Table 2.

NLP-ML models trained on microarray sample descriptions can accurately infer annotations for samples from five different genomics exp types

	RNA-seq	ChIP-seq	Methylation array	CGHA	Microarray	Total
Adipose tissue	7	8	10	3	10	38
Brain	10	10	10	9	10	49
Colon	6	10	10	5	9	40
Neural tube	10	10	10	7	9	46
Muscle tissue	9	0	10	0	10	29

Each row corresponds to one of five top-performing NLP-ML tissue models. The last column shows the total out of 50 that each of these models annotated correctly. Columns 2–6 show the number of samples (out of 10) from each experiment type that each tissue model annotated correctly.

RNA-seq RNA-seq of coding RNA, Methylation array methylation profiling by array, CGHA comparative genomic hybridization by array, Microarray transcription profiling by array.