Supporting IGVF by modeling genetics, function, and phenotype with machine learning

通过机器学习对遗传学、功能和表型进行建模来支持 IGVF

• 批准号: 10297060
• 负责人: Predrag Radivojac
• 金额: $ 35.63万
• 依托单位: NORTHEASTERN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10297060
• 关键词: Active Learning; Address; Adopted; Animal Model; Area; Biochemical; Biological; Biological Assay; Biological Models; Biological Process; Biology; Biotechnology; California; Clinical; Clinical assessments; Communities; Complex; Computer Models; DNA Sequence Alteration; Data; Data Collection; Data Set; Disease; Environment; Evaluation; Experimental Designs; Genes; Genetic Models; Genome; Genomic Segment; Genomic medicine; Genomics; Goals; Health system; Human; Human Genome; Internet; Knowledge; Leadership; Learning; Link; Machine Learning; Measures; Medical Genetics; Medicine; Mendelian disorder; Methodology; Methods; Mission; Modeling; Modernization; Molecular; Molecular Biology; Molecular Conformation; Molecular Structure; Mutation; Nature; Outcome; Output; Paper; Pathogenicity; Pharmacogenetics; Phenotype; Positioning Attribute; Publishing; RNA Splicing; Regulation; Research; Research Personnel; Resource Allocation; Resources; Risk; Site; Statistical Models; Supervision; System; Technology; Time; Tissues; Training; Transcriptional Regulation; Universities; Validation; Variant; Washington; Work; active method; assay development; base; biological systems; clinical care; clinical implementation; cost; design; diverse data; exome sequencing; experimental study; functional genomics; genetic variant; genome sequencing; genomic variation; human disease; improved; innovation; insight; interest; machine learning method; method development; model development; molecular modeling; mutation screening; next generation; novel; predictive modeling; predictive tools; protein structure; supervised learning; tool; trait

PROJECT SUMMARY Leveraging the power of the human genome to understand the risks, causes, and treatments of human dis- ease remains a grand challenge for all of biology and medicine. While sequencing costs have plummeted, and clinical implementation has become commonplace, interpreting human genomes remains a highly challenging task. It is our hypothesis that understanding the function of the genome and its products at a molecular, tissue, and phenotypic level using advanced machine learning will help unlock the door to better interpretation for sci- entific discovery and better clinical outcomes based on genomic medicine. To that end, our team has spent the past two decades working to develop computational models of biology, to predict how those models are perturbed through changes in the genome, and to use those perturbations to model phenotype and disease. We have had many research outputs in this area, having developed and published a number of widely used methods that predict biochemical and phenotypic changes caused by genetic variants to infer phenotype and pathogenicity. However, we believe that there is a coming convergence between the variability in clinical inter- pretation, high-throughput biotechnology assays, and modern machine learning methodology that will result in more accurate clinical assessments and improved clinical care. Therefore, in this ambitious proposal, we are addressing important questions in variant and genome interpretation consistent with this view and the mission of the IGVF Consortium. Our major goals include (1) developing advanced semi-supervised approaches to predict variants that disrupt molecular function and/or are capable of altering phenotypes; (2) identifying in- formative assays, variants, and genes to automate experimental design with an emphasis on resource alloca- tion and reduction of ascertainment bias in the Consortium; and (3) developing machine learning approaches to integrate these models into a workflow of the IGVF Consortium and enable the interaction between compu- tation and experiment in order to catalyze advances in both genetic variant interpretation and predictive model development.

项目摘要 利用人类基因组的力量来了解人类疾病的风险、原因和治疗方法， 对所有生物学和医学来说，轻松仍然是一个巨大的挑战。虽然测序成本大幅下降， 临床实施已经变得司空见惯，但解释人类基因组仍然是一个高度挑战性的问题。 任务我们的假设是，了解基因组及其产物在分子，组织， 和表型水平使用先进的机器学习将有助于打开大门，以更好地解释科学， 基于基因组医学的有效发现和更好的临床结果。为此，我们的团队花费了 在过去的二十年里，我们一直致力于开发生物学的计算模型， 通过基因组的变化而受到干扰，并使用这些干扰来模拟表型和疾病。 我们在这一领域有许多研究成果，开发并出版了一些广泛使用的 预测由遗传变异引起的生物化学和表型变化以推断表型的方法， 致病性然而，我们认为，在临床间的变异性之间有一个即将到来的收敛， 预处理，高通量生物技术测定和现代机器学习方法，将导致 更准确的临床评估和更好的临床护理。因此，在这一雄心勃勃的建议中，我们 解决变异和基因组解释中的重要问题与这一观点和使命一致 IGVF联盟的成员我们的主要目标包括（1）开发先进的半监督方法， 预测破坏分子功能和/或能够改变表型的变体;（2）识别- 形成分析，变异和基因，以自动化实验设计，重点是资源分配， 联盟中确定偏差的消除和减少;（3）开发机器学习方法 将这些模型集成到IGVF联盟的工作流程中，并使计算机之间能够进行交互， 为了促进遗传变异解释和预测模型的进步， 发展