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Reliable post hoc interpretations of deep learning in genomics

基因组学深度学习的可靠事后解释

基本信息

批准号：
10638753
负责人：
Peter K Koo
金额：
$ 38.4万
依托单位：
COLD SPRING HARBOR LABORATORY
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-08-01 至 2027-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10638753
关键词：
ATAC-seq Acceleration Address Affect Benchmarking Binding Biological Biological Assay Biological Sciences Biology Chromatin Communities Complex Computer software Computing Methodologies DNA DNA Sequence Data Development Disease Ensure Future Genetic Transcription Genomics Goals Individual Knowledge Learning Machine Learning Maps Methods Modeling Noise Nucleotides Performance Population Positioning Attribute Recurrence Regulation Regulatory Element Resolution Series Signal Transduction Single Nucleotide Polymorphism Specific qualifier value Structure TensorFlow Time Training Transcriptional Regulation Translating Untranslated RNA base bioinformatics tool biological systems cell type computerized tools deep learning deep neural network direct application functional genomics genomic data geometric structure human disease improved innovation insight machine learning method new technology open source performance tests predictive modeling syntax technology research and development tool transcription factor user-friendly

项目摘要

PROJECT SUMMARY Understanding how the coordination of transcription factors bind to non-coding DNA provides mechanistic insights into transcriptional regulation. Recent developments in deep neural networks (DNNs) have revolutionized our ability to study regulatory genomics. While they have demonstrated improved predictions compared to previous methods based on traditional computational genomics, their low interpretability has earned them a reputation as a black box. To address this gap, post hoc model interpretability methods have emerged to interrogate important features that the network has learned. Of these, attribution maps have demonstrated promise, providing importance scores for each nucleotide in a given sequence; these have a natural interpretation as single-nucleotide variant effects. In principle, attribution maps should contain information to identify motifs that are important for cell-type specific regulatory functions and annotate their positions at base- resolution. However, attribution maps are often noisy in practice; in addition to motifs, they contain spurious importance scores for arbitrary nucleotides for reasons that are not well established. Despite their promise, interpreting a DNN through attribution maps remains challenging. Here we propose three complementary aims that serve to maximize the biological insights that we can achieve from attribution maps for genomic DNNs. In Aim 1, we will develop a model selection framework to identify the optimal DNN from a set of candidate DNNs that yields high generalization performance and interpretable attribution maps. In Aim 2, we will develop robust training strategies based on regularization and data augmentations tailored for genomics, with the broader aim of ensuring that DNNs yield high-quality attribution maps and high generalization. In Aim 3, we will develop and employ interpretable computational methods to directly analyze attribution maps to facilitate discovery of functional motifs and annotate their positions. Each aim will be implemented as open-source software in TensorFlow and PyTorch. As the number of deep learning applications in genomics is rising quickly, the biomedical community will greatly benefit from these user-friendly computational tools by enabling the deployment of robust training and interpretability analysis for any DNN trained on functional genomics assays. This, in turn, will drive new discoveries in cis-regulatory biology across the many biological systems that deep learning has already been applied to and the new applications that will continue to emerge in the future.

项目总结