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Manifold representations and active learning for 21 st century biology

21 世纪生物学的流形表示和主动学习

基本信息

批准号：
10670057
负责人：
BONNIE BERGER
金额：
$ 35.89万
依托单位：
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-06-01 至 2026-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10670057
关键词：
Active Learning Algorithm Design Algorithms Big Data Biological Biology Biotechnology Cells Cellular biology Clustered Regularly Interspaced Short Palindromic Repeats Communities Complex Computational algorithm Computer software Data Data Set Development Dimensions Disease Experimental Designs Feedback Genes Genetic Genomics Heterogeneity High-Throughput Nucleotide Sequencing Individual Machine Learning Measures Memory Modeling Mutation Outcome Pathologic Performance Phenotype Property Proteomics Secure Software Tools Spatial Design State Interests Systems Biology Time Tissues Translating Uncertainty Untranslated RNA Validation Work cell growth computing resources design experimental study genomic data high dimensionality improved insight interest machine learning framework multimodality multiple omics novel precision medicine small molecule structural biology transcriptomics

项目摘要

Project Summary With the rise of high-throughput sequencing and multiplexed biotechnologies enabling single-cell multi-omics and massively parallel CRISPR experiments, the biomedical community is generating a monumental amount of data. These data promise to reveal new biology and drive personal and precision medicine. However, the sheer volume of genomic data is overwhelming current computational resources, requiring prohibitively high compute time, memory usage, and storage. My lab has been at the forefront of solving big data challenges in genomics, designing novel algorithms that enable efficient and secure analyses that were previously computationally infeasible, and that reveal novel structural, cellular, and systems biology. Drawing upon our expertise in developing scalable and insightful algorithms for analyzing genomic, transcriptomic, and proteomic data, we aim to tackle two key data-driven challenges facing the biological community: 1) efficient, accurate, and robust characterization of tissues at the single-cell level, and 2) translating high-throughput datasets into biological discoveries via machine learning-based prediction. To solve the first challenge, we will leverage our discovery that seemingly high-dimensional sequencing data often lies on low-dimensional manifolds that capture the underlying biological state of interest. We will design algorithms that generate these compact, meaningful manifold representations of single-cell omics datasets. This will enable a number of key applications including characterizing co-expression and gene-modules that define healthy and pathologic cell states; integrating multi-modal single-cell omics datasets to more richly characterize cellular diversity; and investigating the mechanisms underlying transcriptomic diversity across tissues and developmental states. To solve the second challenge, we will take a two-pronged approach. First, we will design novel machine learning frameworks that provide a measure of confidence when predicting in unfamiliar biological states, enabling prediction that is robust to “out-of-distribution” (unobserved) examples. We will then work with our experimental collaborators and CROs to rapidly perform experimental validation of model-based predictions. Finally, we will return the experimental results to the model to further improve performance. This will enable an “active learning” feedback loop to efficiently explore a complex biological space for outcomes of interest. We will use this uncertainty-powered active learning approach to explore several pressing biological concerns such as the identification of small molecule compounds with enzymatic or whole-cell growth inhibitory properties, efficient design of spatial- transcriptomic experiments, computationally guided CRISPR perturbation experiments, and identification of functional non-coding mutations. This project will result in 1) numerous software tools with wide utility that efficiently analyze massive biological datasets and guide complex experimentation, and 2) reveal biological insights, especially into biomolecular interactions and cellular heterogeneity.

项目摘要随着高通量测序和多重生物技术的兴起，和大规模并行CRISPR实验，生物医学界正在产生大量的数据这些数据有望揭示新的生物学，并推动个人和精准医疗。然而，大量的基因组数据压倒了当前的计算资源，需要极高的计算能力时间、内存使用和存储。我的实验室一直处于解决基因组学大数据挑战的最前沿，设计新的算法，使以前计算的有效和安全的分析，不可行，并揭示了新的结构，细胞和系统生物学。利用我们的专业知识，开发可扩展和有见地的算法，用于分析基因组，转录组和蛋白质组数据，我们的目标是解决生物界面临的两个关键的数据驱动的挑战：1）高效，准确和强大在单细胞水平上表征组织，以及2）将高通量数据集转化为生物学特性。通过基于机器学习的预测发现。为了解决第一个挑战，我们将利用我们的发现看似高维的测序数据通常位于低维流形上，潜在的生物学状态我们将设计算法来生成这些紧凑的，有意义的，单细胞组学数据集的多种表示。这将使许多关键应用程序，包括表征定义健康和病理细胞状态的共表达和基因模块; 多模态单细胞组学数据集，以更丰富地表征细胞多样性;并研究在组织和发育状态中转录组多样性的潜在机制。为了解决第二个问题面对挑战，我们将双管齐下。首先，我们将设计新颖的机器学习框架，在不熟悉的生物状态下进行预测时，提供一种置信度，从而实现稳健的预测到“未观察到的”（out-of-distribution）例子。然后，我们将与我们的实验合作者和CRO合作快速执行基于模型的预测的实验验证。最后，我们将把实验性的模型，以进一步提高性能。这将实现“主动学习”反馈循环，有效地探索复杂的生物空间，以获得感兴趣的结果。我们将使用这种不确定性驱动的积极的学习方法，探索几个紧迫的生物问题，如识别小具有酶或全细胞生长抑制特性的分子化合物，空间- 转录组学实验，计算引导的CRISPR扰动实验，以及功能性非编码突变。这个项目将导致1）众多的软件工具，具有广泛的实用性，有效地分析大量生物数据集并指导复杂的实验，以及2）揭示生物洞察力，特别是对生物分子相互作用和细胞异质性。