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Enhancing AI-readiness of multi-omics data for cancer pharmacogenomics

增强癌症药物基因组学多组学数据的人工智能就绪性

基本信息

批准号：
10840074
负责人：
Yu-Chiao Chiu
金额：
$ 31.8万
依托单位：
UNIVERSITY OF PITTSBURGH AT PITTSBURGH
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-03-01 至 2025-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10840074
关键词：
Administrative Supplement Artificial Intelligence Awareness Bioinformatics Biology Biomedical Engineering Biomedical Research Clinical Collaborations Communities Complex Comprehension Data Data Set Development Discipline Drug Modelings Ensure Event Fostering Gene Expression Gene Mutation Genes Genomics Goals Image Malignant Childhood Neoplasm Malignant Neoplasms Methodology Methods Modeling Multiomic Data Parents Pediatric Neoplasm Pharmaceutical Preparations Pharmacogenomics Prediction of Response to Therapy Readiness Research Personnel Resources Scientist Structure United States National Institutes of Health artificial intelligence method cancer cell community engagement data format deep learning deep learning model drug sensitivity genomic data image guided improved multidimensional data multiple omics novel novel therapeutics parent project predictive modeling response symposium tool web server

项目摘要

Summary/Abstract The scarcity of comprehensive pharmacogenomics resources poses a significant obstacle to the development of new therapies for pediatric cancers. Our parent R00 project seeks to overcome this challenge by creating and validating a novel deep learning model for predicting drug sensitivity of pediatric tumors using integrative multi- omics profiles. However, the utilization of cutting-edge deep learning models to analyze multi-omics is often challenging since the data are high-dimensional and unstructured. Under the parent project, we have evaluated several embedding methods to transform multi-omics data into a structured format that enables artificial intelligence (AI) applications. In response to NOT-OD-23-082 “Administrative Supplements to Support Collaborations to Improve the AI/ML-Readiness of NIH-Supported Data,” we propose to supplement the parent project by further enhancing the AI-readiness of multi-omics data for studying cancer pharmacogenomics. Our hypothesis is that biology-guided image embedding of unstructured multi-omics data enhances the information captured by deep learning, enabling accurate modeling and prediction of treatment responses. We aim to achieve three relevant, but independent, goals: 1) methodology: to develop better data conversion methods for AI-readiness of cancer multi-omics, 2) accessibility: to make AI-ready tools and data more accessible to the biomedical research community, and 3) engagement: to promote collaboration on AI-readiness among the communities of bioinformatics, biomedical engineering, and biomedicine. Specifically, Aim 1 will evaluate a comprehensive array of biologically meaningful ways to transform unstructured multi-omics data, including gene mutation and gene expression profiles, to an image-like data format that can be analyzed by convolutional models. Our approach will embed functional similarities of genes to ensure interpretability. In Aim 2, we will develop an interactive web server that provides easy access to data conversion tools and AI-ready cancer data. Finally, in Aim 3, we will organize a community engagement event at a flagship conference of bioinformatics to enhance awareness of current gaps in AI-readiness and foster collaboration and diversity among clinical, basic, and computational scientists and trainees. The proposed supplement has brought together a collaborative team of experts from diverse disciplines, covering cancer bioinformatics, genomics and pharmacogenomics, AI methodology, and community engagement events. Successful completion of this supplement will have a significant impact on advancing the AI-readiness of large cancer data, aligning with the objectives of the parent R00 project.

摘要/文摘