Scalable Bayesian Network analysis of multimodal FACS and SUMOylation data, with generalization to other big mixed biological datasets

多模式 FACS 和 SUMOylation 数据的可扩展贝叶斯网络分析，并推广到其他大型混合生物数据集

• 批准号: 10359178
• 负责人: Andrei Rodin
• 金额: $ 26.18万
• 依托单位: BECKMAN RESEARCH INSTITUTE/CITY OF HOPE
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10359178
• 关键词: Address; Algorithmic Analysis; Algorithmic Software; Algorithms; Alzheimer' s Disease; Bayesian Analysis; Bayesian Network; Belief; Biological; Cells; Chromatin; Cities; Clinical; Clinical Trials; Computer software; Data; Data Analyses; Data Set; Data Storage and Retrieval; Development; Epidemiology; Epigenetic Process; Evaluation; Evolution; Flow Cytometry; Genetic; Genomics; Goals; Hybrids; Immune signaling; Immunogenetics; Immunological Models; Letters; Literature; Malignant Neoplasms; Methodology; Methods; Modeling; Modernization; Outcome; Output; Pathway Analysis; Pathway interactions; Phenotype; Process; Proteomics; Public Health; Publications; Research; Research Personnel; Research Project Grants; Reverse engineering; Series; Sumoylation Pathway; Systems Biology; Testing; Transfer RNA; Visualization; Work; algorithm development; base; biological research; cancer epidemiology; data handling; experimental study; genetic epidemiology; genome-wide; genome-wide analysis; genomic data; heterogenous data; heuristics; high dimensionality; interest; interoperability; metabolomics; multimodality; network models; novel; parallelization; public health relevance; reconstruction; simulation; software development; tool; transcriptomics; usability; user-friendly; virtual

Scalable Bayesian Network analysis of multimodal FACS and SUMOylation data, with generalization to other big mixed biological datasets Abstract The Bayesian, or Belief, Network (BN) modeling is a powerful tool that is currently emerging as one of the principal data analysis, exploration and visualization methods for multimodal (aka mixed, or heterogeneous) “big” biological data. We have previously developed comprehensive BN algorithms and software package aimed at heterogeneous big biological data analysis. Over the recent years we have applied it to the different biological research domains / datasets (including chromatin interaction, tRNA evolution, genetic epidemiology and metabolomics, cancer epidemiology and single cell thymopoiesis data); work on three more projects (inferring immune signaling networks using FACS data, genome-wide SUMOylation, Alzheimer's genomic analysis) is currently in progress. In course of this work we have identified crucial “bottlenecks” that need to be addressed, on the methodological level, to make the BN analysis universally usable in our general context (that is, big biological data containing large numbers of variables of different types). These issues (scalability of the BN reconstruction process, handling mixed data types, and interpretation, evaluation & comparison of the resulting network models) have not been adequately addressed in the field yet, thus limiting the usability of the otherwise very powerful and elegant BN approach. Consequently, the primary goal of this project is to develop novel BN analysis algorithms with emphasis on (a) scalability, (b) handling mixed data types, and (c) resulting networks' interpretation and evaluation. We are particularly interested in the BN analysis of the quantitative flow cytometry (FACS) data generated as part of the ongoing City of Hope cancer immunogenetics research projects, as this type of data exemplifies BN modeling challenges, and any advances in algorithm and software development would be generalizable to most instances of big biological data. We will subsequently apply the BN analysis to the SUMOylation and chromatin interaction genomic data (also generated as part of the ongoing collaborative City of Hope research projects), to further test generalizability, and to produce additional biological results.

多模式FACS和SUMO化数据的可扩展贝叶斯网络分析， 推广到其他大型混合生物数据集 摘要 贝叶斯或信念网络（BN）建模是一种强大的工具，目前正在成为 多模态（又名混合，或 异构）“大”生物数据。我们以前已经开发了全面的BN算法 以及针对异构生物大数据分析的软件包。近年来，我们 已经将其应用于不同的生物研究领域/数据集（包括染色质相互作用， tRNA进化，遗传流行病学和代谢组学，癌症流行病学和单细胞 胸腺生成数据）;另外三个项目的工作（使用流式细胞仪推断免疫信号网络 数据，全基因组SUMO化，阿尔茨海默氏症基因组分析）目前正在进行中。过程中 在这项工作中，我们确定了需要解决的关键“瓶颈”， 水平，使BN分析在我们的一般背景下（即大生物数据）普遍可用 包含大量不同类型的变量）。这些问题（BN的可扩展性 重建过程，处理混合数据类型，以及解释，评估和比较 由此产生的网络模型）尚未在现场得到充分解决，因此限制了 这是一个非常强大和优雅的BN方法的可用性。 因此，本项目的主要目标是开发新的BN分析算法， 重点是（a）可扩展性，（B）处理混合数据类型，以及（c）最终网络的解释 和评价。我们对定量流式细胞术的BN分析特别感兴趣 作为正在进行的City of Hope癌症免疫遗传学研究项目的一部分， 由于这种类型的数据使BN建模面临挑战， 发展将可推广到大生物数据的大多数情况。我们随后将 将BN分析应用于SUMO化和染色质相互作用基因组数据（也作为 正在进行的希望之城合作研究项目的一部分），以进一步测试普遍性，并 产生额外的生物学结果。

项目成果

Regulation of Enhancers by SUMOylation Through TFAP2C Binding and Recruitment of HDAC Complex to the Chromatin.

通过 TFAP2C 结合和招募 HDAC 复合物到染色质，SUMO 化对增强子进行调节。

• DOI: 10.21203/rs.3.rs-4201913/v1
• 发表时间: 2024
• 期刊: Research square
• 作者: Abeywardana,Tharindumala;Wu,Xiwei;Huang,Shih-Ting;AldanaMasangkay,Grace;Rodin,AndreiS;Branciamore,Sergio;Gogoshin,Grigoriy;Li,Arthur;Du,Li;Tharuka,Neranjan;Tomaino,Ross;Chen,Yuan
• 通讯作者: Chen,Yuan

Patient generated health data and electronic health record integration in oncologic surgery: A call for artificial intelligence and machine learning.

• DOI: 10.1002/jso.26232
• 发表时间: 2021-01
• 期刊: Journal of surgical oncology
• 影响因子: 2.5
• 作者: Melstrom LG;Rodin AS;Rossi LA;Fu P Jr;Fong Y;Sun V
• 通讯作者: Sun V

Graph Neural Networks in Cancer and Oncology Research: Emerging and Future Trends.

• DOI: 10.3390/cancers15245858
• 发表时间: 2023-12-15
• 期刊: CANCERS
• 影响因子: 5.2
• 作者: Gogoshin, Grigoriy;Rodin, Andrei S.
• 通讯作者: Rodin, Andrei S.

Utilization of model-agnostic explainable artificial intelligence frameworks in oncology: a narrative review.

• DOI: 10.21037/tcr-22-1626
• 发表时间: 2022-10
• 期刊: Translational cancer research
• 影响因子: 0.9

Integration of artificial intelligence in lung cancer: Rise of the machine.

肺癌中人工智能的整合：机器的崛起。

• DOI: 10.1016/j.xcrm.2023.100933
• 发表时间: 2023-02-21
• 期刊: Cell reports. Medicine
• 作者: Ladbury C;Amini A;Govindarajan A;Mambetsariev I;Raz DJ;Massarelli E;Williams T;Rodin A;Salgia R
• 通讯作者: Salgia R