Our projects on deep Learning for Biomedicine
This front adapts from our legacy website deepchrome.org (later to deepchrome.net)
and introduces updates of a suite of
deep learning tools we have developed for learning patterns and making predictions on biomedical data (mostly from functional genomics).
Please feel free to email me when you find my typos.
Biology and medicine are rapidly becoming data-intensive. A recent comparison of genomics with social media, online videos, and other data-intensive disciplines suggests that genomics alone will equal or surpass other fields in data generation and analysis within the next decade. The volume and complexity of these data present new opportunities, but also pose new challenges. Data sets are complex, often ill-understood and grow at a a faster scale than computational capabilities. Problems of this nature may be particularly well-suited to deep learning techniques (see Opportunities and obstacles for deep learning in biology and medicine). The website introduces a suite of deep learning tools we have developed for learning patterns and making predictions on biomedical data (mostly from functional genomics).
Our technical focus in this direction center on making DNN interpretable.
Background of Learning: Representation Learning and Deep Learning
The performance of machine learning algorithms is largely dependent on the
data representation (or features) on which they are
applied. Deep learning aims at discovering learning algorithms that can
find multiple levels of representations directly from data, with higher
levels representing more abstract concepts. In recent years,
the field of deep learning has lead to groundbreaking performance in many applications such as computer vision, speech understanding, natural language processing, and computational biology.
Have questions or suggestions? Feel free to ask me on Twitter or email me.
Thanks for reading!
I gave a tutorial talk at UVA-VADC Seminar Series 2021 and at monthly NIH Data Science Showcase seminar.
I gave a tutorial talk at
UVA-CPHG Seminar Series 2018.
Tool DeepDIff: DeepDiff: Deep-learning for predicting Differential gene expression from histone modifications
Here are the slides of tutorial talk I gave at ACM-BCB 2018.
Here are the slides of lecture talks I gave at UCLA CGWI and NLM-CBB seminar about our deep learning tools: DeepChrome, AttentiveChrome and DeepMotif.
Jack’s DeepMotif paper (Deep Motif Dashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks ) have received the “best paper awar...
Tool AttentiveChrome: Attend and Predict: Using Deep Attention Model to Understand Gene Regulation by Selective Attention on Chromatin
Tool Deep Motif Dashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks
Tool DeepChrome: deep-learning for predicting gene expression from histone modifications
Tool GaKCo-SVM: a Fast GApped k-mer string Kernel using COunting
Tool TSK: Transfer String Kernel for Cross-Context DNA-Protein Binding Prediction
Tool MUST-CNN: A Multilayer Shift-and-Stitch Deep Convolutional Architecture for Sequence-based Protein Structure Prediction
Title: Deep Learning for Character-based Information Extraction on Chinese and Protein Sequence
Here are the slides of one lecture talk I gave at UVA CPHG Seminar Series in 2014 about our deep learning tools back then.
Tool Multitask-ProteinTagging: A unified multitask architecture for predicting local protein properties