METHODS AND COMPOSITIONS THAT UTILIZE TRANSCRIPTOME SEQUENCING DATA IN MACHINE LEARNING-BASED CLASSIFICATION

1 .- 80 . (canceled)

81 . A method for processing a biological sample, comprising:

(a) assaying one or more nucleic acid sequences from said biological sample to obtain a biological dataset comprising gene expression levels, sequence variant information, or a combination thereof corresponding to said one or more nucleic acid sequences;

(b) comparing said biological dataset assayed in (a) to a second dataset comprising gene expression levels, sequence variant information, or a combination thereof corresponding to one or more nucleic acid sequences of a control sample;

(c) assigning a call to said one or more nucleic acid sequences of said biological dataset based on said comparing of (b), wherein said call is a no-call, a reference-call, or a noise-call;

(d) assigning said noise-call to a nucleic acid sequence of said biological dataset; and

(e) upon assigning said noise-call to said nucleic acid sequence, (i) flagging said nucleic acid sequence within said biological dataset, or (ii) removing said nucleic acid sequence from said biological dataset, to produce said modified biological dataset.

82 . The method of claim 81 , wherein said biological dataset comprises said gene expression levels.

83 . The method of claim 81 , wherein said biological dataset comprises said sequence variant information.

84 . The method of claim 81 , wherein said second dataset comprises said gene expression levels.

85 . The method of claim 81 , wherein said second dataset comprises said sequence variant information.

86 . The method of claim 81 , wherein said flagging comprises weighting said nucleic acid sequence differently from nucleic acid sequences of said biological dataset that are not assigned said noise-call.

87 . The method of claim 81 , wherein said assaying comprises assaying a first portion of said biological sample separately from assaying a second portion of said biological sample.

88 . The method of claim 81 , wherein said biological sample is obtained from a first source and a second source, wherein said first source and said second source are different.

89 . The method of claim 81 , wherein said comparing further comprises determining a difference in expression level between said gene expression levels of said one or more nucleic acid sequences from said biological sample compared to said gene expression levels of one or more nucleic acid sequences of said control sample having at least about 90% homology to said one or more nucleic acid sequences from said biological sample.

90 . The method of claim 81 , wherein said comparing further comprises determining a presence or an absence of a fusion in said one or more nucleic acid sequences of said biological sample compared to a presence or an absence of said fusion in one or more nucleic acid sequences of said control sample having at least about 90% homology to said one or more nucleic acid sequences of said biological sample.

91 . The method of claim 81 , wherein said comparing further comprises determining a presence or an absence of a sequence variant, a sequence variant count number, or a combination thereof in said one or more nucleic acid sequences of said biological sample compared to a present or an absence of said sequence variant, said sequence variant count number, or a combination thereof in one or more nucleic acid sequences of said control sample having at least about 90% homology to said one or more nucleic acid sequences of said biological sample.

92 . The method of claim 81 , wherein said biological sample is independent from said control sample.

93 . The method of claim 81 , wherein nucleic sequence assigned said noise-call in (d) comprises a transcript degradation, an impartial fragmentation, an incomplete library preparation, a 3′ to 5′ bias, a polymerase processivity, a polymerase sequence bias, or any combination thereof.

94 . The method of claim 81 , wherein said modified biological dataset comprises one or more nucleic acid sequences assigned a no-call, a reference call, or a combination thereof.

95 . The method of claim 81 , wherein at least about 70% of the one or more nucleic acid sequences of said biological sample have at least about 90% sequence homology to a nucleic acid sequence of said one or more nucleic acid sequences of said control sample.

96 . The method of claim 81 , wherein said nucleic acid sequence assigned said noise-call in (d) comprises at least about 90% sequence homology to BRAF, HRAS, KRAS, NRAS, TSHR, or RET, or any fragment thereof.

97 . The method of claim 81 , wherein said nucleic acid sequence assigned said noise-call in (d) comprises at least about 90% sequence homology to TSHR, RET, NRAS, TP53, PAX8, FAT1, VT11A, BRAF, HRAS, or KRAS, or any fragment thereof.

98 . The method of claim 81 , further comprising, employing said modified biological dataset to train a trained algorithm.

99 . The method of claim 81 , wherein said biological sample is obtained from a subject having or suspected of having a thyroid or lung disease condition.

100 . The method of claim 81 , further comprising, prior to (a), obtaining said biological sample from said subject by fine needle aspiration.

101 . The method of claim 81 , wherein said biological sample is cytologically indeterminate.

102 . The method of claim 81 , wherein said control sample is obtained from a subject suspected of having or having been diagnosed with a disease.

103 . The method of claim 81 , wherein said one or more nucleic acid sequences of said control sample are associated with said noise-call.

104 . The method of claim 81 , further comprising modifying said biological data set by removing said nucleic acid sequence from said biological dataset.