DrQi’s tutorial talk on “Make Deep Learning Interpretable for Sequential Data Analysis in Biomedicine” (Including our work on DeepChrome - AttentiveChrome - GCNChrome - DeepMotif - DeepVHPPI - MotifTransformer)

I gave a tutorial talk at UVA-VADC Seminar Series 2021 and at monthly NIH Data Science Showcase seminar.

Title: Make Deep Learning Interpretable for Sequential Data Analysis in Biomedicine

Slide PDF

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This tutorial includes four of our recent papers:

Tool DeepChrome: deep-learning for predicting gene expression from histone modifications

• Paper: @Bioinformatics

• GitHub

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

• Paper: Published at NeurIPS2017

• GitHub

Tool: GCNChrome: Graph Convolutional Networks for Epigenetic State Prediction Using Both Sequence and 3D Genome Data

• Paper @Bioinformatics

• GitHub: https://github.com/QData/ChromeGCN

Tool: Transfer Learning for Predicting Virus-Host Protein Interactions for Novel Virus Sequences

• PDF @ ACM BCB21

• GitHub https://github.com/QData/DeepVHPPI

Thanks for reading!