Maximizing the predictive power of high-throughput, microscopy-based phenotypic screens

最大限度地提高基于显微镜的高通量表型筛选的预测能力

基本信息

批准号：
10589939
负责人：
LANI F WU
金额：
$ 41.92万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-08-01 至 2026-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10589939
关键词：
Acceleration Active Learning Address Adoption Affect Biological Biological Markers Cancer cell line Cell Cycle Regulation Cell Line Chemical Structure Chemicals Classification Collection Communities Computer Vision Systems Computing Methodologies Consumption Data Set Detection Disease Disparate Drug Screening Drug resistance Epigenetic Process Evaluation Funding Future Generations Genetic Grant Image Individual Knowledge Laboratories Learning Libraries Machine Learning Malignant Neoplasms Methods Microscopy Modeling Molecular Target Mutation Pathway interactions Pharmaceutical Preparations Phenotype Property Proteomics Research Personnel Time cancer type candidate identification deep learning design drug discovery druggable target experimental study fighting imaging approach improved innovation learning strategy new therapeutic target novel patient subsets proteostasis response screening side effect small molecule transcriptomics

项目摘要

PROJECT SUMMARY We currently have an unprecedented ability to profile the genetic- and pathway-level changes that occur in cancer. Yet, clinicians lack the diverse arsenal of drugs needed to treat subpopulations of patients more effectively, reduce side effects and offer second-line treatment when drug resistance emerges. There is a pressing need to dramatically increase the repertoire of drugs available to fight cancer. Advances in automated microscopy and computer vision, allow the widespread use of phenotypic profiling in early drug discovery. In this grant, we address two challenges. First, the power of phenotypic profiling has led to a growing number of large, disparate image datasets. In aim 1, we will develop machine-learning approaches that combine disparate datasets to obtain accurate predictions of uncharacterized compound function. Second, phenotypic screens can identify candidate compounds across multiple, diverse pathways, but often only use a single cancer cell line. In aim 2, we will develop strategies to identify minimal collections of cell lines that maximize detection of small molecule activities. Successful execution will: increase the power of phenotypic profiling by harnessing existing phenotypic screening datasets and providing a rational approach for selecting cell lines that maximize the chance of discovering hits in desired pathways.

项目摘要目前，我们具有前所未有的能力，可以介绍癌症中发生的遗传和途径水平变化。然而，临床医生缺乏更有效治疗患者亚群所需的药物的多样化，减少副作用并在耐药性出现时提供二线治疗。迫切需要大大增加可与癌症作斗争的药物库。自动显微镜和计算机视觉的进步，可以在早期药物发现中广泛使用表型分析。在这笔赠款中，我们解决了两个挑战。首先，表型分析的力量导致了越来越多的大型图像数据集。在AIM 1中，我们将开发机器学习方法，这些方法结合了不同的数据集，以获得对未表征化合物功能的准确预测。其次，表型筛选可以鉴定出多种不同途径的候选化合物，但通常只使用单个癌细胞系。在AIM 2中，我们将制定策略，以确定最小的细胞系集合，以最大程度地检测小分子活性。成功的执行将：通过利用现有的表型筛选数据集并提供一种合理的方法来选择细胞系，从而最大程度地发现在所需途径中发现命中的机会，从而提高表型分析的功能。