed Nov. 28, 2021, 4 pages. [cited by applicant]
KR 10-2020-7037713—Voluntary Amendments with translation, dated Nov. 9, 2021, 26 pages. [cited by applicant]
ZA 2020/07998—Notice of Allowance, dated Aug. 12, 2021, 2 pages. [cited by applicant]
EP 20718112.4—Rules 161(2) and 162 Communication, dated Oct. 28, 2021, 3 pages. [cited by applicant]
IL 279527—Response to Notice Before Examination dated Aug. 1, 2021, filed Nov. 28, 2021, 3 pages. [cited by applicant]
KR 10-2021-7003269—Voluntary Amendments with translation, dated Nov. 9, 2021, 7 pages. [cited by applicant]
ZA 2020/07999—Notice of Allowance, dated Aug. 12, 2021, 2 pages. [cited by applicant]
EP 20719294.9—Rules 161(1) and 162 Communication, dated Oct. 28, 2021, 3 pages. [cited by applicant]
IL 281668—Response to Notice Before Examination dated Oct. 10, 2021, filed Feb. 8, 2022, 4 pages. [cited by applicant]
KR 10-2021-7009877—Voluntary Amendments with translation, dated Nov. 9, 2021, 21 pages. [cited by applicant]
EP 20757979.8—Rules 161(2) and 162 Communication, dated Oct. 28, 2021, 3 pages. [cited by applicant]
IL 279533—Response to Notice Before Examination dated Aug. 1, 2021, filed Nov. 29, 2021, 3 pages. [cited by applicant]
KR 10-2021-7003270—Voluntary Amendments with translation, dated Nov. 9, 2021, 29 pages. [cited by applicant]
ZA 2020/08000—Notice of Acceptance, dated Aug. 12, 2021, 2 pages. [cited by applicant]
Robinson et al., Computational Exome and Genome Analysis—Chapter 3 Illumina Technology, dated 2018, 25 pages. [cited by applicant]
Wang et. al., An adaptive decorrelation method removes Illumina DNA base-calling errors caused by crosstalk between adjacent clusters—with Supplemental Materials, Scientific Reports, published Feb. 20, 2017, 17 pages. [cited by applicant]
PCT/US2020/033280—International Preliminary Report on Patentability, dated Jul. 23, 2021, 11 pages. [cited by applicant]
Pfeiffer et. al., Systematic evaluation of error rates and causes in short samples in next-generation sequencing, Scientific Reports, published Jul. 19, 2018, 14 pages. [cited by applicant]
PCT/US2020/033281—International Preliminary Report on Patentability, dated Aug. 31, 2021, 10 pages. [cited by applicant]
Bell, C. J. et al. Comprehensive carrier testing for severe childhood recessive diseases by next generation sequencing. Sci. Transl. Med. 3, Jan. 12, 2011, 28 pages. [cited by applicant]
Smedley, D. et al. A whole-genome analysis framework for effective identification of pathogenic regulatory variants in mendelian disease. Am. J. Hum. Genet. 99, 595-606 (2016). [cited by applicant]
Jagadeesh, K. A. et al. M-CAP eliminates a majority of variants of uncertain significance in clinical exomes at high sensitivity. Nat. Genet. 48, 1581-1586 (2016). [cited by applicant]
Grimm, D. G. The evaluation of tools used to predict the impact of missense variants is hindered by two types of circularity. Human. Mutat. 36, 513-523 (2015). [cited by applicant]
Hefferman, R. et al. Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning. Sci. Rep. 5, 11476 (2015) 11 pages. [cited by applicant]
Wang, S., Peng, J., Ma, J. & Xu, J. Protein secondary structure prediction using deep convolutional neural fields. Sci. Rep. 6, 18962-18962 (2016). [cited by applicant]
Harpak, A., Bhaskar, A., & Pritchard, J. K. Mutation rate variation is a primary determinant of the distribution of allele frequencies in humans. PLoS Genet. Dec. 15, 2016, 22pgs. [cited by applicant]
Payandeh, J., Scheuer, T., Zheng, N. & Catterall, W. A. The crystal structure of a voltage-gated sodium channel. Nature 475, 353-358 (2011). [cited by applicant]
Shen, H. et al. Structure of a eukaryotic voltage-gated sodium channel at near-atomic resolution. Science 355, eaal4326 (2017), 19 pages. [cited by applicant]
Nakamura, K. et al. Clinical spectrum of SCN2A mutations expanding to Ohtahara syndrome. Neurology 81, 992-998 (2013). [cited by applicant]
Ioannidis, Nilah M., et al., “REVEL—An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants”, Oct. 5, 2016, 9 pages. [cited by applicant]
Quang Daniel, et. al., “DANN—a deep learning approach for annotating the pathogenicity of genetic variants”, Oct. 22, 2014, 3 pages. [cited by applicant]
Sundaram, et. al., “Predicting the clinical impact of human mutation with deep neural networks”, Aug. 2018, 15pgs. [cited by applicant]
Xiong, et. al., “The human splicing code reveals new insights into the genetic determinants of disease”, Jan. 9, 2015, 20pgs. [cited by applicant]
Yue, et. al., “Deep Learning for Genomics—A Concise Overview from internet”, May 8, 2018, 40pgs. [cited by applicant]
Yuen, et. al., “Genome wide characteristics of de novo mutations in autism”, Jun. 1, 2016, 10pgs. [cited by applicant]
Libbrecht, et. al., “Machine learning in genetics and genomics”, Jan. 2, 2017, 30pgs. [cited by applicant]
Min, et. al., “Deep Learning in Bioinformatics”, Jul. 25, 2016, 19 pgs. [cited by applicant]
Torng, Wen, et al., “3D deep convolutional neural networks for amino acid environment similarity analysis”, 2017, 23pages. [cited by applicant]
Chen, Kathleen M., et. al., “Selene—a PyTorch based deep learning library for sequence level data”, Oct. 10, 2018, 15pages. [cited by applicant]
Grob, C., et. al., “Predicting variant deleteriousness in non human species Applying the CADD approach in mouse”, 2018, 11 pages. [cited by applicant]
Li, et. al., “FoldingZero—Protein Folding from Scratch in Hydrophobic Polar Model”, Dec. 3, 2018, 10 pages. [cited by applicant]
Rentzsch, et. al.,_“CADD—predicting the deleteriousness of variants throughout the human genome”, Oct. 11, 2018, 9 pages. [cited by applicant]
Zou, etal, “A primer on deep learning in genomics”, Nov. 26, 2018, 7pages. [cited by applicant]
Alberts, Bruce, et al., “Molecular biology of the cell”, Sixth Edition, 2015, 3 pages. [cited by applicant]
PCT/US2018/055840—International Search Report and Written Opinion dated Jan. 25, 2019, 18 pages. [cited by applicant]
Wei etal_The Role of Balanced Training and Testing Data Sets for Binary Classifiers in Bioinformatics dated Jul. 9, 2013 12 pages. [cited by applicant]
PCT/US2018/055878—International Search Report and Written Opinion dated Jan. 22, 2019, 20 pages. [cited by applicant]
PCT/US2018/055881—International Search Report and Written Opinion dated Jan. 25, 2019, 17 pages. [cited by applicant]
Duggirala, Ravindranath, et.al., “Genome Mapping and Genomics in Human and Non Human Primate”, 2015, 306pgs. [cited by applicant]
Brookes, Anthony J., “The essence of SNPs”, 1999, pp. 177-186. [cited by applicant]
UniProtKB P04217 A1BG Human [retrieved on Mar. 13, 2019 from (www.uniprot.org/uniprot/P04217), 12pages. [cited by applicant]
Bahar, Protein Actions Principles and Modeling, Chapter 7, 2017 pp. 165-166. [cited by applicant]
Dunbrack, Roland L., Re Question about your Paper titled “The Role of Balanced Training and Testing Data Sets for Binary Classifiers in Bioinformatics”, Message to Sikander Mohammed Khan, Feb. 3, 2019, E-mailm, 3pgs. [cited by applicant]
DbSNP rs2241788 [Retrieved on Mar. 13, 2019], Retrieved from the Internet<www.ncbi.nlm.nih.gov/snp/rs2241788>, 5 pages. [cited by applicant]
Wei, et. al., “Prediction of phenotypes of missense mutations in human proteins from biological assemblies”, Feb. 2013, 28 pages. [cited by applicant]
Zhang, Jun, and Bin Liu. “PSFM-DBT—identifying DNA-binding proteins by combing position specific frequency matrix and distance-bigram transformation.” International journal of molecular sciences 18.9 (2017) 1856. [cited by applicant]
Gao, Tingting, et al. “Identifying translation initiation sites in prokaryotes using support vector machine.” Journal of theoretical biology 262.4 (2010) 644-649. (Year 2010). [cited by applicant]
Bi, Yingtao, et al. “Tree-based position weight matrix approach to model transcription factor binding site profiles.” PloS one6.9 (2011) e24210. [cited by applicant]
Korhonen, Janne H., et al. “Fast motif matching revisited—high-order PWMs, SNPs and indels.” Bioinformatics 33.4 (2016) 514-521. [cited by applicant]
Wong, Sebastien C., et al. “Understanding data augmentation for classification—when to warp?. ” 2016 international conference on digital image computing—techniques and applications (DICTA). IEEE, 2016. [cited by applicant]
Chang, Chia-Yun, et al. “Oversampling to overcome overfitting—exploring the relationship between data set composition, molecular descriptors, and predictive modeling methods.” Journal of chemical information and modelin… [cited by applicant]
Li, Gangmin, and Bei Yao. “Classification of Genetic Mutations for Cancer Treatment with Machine Learning Approaches.” International Journal of Design, Analysis and Tools for Integrated Circuits and Systems 7.1 (2018) p… [cited by applicant]
Martin-Navarro, Antonio, et al. “Machine learning classifier for identification of damaging missense mutations exclusive to human mitochondrial DNA-encoded polypeptides.” BMC bioinformatics 18.1 (2017) p. 158. [cited by applicant]
Krizhevsky, Alex, et al, ImageNet Classification with Deep Convolutional Neural Networks, 2012, 9 Pages. [cited by applicant]
Geeks for Geeks, “Underfitting and Overfilling in Machine Learning”, [retrieved on Aug. 26, 2019]. Retrieved from the Internet <www.geeksforgeeks.org/underfitting-and-overfitting-in-machine- - learning/>, 2 pages. [cited by applicant]
Despois, Julien, “Memorizing is not learning!—6 tricks to prevent overfitting in machine learning”, Mar. 20, 2018, 17 pages. [cited by applicant]
Bhande, Anup What is underfitting and overfitting in machine learning and how to deal with it, Mar. 11, 2018, 10pages. [cited by applicant]
PCT/US2019031621—International Search Report and Written Opinion dated Aug. 7, 2019, 17 pages. [cited by applicant]
Carter et al., “Cancer-specific high-throughput annotation of somatic mutations—computational prediction of driver missense mutations,” Cancer research 69, No. 16 (2009) pp. 6660-6667. [cited by applicant]
PCT/US2021/018258—Second Written Opinion, dated Jan. 25, 2022, 11 pages. [cited by applicant]
PCT/US2021/018910—International Search Report and Written Opinion, dated Aug. 25, 2021, 24 pages. [cited by applicant]
Puckelwartz et al., Supercomputing for the parallelization of whole genome analysis, Bioinformatics, dated Feb. 12, 2014, pp. 1508-1513, 6 pages. [cited by applicant]
Kelly et al., Churchill: an ultra-fast, deterministic, highly scalable and balanced parallelization strategy for the discovery of human genetic variation in clinical and population-scale genomics, Genome Biology, Bio-Me… [cited by applicant]
PCT/US2021/018910—Article 34 Amendment, filed Dec. 19, 2021, 9 pages. [cited by applicant]
PCT/US2021/018910—Second Written Opinion, dated Feb. 21, 2022, 17 pages. [cited by applicant]
PCT/US2021/018422—Article 34 Amendment, dated Dec. 20, 2021, 7 pages. [cited by applicant]
PCT/US/2021/018427—Second Written Opinion, dated Feb. 4, 2022, 9 pages. [cited by applicant]
PCT/US/2021/018427—Article 34 Amendment, filed Dec. 19, 2021, 7 pages. [cited by applicant]
PCT/US2021/018913—Second Written Opinion, dated Feb. 4, 2022, 8 pages. [cited by applicant]
Ye et al., BlindCall: ultra-fast base-calling of high-throughput sequencing data by blind deconvolution, Bioinformatics, vol. 30, No. 9, dated Jan. 9, 2014, pp. 1214-1219, 6 pages. [cited by applicant]
Wang et al., Achieving Accurate and Fast Base-calling by a Block model of the Illumina Sequencing Data, Science Direct, vol. 48, No. 28, dated Jan. 1, 2015, pp. 1462-1465, 4 pages. [cited by applicant]
PCT/US2021/018913—Article 34 Amendment, filed Dec. 19, 2021, 18 pages. [cited by applicant]
PCT/US2021/018915—Second Written Opinion, dated Feb. 4, 2022, 9 pages. [cited by applicant]
PCT/US2021/018915—Article 34 Amendment, filed Dec. 19, 2021, 7 pages. [cited by applicant]
PCT/US2021/018917—Second Written Opinion, dated Feb. 4, 2022, 7 pages. [cited by applicant]
PCT/US2021/018917—Article 34 Amendment, filed Dec. 19, 2021, 6 pages. [cited by applicant]
U.S. Appl. No. 17/468,411—Office Action, dated Feb. 24, 2022, 36 pages. [cited by applicant]
Gao et al., Deep Learning in Protein Structural Modeling and Design, Patterns—CelPress, dated Dec. 11, 2020, 23 pages. [cited by applicant]
Pejaver et al., Inferring the molecular and phenotypic impact of amino acid variants with MutPred2—with Supplementary Information, Nature Communications, dated 2020, 59 pages. [cited by applicant]
Pakhrin et al., Deep learning based advances in protein structure prediction, International Journal of Molecular sciences, published May 24, 2021, 30 pages. [cited by applicant]
Wang et al. Predicting the impacts of mutations on protein-ligand binding affinity based on molecular dynamics simulations and machine learning methods, Computational and Structural Biotechnology Journal 18, dated Feb. … [cited by applicant]
Iqbal et al., Comprehensive characterization of amino acid positions in protein structures reveals molecular effects of missense variants, and supplemental information, PNAS, vol. 117, No. 45, dated Nov. 10, 2020, 35 pa… [cited by applicant]
Forghani et al., Convolutional Neural Network Based Approach to In Silica Non-Anticipating Prediction of Antigenic Distance for Influenza Virus, Viruses, published Sep. 12, 2020, vol. 12, 20 pages. [cited by applicant]
Jing et al., Learning from protein structure with geometric vector perceptrons, Arxiv: 2009: 01411v2, dated Dec. 31, 2020, 18 pages. [cited by applicant]
Stenson, P. D. et al. The Human Gene Mutation Database—building a comprehensive mutation repository for clinical and molecular genetics, diagnostic testing and personalized genomic medicine. Hum. Genet. 133, 1-9 (2014). [cited by applicant]
Alipanahi, et. al., “Predicting the Sequence Specificities of DNA and RNA Binding Proteins by Deep Learning”, Aug. 2015, 9pgs. [cited by applicant]
Angermueller, et. al., “Accurate Prediction of Single Cell DNA Methylation States Using Deep Learning”, Apr. 11, 2017, 13pgs. [cited by applicant]
Ching, et. al., “Opportunities and Obstacles for Deep Learning in Biology and Medicine”, Jan. 19, 2018, 123pgs. [cited by applicant]
Ching, et. al., “Opportunities and Obstacles for Deep Learning in Biology and Medicine”, May 26, 2017, 47pgs. [cited by applicant]
Gu, et. al., “Recent Advances in Convolutional Neural Networks”, Jan. 5, 2017, 37pgs. [cited by applicant]
Leung, et. al., “Deep learning of the tissue regulated splicing code”, 2014, 9pgs. [cited by applicant]
Leung, et. al., “Inference of the Human Polyadenylation Code”, Apr. 27, 2017, 13pgs. [cited by applicant]
Leung, et. al., “Machine Learning in Genomic Medicine”, Jan. 1, 2016, 22pgs. [cited by applicant]
Park, et. al., “Deep Learning for Regulatory Genomics”, Aug. 2015, 2pgs. [cited by applicant]
MacArthur, D. G. et al. Guidelines for investigating causality of sequence variants in human disease. Nature 508, 469-476 (2014). [cited by applicant]
Rehm, H. L. et al. ClinGen—the Clinical Genome Resource. N. Engl. J. Med. 372, 2235-2242 (2015). [cited by applicant]
Bamshad, M. J. et al. Exome sequencing as a tool for Mendelian disease gene discovery. Nat. Rev. Genet. 12, 745-755 (2011). [cited by applicant]
Rehm, H. L. Evolving health care through personal genomics. Nat. Rev. Genet. 18, 259-267 (2017). [cited by applicant]
Richards, S. et al. Standards and guidelines for the interpretation of sequence variants—a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathol… [cited by applicant]
Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285-291 (2016). [cited by applicant]
Mallick, S. et al. The Simons Genome Diversity Project—300 genomes from 142 diverse populations. Nature 538, 201-206 (2016). [cited by applicant]
Genomes Project Consortium. et al. A global reference for human genetic variation. Nature 526, 68-74 (2015). [cited by applicant]
Liu, X., Jian, X. & Boerwinkle, E. dbNSFP—a lightweight database of human nonsynonymous SNPs and their functional predictions. Human. Mutat. 32, 894-899 (2011). [cited by applicant]
Chimpanzee Sequencing Analysis Consortium. Initial sequence of the chimpanzee genome and comparison with the human genome. Nature 437, 69-87 (2005). [cited by applicant]
Takahata, N. Allelic genealogy and human evolution. Mol. Biol. Evol. 10, 2-22 (1993). [cited by applicant]
Asthana, S., Schmidt, S., & Sunyaev, S. A limited role for balancing selection. Trends Genet. 21, 30-32 (2005). [cited by applicant]
Leffler, E. M. et al. Multiple instances of ancient balancing selection shared between humans and chimpanzees. Science 339, 12 pages (2013). [cited by applicant]
Samocha, K. E. et al. A framework for the interpretation of de novo mutation in human disease. Nat. Genet. 46, 944-950 (2014). [cited by applicant]
Ohta, T. Slightly deleterious mutant substitutions in evolution. Nature 246, 96-98 (1973). [cited by applicant]
Reich, D. E. & Lander, E. S. On the allelic spectrum of human disease. Trends Genet. 17, 502-510 (2001). [cited by applicant]
Whiffin, N. et al. Using high-resolution variant frequencies to empower clinical genome interpretation. Genet. Med. 19, 1151-1158(2017). [cited by applicant]
Prado-Martinez, J. et al. Great ape genome diversity and population history. Nature 499, 471-475 (2013). [cited by applicant]
Klein, J., Satta, Y., O'HUigin, C., & Takahata, N. The molecular descent of the major histocompatibility complex. Annu. Rev. Immunol. 11, 269-295 (1993). [cited by applicant]
De Manuel, M. et al. Chimpanzee genomic diversity reveals ancient admixture with bonobos. Science 354, 477-481 (2016). [cited by applicant]
Locke, D. P. et al. Comparative and demographic analysis of orang-utan genomes. Nature 469, 529-533 (2011). [cited by applicant]
Rhesus Macaque Genome Sequencing Analysis Consortium. Evolutionary and biomedical insights from the rhesus macaque genome. Science 316, 222-234 (2007). [cited by applicant]
Worley, K. C. et al. The common marmoset genome provides insight into primate biology and evolution. Nat. Genet. 46, 850-857 (2014). [cited by applicant]
Sherry, S. T. et al. dbSNP—the NCBI database of genetic variation. Nucleic Acids Res. 29, 308-211 (2001). [cited by applicant]
Schrago, C. G., & Russo, C. A. Timing the origin of New World monkeys. Mol. Biol. Evol. 20, 1620-1625 (2003). [cited by applicant]
Landrum, M. J. et al. ClinVar—public archive of interpretations of clinically relevant variants. Nucleic Acids Res. 44, D862-868 (2016). [cited by applicant]
Brandon, E. P., Idzerda, R. L. & McKnight, G. S. Targeting the mouse genome—a compendium of knockouts (Part II). Curr. Biol. 5, 758-765 (1995). [cited by applicant]
Jeschke, J. G. & Currie, P. D. Animal models of human disease—zebrafish swim into view. Nat. Rev. Genet. 8, 353-367 (2007). [cited by applicant]
Sittig, L. J. et al. Genetic background limits generalizability of genotype—phenotype relationships. Neuron 91, 1253-1259 (2016). [cited by applicant]
Bazykin, G. A. et al. Extensive parallelism in protein evolution. Biol. Direct 2, 20, 13 pages (2007). [cited by applicant]
Ng, P. C., & Henikoff, S. Predicting deleterious amino acid substitutions. Genome Res. 11, 863-874 (2001). [cited by applicant]
Adzhubei, I. A. et al. A method and server for predicting damaging missense mutations. Nat. Methods 7, 248-249 (2010). [cited by applicant]
Chun, S. & Fay, J. C. Identification of deleterious mutations within three human genomes. Genome Res. 19, 1553-1561 (2009). [cited by applicant]
Schwarz, J. M., Rodelsperger, C., Schuelke, M. & Seelow, D. MutationTaster evaluates disease-causing potential of sequence alterations. Nat. Methods 7, 575-576 (2010). [cited by applicant]
Reva, B., Antipin, Y., & Sander, C. Predicting the functional impact of protein mutations—application to cancer genomics. Nucleic Acids Res. 39, e118 (2011), 14pgs. [cited by applicant]
Dong, C. et al. Comparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole exome sequencing studies. Hum. Mol. Genet. 24, 2125-2137 (2015). [cited by applicant]
Carter, H., Douville, C., Stenson, P. D., Cooper, D. N., & Karchin, R. Identifying Mendelian disease genes with the variant effect scoring tool. BMC Genom, (2013), 13 pages. [cited by applicant]
Choi, Y., Sims, G. E., Murphy, S., Miller, J. R., & Chan, A. P. Predicting the functional effect of amino acid substitutions and indels. PLoS One 7, e46688 (2012). [cited by applicant]
Gulko, B., Hubisz, M. J., Gronau, I., & Siepel, A. A method for calculating probabilities of fitness consequences for point mutations across the human genome. Nat. Genet. 47, 276-283 (2015). [cited by applicant]
Shihab, H. A. et al. An integrative approach to predicting the functional effects of non-coding and coding sequence variation. Bioinformatics 31, 1536-1543 (2015). [cited by applicant]
PCT/US2021/018422 International Search Report and Written Opinion, dated Jun. 10, 2021, 12 pages. [cited by applicant]
Aggarwal, Neural Networks and Deep Learning: A Textbook, Springer, dated Aug. 26, 2018, 512 pages. [cited by applicant]
Wang et. al., Deep Neural Network Approximation for Custom Hardware: Where We've Been, Where We're Going, Cornell University, dated Jan. 21, 2019, 37 pages. [cited by applicant]
Lavin et. al., Fast Algorithms for Convolutional Neural Networks, dated Nov. 10, 2015, 9 pages. [cited by applicant]
Liu et. al., A Uniform Architecture Design for Accelerating 2D and 3D CNNs on FPGAs, published Jan. 7, 2019, 19 pages. [cited by applicant]
PCT/US2021/018427 International Search Report and Written Opinion, dated Jun. 1, 2021, 15 pages. [cited by applicant]
PCT/US2021/018913 International Search Report and Written Opinion, dated Jun. 10, 2021, 11 pages. [cited by applicant]
Zeng et. al., Causalcall: Nanopore Basecalling Using a Temporal Convolutional Network, dated Jan. 20, 2020, 11 pages. [cited by applicant]
PCT/US2021/018915 International Search Report and Written Opinion, dated Jun. 15, 2021, 13 pages. [cited by applicant]
Kwon et. al., Understanding Reuse, Performance, and Hardware Cost of DNN Dataflow—A Data-Centric Approach, Proceedings of the 52nd Annual IEEE/ACM International Symposium on Microarchitecture, dated Oct. 12, 2019, 13 pa… [cited by applicant]
Sze et. al., Efficient Processing of Deep Neural Networks: A Tutorial and Survey, Cornell University Library, dated Mar. 27, 2017, 21 pages. [cited by applicant]
Sundaram, L et. al., “Predicitng the clinical impact of human mutation with deep neural networks”, Nat. Genet. 50, 1161-1170 (2018). [cited by applicant]
Jaganathan, K. et. al., “Predicting splicing from primary sequence with deep learning”, Cell 176, 535-548, (2019). [cited by applicant]
Kircher, Martin, et al. “A general framework for estimating the relative pathogenicity of human genetic variants.” Nature genetics 46.3 (2014): 310. (Year:2014). [cited by applicant]
Henikoff, S. & Henikoff, J. G. Amino acid substitution matrices from protein blocks. Proc. Natl. Acad. Sci. USA 89, 10915-10919 (1992). [cited by applicant]
Li, W. H., Wu, C. I. & Luo, C. C. Nonrandomness of point mutation as reflected in nucleotide substitutions in pseudogenes and its evolutionary implications. J. Molec. Evol. 21, 58-71 (1984). [cited by applicant]
Grantham, R. Amino acid difference formula to help explain protein evolution. Science 185, 862-864 (1974). [cited by applicant]
LeCun, Y., Botlou, L., Bengio, Y., & Haffner, P. Gradient based learning applied to document recognition. Proc. IEEE 36, 2278-2324 (1998). [cited by applicant]
Vissers, L. E., Gilissen, C., & Veltman, J. A. Genetic studies in intellectual disability and related disorders. Nat. Rev. Genet. 17, 9-18 (2016). [cited by applicant]
Neale, B. M. et al. Patterns and rates of exonic de novo mutations in autism spectrum disorders. Nature 485, 242-245 (2012). [cited by applicant]
Sanders, S. J. et al. De novo mutations revealed by whole-exome sequencing are strongly associated with autism. Nature 485, 237-241 (2012). [cited by applicant]
De Rubeis, S. et al. Synaptic, transcriptional and chromatin genes disrupted in autism. Nature 515, 209-215 (2014). [cited by applicant]
Deciphering Developmental Disorders Study. Large-scale discovery of novel genetic causes of developmental disorders. Nature 519, 223-228 (2015). [cited by applicant]
Deciphering Developmental Disorders Study. Prevalence and architecture of de novo mutations in developmental disorders. Nature 542, 433-438 (2017). [cited by applicant]
Iossifov, I. et al. The contribution of de novo coding mutations to autism spectrum disorder. Nature 515, 216-221 (2014). [cited by applicant]
Zhu, X. Need, A. C., Petrovski, S. & Goldstein, D. B. One gene, many neuropsychiatric disorders: lessons from Mendelian diseases. Nat. Neurosci. 17, 773-781, (2014). [cited by applicant]
Leffler, E. M. et al. Revisiting an old riddle: what determines genetic diversity levels within species? PLoS Biol. 10, e1001388 (2012), 9pages. [cited by applicant]
Estrada, A. et al. Impending extinction crisis of the world's primates—why primates matter. Sc. Adv. 3, e1600946 (2017), 17 pages. [cited by applicant]
Kent, W. J. et al. The human genome browser at UCSC. Genome Res. 12, 996-1006 (2002). [cited by applicant]
Tyner, C. et al. The UCSC Genome Browser database—2017 update. Nucleic Acids Res. 45, D626-D634 (2017). [cited by applicant]
Kabsch, W., & Sander, C. Dictionary of protein secondary structure—pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22, 2577-2637 (1983). [cited by applicant]
Joosten, R. P. et al. A series of PDB related databases for everyday needs. Nucleic Acids Res. 39, 411-419 (2011). [cited by applicant]
He, K, Zhang, X., Ren, S., & Sun, J. Identity mappings in deep residual networks. in 14th European Conference on Computer Vision—ECCV 2016. ECCV 2016. Lecture Notes in Computer Science, vol. 9908; 630 6, 15 (Springer, C… [cited by applicant]
Ionita-Laza, I., McCallum, K., Xu, B., & Buxbaum, J. D. A spectral approach integrating functional genomic annotations for coding and noncoding variants. Nat. Genet. 48, 214-220 (2016). [cited by applicant]
Li, B. et al. Automated inference of molecular mechanisms of disease from amino acid substitutions. Bioinformatics 25, 2744-2750 (2009). [cited by applicant]
Lu, Q. et al. A statistical framework to predict functional non-coding regions in the human genome through integrated analysis of annotation data. Sci. Rep. 5, 10576 (2015), 13pgs. [cited by applicant]
Shihab, H. A. et al. Predicting the functional, molecular, and phenotypic consequences of amino acid substitutions using hidden Markov models. Human. Mutat. 34, 57-65 (2013). [cited by applicant]
Davydov, E. V. et al. Identifying a high fraction of the human genome to be under selective constraint using GERP++. PLoS Comput. Biol. 6, Dec. 2, 2010, 13 pages. [cited by applicant]
Liu, X., Wu, C., Li, C., & Boerwinkle, E. dbNSFPv3.0 a one-stop database of functional predictions and annotations for human nonsynonymous and splice-site SNVs. Human. Mutat. 37, 235-241 (2016). [cited by applicant]
Jain, S., White, M., Radivojac, P. Recovering true classifier performance in positive-unlabeled learning. in Proceedings Thirty-First AAAI Conference on Artificial Intelligence. 2066-2072 (AAAI Press, San Francisco; 201… [cited by applicant]
De Ligt, J. et al. Diagnostic exome sequencing in persons with severe intellectual disability. N. Engl. J. Med. 367, 1921-1929 (2012). [cited by applicant]
Iossifov, I. et al. De novo gene disruptions in children on the autistic spectrum. Neuron 74, 285-299 (2012). [cited by applicant]