Prototype Matching Networks for Large-Scale Multi-label Genomic Sequence Classification
  One of the fundamental tasks in understanding genomics is the problem of
predicting Transcription Factor Binding Sites (TFBSs). With more than hundreds
of Transcription Factors (TFs) as labels, genomic-sequence based TFBS
prediction is a challenging multi-label classification task. There are two
major biological mechanisms for TF binding: (1) sequence-specific binding
patterns on genomes known as "motifs" and (2) interactions among TFs known as
co-binding effects. In this paper, we propose a novel deep architecture, the
Prototype Matching Network (PMN) to mimic the TF binding mechanisms. Our PMN
model automatically extracts prototypes ("motif"-like features) for each TF
through a novel prototype-matching loss. Borrowing ideas from few-shot matching
models, we use the notion of support set of prototypes and an LSTM to learn how
TFs interact and bind to genomic sequences. On a reference TFBS dataset with
$2.1$ $million$ genomic sequences, PMN significantly outperforms baselines and
validates our design choices empirically. To our knowledge, this is the first
deep learning architecture that introduces prototype learning and considers
TF-TF interactions for large-scale TFBS prediction. Not only is the proposed
architecture accurate, but it also models the underlying biology.
