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Transforming dbGaP genetic and genomic data to FAIR-ready by artificial intelligence and machine learning algorithms

通过人工智能和机器学习算法将 dbGaP 遗传和基因组数据转变为 FAIR-ready

基本信息

批准号：
10842954
负责人：
Zhongming Zhao
金额：
$ 30.61万
依托单位：
UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-14 至 2025-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10842954
关键词：
ATAC-seq Acceleration Address Administrative Supplement Algorithmic Analysis Algorithms Alleles Alzheimer&apos s Disease Artificial Intelligence Attention Brain Brain Diseases Case Study Cells Chromosomes Code Cognitive Collection Complex Custom DNA sequencing DNase I hypersensitive sites sequencing Data Data Discovery Data Set Disease Funding Gene Expression Genes Genetic Genetic Markers Genetic Transformation Genetic study Genomics Genotype Glioblastoma Goals Grant Haplotypes Histones Human Image Image Analysis Impaired cognition Individual Investigation Language Logistic Regressions Machine Learning Methods Modeling Molecular Multiomic Data Multiple Sclerosis Mus Neural Network Simulation Pathway interactions Phenotype Principal Investigator Procedures Process Research Personnel Resolution Role Schizophrenia Screening procedure Standardization Testing Tissues Training United States National Institutes of Health United States National Library of Medicine Untranslated RNA Variant Vocabulary Weight Work artificial intelligence algorithm autoencoder cell type cognitive testing convolutional neural network database of Genotypes and Phenotypes deep learning deep learning algorithm deep learning model digital imaging dosage epigenomics gene discovery genetic resource genetic variant genome sequencing genome wide association study genomic data improved learning strategy machine learning algorithm malignant breast neoplasm multiple omics neural network algorithm phenotypic data programs prototype repository risk variant single-cell RNA sequencing spatiotemporal trait two-dimensional

项目摘要

dbGaP is a repository for NIH funded projects and it contains many genetic and genomic data. However, data there are not ready for AI and machine learning applications. This application proposes methods to address this issue. We have two aims: 1). Develop and standardize procedures to transfer genetic and genomic data into image like objects and tokenized custom vocabulary so that the data can be utilized by advanced AI algorithms such CNN, autoencoder and transformer. To transform genetic data into image, we recode allele dosage value as pixel intensity and arrange a collection of genetic markers such as SNPs and CNVs into an artificial image object so that it can be analyzed by CNN algorithms. Genetic markers can also be used to define haplotypes, which can be tokenized into custom vocabularies for use in NLP models. 2). Use Alzheimer's disease and schizophrenia as case studies to demonstrate the utilities of transformed data for the discovery and identification of risk variants/genes for both conditions. We plan to impute genetically controlled gene expression using brain specific eQTLs and individual genotypes for an AD dataset, and transform the expression data into image objects for analyses by CNN model with self attention mechanism. For schizophrenia, we plan to use k- mer tokenizer to break haplotypes into a collection of small haplotype blocks and treat them as tokens for analyses by NLP models. We use both CNN and NLP models as screen tools to select promising candidates using the attention weights, and then directly test these candidates for their association with AD/schizophrenia using logistic regression. Due to the selection effect, we can dramatically reduce the number of testing, significantly increase our statistical power to detect risk variants/genes to AD/schizophrenia.

dbGaP是NIH资助项目的存储库，它包含许多遗传和基因组数据。然而，那里的数据还没有为人工智能和机器学习应用做好准备。本申请提出了解决这一问题的方法。我们有两个目标：1）。发展和规范将遗传和基因组数据转换为图像状对象和标记化定制的过程词汇表，以便数据可以被先进的人工智能算法（如CNN、自动编码器）利用。和Transformer。为了将遗传数据转化为图像，我们将等位基因剂量值重新编码为像素将诸如SNP和CNV遗传标记的集合强度化并排列成人工图像对象，以便可以通过CNN算法进行分析。也可以使用遗传标记定义单倍型，可以标记成自定义词汇表，用于NLP模型。 2）。使用阿尔茨海默病和精神分裂症作为案例研究，以证明用于发现和鉴定两种病症的风险变体/基因的转换数据。我们计划使用脑特异性eQTL来估算遗传控制的基因表达， AD数据集的个体基因型，并将表达数据转换为图像对象用具有自注意机制的CNN模型进行分析。对于精神分裂症，我们计划使用k- mer标记化器将单倍型分解成小的单倍型块的集合，并将它们视为用于NLP模型分析的令牌。我们使用CNN和NLP模型作为屏幕工具，使用注意力权重选择有希望的候选者，然后直接测试这些候选者使用logistic回归分析其与AD/精神分裂症的关联。由于选择效应，我们可以大大减少测试的数量，显著提高我们的统计能力，检测AD/精神分裂症的风险变体/基因。

项目成果

期刊论文数量（42）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

DeepVISP: Deep Learning for Virus Site Integration Prediction and Motif Discovery.

DOI：
10.1002/advs.202004958
发表时间：
2021-05
期刊：
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
影响因子：
0
作者：
Xu H;Jia P;Zhao Z
通讯作者：
Zhao Z

TissGDB: tissue-specific gene database in cancer.

DOI：
10.1093/nar/gkx850
发表时间：
2018-01-04
期刊：
Nucleic acids research
影响因子：
14.9
作者：
Kim P;Park A;Han G;Sun H;Jia P;Zhao Z
通讯作者：
Zhao Z

Federated learning based futuristic biomedical big-data analysis and standardization.

DOI：
10.1371/journal.pone.0291631
发表时间：
2023
期刊：
PLOS ONE
影响因子：
3.7
作者：
Fathima, Afifa Salsabil;Basha, Syed Muzamil;Ahmed, Syed Thouheed;Mathivanan, Sandeep Kumar;Rajendran, Sukumar;Mallik, Saurav;Zhao, Zhongming
通讯作者：
Zhao, Zhongming

KinaseMD: kinase mutations and drug response database.