Best Paper Award for Deep Motif Dashboard

Jack’s DeepMotif paper (Deep Motif Dashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks ) have received the “best paper award“ at NIPS17 workshop for Transparent and interpretable Machine Learning in Safety Critical Environments. Big congratulations!!!

Prototype Matching Networks for Large-Scale Multi-label Genomic Sequence Classification

Prototype Matching Networks : A novel deep learning architecture for Large-Scale Multi-label Genomic Sequence Classification

Paper: @Arxiv

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.

Citations

@article{lanchantin2017prototype,
  title={Prototype Matching Networks for Large-Scale Multi-label Genomic Sequence Classification},
  author={Lanchantin, Jack and Sekhon, Arshdeep and Singh, Ritambhara and Qi, Yanjun},
  journal={arXiv preprint arXiv:1710.11238},
  year={2017}
}

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AttentiveChrome-Deep Attention Model to Understand Gene Regulation by Selective Attention on Chromatin

Tool AttentiveChrome: Attend and Predict: Using Deep Attention Model to Understand Gene Regulation by Selective Attention on Chromatin

Paper: @Arxiv + Published at [NIPS2017]

(https://papers.nips.cc/paper/7255-attend-and-predict-understanding-gene-regulation-by-selective-attention-on-chromatin.pdf)

GitHub

talk slides PDF

poster PDF

Abstract:

The past decade has seen a revolution in genomic technologies that enable a flood of genome-wide profiling of chromatin marks. Recent literature tried to understand gene regulation by predicting gene expression from large-scale chromatin measurements. Two fundamental challenges exist for such learning tasks: (1) genome-wide chromatin signals are spatially structured, high-dimensional and highly modular; and (2) the core aim is to understand what are the relevant factors and how they work together? Previous studies either failed to model complex dependencies among input signals or relied on separate feature analysis to explain the decisions. This paper presents an attention-based deep learning approach; we call AttentiveChrome, that uses a unified architecture to model and to interpret dependencies among chromatin factors for controlling gene regulation. AttentiveChrome uses a hierarchy of multiple Long short-term memory (LSTM) modules to encode the input signals and to model how various chromatin marks cooperate automatically. AttentiveChrome trains two levels of attention jointly with the target prediction, enabling it to attend differentially to relevant marks and to locate important positions per mark. We evaluate the model across 56 different cell types (tasks) in human. Not only is the proposed architecture more accurate, but its attention scores also provide a better interpretation than state-of-the-art feature visualization methods such as saliency map. Code and data are shared at www.deepchrome.org

Citations

@inproceedings{singh2017attend,
  title={Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin},
  author={Singh, Ritambhara and Lanchantin, Jack and Sekhon, Arshdeep  and Qi, Yanjun},
  booktitle={Advances in Neural Information Processing Systems},
  pages={6769--6779},
  year={2017}
}

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Memory Matching Networks for Genomic Sequence Classification

Tool Memory Matching Networks for Genomic Sequence Classification

Paper: @Arxiv

GitHub

Poster

Abstract

When analyzing the genome, researchers have discovered that proteins bind to DNA based on certain patterns of the DNA sequence known as “motifs”. However, it is difficult to manually construct motifs due to their complexity. Recently, externally learned memory models have proven to be effective methods for reasoning over inputs and supporting sets. In this work, we present memory matching networks (MMN) for classifying DNA sequences as protein binding sites. Our model learns a memory bank of encoded motifs, which are dynamic memory modules, and then matches a new test sequence to each of the motifs to classify the sequence as a binding or nonbinding site.

Citations

@article{lanchantin2017memory,
  title={Memory Matching Networks for Genomic Sequence Classification},
  author={Lanchantin, Jack and Singh, Ritambhara and Qi, Yanjun},
  journal={arXiv preprint arXiv:1702.06760},
  year={2017}
}

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Deep Motif Dashboard- Visualizing and Understanding Genomic Sequences Using Deep Neural Networks

Tool Deep Motif Dashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks

Paper: @Arxiv | @PSB17

GitHub

Talk Slides

Abstract:

Deep neural network (DNN) models have recently obtained state-of-the-art prediction accuracy for the transcription factor binding (TFBS) site classification task. However, it remains unclear how these approaches identify meaningful DNA sequence signals and give insights as to why TFs bind to certain locations. In this paper, we propose a toolkit called the Deep Motif Dashboard (DeMo Dashboard) which provides a suite of visualization strategies to extract motifs, or sequence patterns from deep neural network models for TFBS classification. We demonstrate how to visualize and understand three important DNN models: convolutional, recurrent, and convolutional-recurrent networks. Our first visualization method is finding a test sequence’s saliency map which uses first-order derivatives to describe the importance of each nucleotide in making the final prediction. Second, considering recurrent models make predictions in a temporal manner (from one end of a TFBS sequence to the other), we introduce temporal output scores, indicating the prediction score of a model over time for a sequential input. Lastly, a class-specific visualization strategy finds the optimal input sequence for a given TFBS positive class via stochastic gradient optimization. Our experimental results indicate that a convolutional-recurrent architecture performs the best among the three architectures. The visualization techniques indicate that CNN-RNN makes predictions by modeling both motifs as well as dependencies among them.

Citations

@inproceedings{lanchantin2017deep,
  title={Deep motif dashboard: Visualizing and understanding genomic sequences using deep neural networks},
  author={Lanchantin, Jack and Singh, Ritambhara and Wang, Beilun and Qi, Yanjun},
  booktitle={PACIFIC SYMPOSIUM ON BIOCOMPUTING 2017},
  pages={254--265},
  year={2017},
  organization={World Scientific}
}

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