DMS/NIGMS 2: A Stability Driven Recommendation System for Efficient Disease Mechanistic Discovery

DMS/NIGMS 2：用于高效疾病机制发现的稳定性驱动推荐系统

• 批准号: 10793779
• 负责人: Bin Yu
• 金额: $ 27.13万
• 依托单位: UNIVERSITY OF CALIFORNIA BERKELEY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-25 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10793779
• 关键词: Address; Algorithms; Awareness; Biological; Case Study; Clinical Trials; Complex; Data; Data Science; Decision Trees; Dependence; Disease; Effectiveness; Entropy; Etiology; Follow-Up Studies; Gene Silencing; Genes; Genetic; Genome; Hand; Head; Heart Diseases; Heart Hypertrophy; Heritability; Human; Investigation; Learning; Linkage Disequilibrium; Measures; Methodology; Methods; Modeling; National Institute of General Medical Sciences; Noise; Population; Problem Sets; Process; Proteins; Recommendation; Research Personnel; Risk Factors; Sampling; Signal Transduction; Small Interfering RNA; Source; Statistical Data Interpretation; Structure; Survival Analysis; System; Testing; Validation; Variant; Weight; Work; clinical practice; cohort; cost; disease heterogeneity; disease phenotype; effective therapy; experimental study; flexibility; follow-up; gene interaction; genome wide association study; improved; induced pluripotent stem cell derived cardiomyocytes; insight; multimodality; novel; random forest; simulation; success; trait

Overview. It is crucial to uncover the biological features underlying disease mechanisms to develop effective treatments and therapies. Typically, this is done via a two-step process: in stage 1, statistical analyses are used to recommend candidate variants/genes for follow-up investigation. In stage 2, researchers conduct costly experiments, clinical trials, or external studies via independent cohorts to validate or establish causality between candidate features and disease traits. To minimize costs, recommendations should lead to high-yield experiments and be replicable. These recommendations are often generated through GWAS methods, based on linear mixed models. Despite the successes of GWAS, there still exists a substantial heritability gap limiting the applicability of these associations in clinical practice. A number of key issues can contribute to missing heritability including: the need for more informative, multi-modal features; unidentified non-linear and epistatic effects; linkage disequilibrium among variants; and heterogeneous sources of variability. To confront these challenges, we propose a reality-checked stability-driven feature recommendation system based on decision trees that aims at efficient discoveries for high yields in experimentation. We build upon iterative random forests (iRF) and the veridical data science framework based on the principles of Predictability, Computability and Stability (PCS) developed by the PI to propose a number of novel advances for stage 1. We propose: (1) generalized MDI (gMDI) a stability-driven non-linear feature important measure for improving iRF recommendations; (2) dependence-aware feature and interaction discovery; (3) supervised local feature importance for heterogeneous mechanistic discoveries; and (4) validation through gene-silencing experiments. Importantly, we generate multi-modal features to extract information across the genome. Intellectual Merit. Our proposals: improve MDI-based methods by addressing drawbacks of MDI and tailoring to problem settings; incorporate gMDI and dependence structure in iRF; and detect heterogeneous sources of noise. Each aim will be vetted and follow the veridical data science framework. In the case study, we will recommend genes and interactions for gene-silencing experiments. These will supply valuable insights into genetic mechanisms underlying traits related to cardiac hypertrophy. Results of this work will impact mechanistic discovery for complex diseases and advance statistical methodology.

概述。揭示发展的生物学特征至关重要 有效的治疗和疗法。通常，这是通过两步过程完成的：在第1阶段，统计 分析用于建议候选变体/基因进行后续研究。在第2阶段， 研究人员通过独立队列进行昂贵的实验，临床试验或外部研究 验证或建立候选特征和疾病特征之间的因果关系。为了最大程度地减少成本， 建议应导致高收益实验并具有可复制性。这些建议是 通常是通过线性混合模型通过GWAS方法生成的。尽管取得了成功 GWAS，仍然存在一个巨大的遗传差距，限制了这些关联的适用性 临床实践。许多关键问题可能导致缺少遗传力，包括：需要更多 信息丰富的多模式特征；身份不明的非线性和上皮效应；连锁不平衡 在变体中；和异质的可变性来源。为了面对这些挑战，我们提出了一个 基于决策树的实现现实检查的稳定性驱动的功能推荐系统 实验中高收率的有效发现。我们建立在迭代的随机森林（IRF）和 基于可预测性，可计算性和稳定性原理的垂直数据科学框架 （PC）由PI开发以提出阶段1的许多新颖进步。我们提出：（1） 广义MDI（GMDI）稳定性驱动的非线性特征用于改善IRF的重要措施 建议； （2）依赖感知功能和互动发现； （3）监督当地功能 对于异质机械发现的重要性； （4）通过基因沉默验证 实验。重要的是，我们生成多模式特征以在整个基因组中提取信息。 智力优点。我们的建议：通过解决MDI和MDI的缺点来改善基于MDI的方法 针对问题设置量身定制；将GMDI和依赖性结构纳入IRF；并检测 噪音的异质来源。每个目标都将进行审查，并遵循Veridical数据科学框架。 在案例研究中，我们将建议基因和基因分解实验的相互作用。这些会 提供对与心脏肥大有关的遗传机制的宝贵见解。结果 这项工作将影响复杂疾病的机理发现并提高统计方法。