Title: Prototype Matching Networks for Large-Scale Multi-label Genomic Sequence Classification,
Abstract: 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.