Developing unbiased AI/Deep learning pipelines to strengthen lung cancer health disparities research

开发公正的人工智能/深度学习管道以加强肺癌健康差异研究

基本信息

批准号：
10841956
负责人：
Liang Liu
金额：
$ 30.55万
依托单位：
WAKE FOREST UNIVERSITY HEALTH SCIENCES
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-04-10 至 2028-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10841956
关键词：
Administrative Supplement Age Algorithms Area Baptist Church Bioinformatics Biomedical Engineering Black Populations Black race Cancer Biology Cancer Center Cancer Patient Cancer health equity Catchment Area Cell Communication Cells Clinical Trials Collaborations Combined Modality Therapy Comprehensive Cancer Center Computing Methodologies Data Data Analyses Data Set Deposition Development Disease Disparity population Ensure Entropy Equity Ethnic Population Event Excision Female Funding Future Gender Gene Expression Genes Genomics Goals Health Disparities Research Health Promotion Incidence Inequality Interdisciplinary Study Investigation Label Malignant neoplasm of lung Methodology Mission Modeling Molecular Non-Small-Cell Lung Carcinoma Oxidation-Reduction Paper Parents Pathway interactions Patients Performance Phase Process Race Readiness Reduce health disparities Research Research Personnel Resolution Resource Development Sampling Sampling Biases Sex Bias Standardization Testing Therapeutic Training United States National Institutes of Health Work black lung black patient cancer health disparity cancer type data sharing deep learning deep learning model epidemiologic data ethnic bias forest gene regulatory network health disparity improved innovation insight lipid metabolism male mortality novel novel therapeutics parent grant predictive modeling racial bias racial health disparity racial population response single cell sequencing single-cell RNA sequencing skills success targeted treatment therapeutically effective tumor tumor microenvironment

项目摘要

SUMMARY Our funded R01 entitled “Overcoming racial health disparities in lung cancer through innovative mechanism- based therapeutic strategies” proposes to generate high-resolution spatial gene expression and single-cell sequencing (scRNA-Seq) profiles of tumors in Black and White patients with NSCLC. In addition to the data analysis proposed in the R01 to depict the differences between White and Black patients, these data provide a comprehensive resource for the development of AI-ML models that can help us to gain further insight into the cellular landscapes of lung cancer and its tumor microenvironment, thus informing us on novel combination therapies that would overcome health disparities and achieve equity among different racial/ethnic groups. In response to the NOT-OD-23-082 entitled, Administrative Supplements to Support Collaborations to Improve the AI/ML-Readiness of NIH-Supported Data, we seek to develop pipelines to transform the data into an AI/DL-ready format to enable application of AI/deep learning research in high-resolution single cell sequencing data from this R01 and other studies. Moreover, we will explore computational methodologies to overcome the commonly existing issue – sampling bias, such as unbalanced races or genders, and ensure that data is ready for a fairer AI/DL model training and prediction in a fair fashion. We will achieve these goals through two Specific Aims 1) to develop a “Fairness” pipeline to mitigate the effects of sampling bias due to population inequalities in Black and White, Male and Female patients and to transform scRNA-Seq data into AI/deep learning model-ready format, and 2) to validate the preprocessed data in Specific Aim 1 on a scDL pipeline with additional scRNA-Seq data collected from the parent R01 and publicly available unbalanced scRNA-Seq datasets. The Fairness pipeline will take advantages of the Gerchberg-Saxton algorithm (GS) that can transform raw data into an AI/DL-ready format that is more suitable for AI investigations by correcting bias caused by sampling inequalities in Black and White, Male and Female patients. We hypothesize that once the GS transformation is completed, the remaining features of the scRNA-Seq dataset will have more uniform contribution in the DL model training phase easing to understanding and investigation of gene regulatory networks, cell-to-cell interactions, therapeutic-relevant pathways, and identifying potential targets. The application of the proposed pipeline will overcome the bias issue in publicly available datasets and the data generated in the future. All data will be well documented in a CSV format with unique column labels including cell labels and patient ID. Other information such as sample ID, race, gender, cancer type/subtype, age, etc., will also be documented and available for public use through data sharing. We believe that this effort can enable biologically meaningful discoveries regarding cancer disparities without the impact from data bias, which aligns well with the NIH (National Institutes of Health) mission to promote health and reduce health disparities.

概括我们资助的R01题为“通过创新机制克服肺癌的种族健康差异- 基于治疗策略”提出生成高分辨率空间基因表达和单细胞除了数据之外，还对黑人和白人 NSCLC 患者的肿瘤进行测序 (scRNA-Seq) 分析。 R01 中提出的分析描述了白人和黑人患者之间的差异，这些数据提供了用于开发 AI-ML 模型的综合资源，可以帮助我们进一步深入了解肺癌的细胞景观及其肿瘤微环境，从而为我们提供新的组合能够克服健康差异并实现不同种族/族裔群体之间平等的疗法。对题为“支持合作改进行政补充”的 NOT-OD-23-082 的回应 NIH 支持的数据的 AI/ML 就绪性，我们寻求开发管道将数据转换为 AI/DL 就绪型格式，以实现人工智能/深度学习研究在高分辨率单细胞测序数据中的应用此外，我们将探索克服常见问题的计算方法。现有问题 – 抽样偏差，例如种族或性别不平衡，并确保数据为更公平的数据做好准备我们将通过两个具体目标来实现这些目标：1）以公平的方式进行 AI/DL 模型训练和预测。制定“公平”渠道，以减轻黑人和黑人人口不平等造成的抽样偏差的影响白人、男性和女性患者，并将 scRNA-Seq 数据转换为 AI/深度学习模型就绪格式， 2) 使用额外的 scRNA-Seq 数据在 scDL 管道上验证特定目标 1 中的预处理数据从父 R01 和公开可用的不平衡 scRNA-Seq 数据集收集。将利用 Gerchberg-Saxton 算法 (GS)，将原始数据转换为 AI/DL 就绪的数据通过纠正黑人和黑人中抽样不平等引起的偏差，更适合人工智能调查的格式我们勇敢地说，一旦 GS 改造完成，剩下的人。 scRNA-Seq 数据集的特征将在 DL 模型训练阶段有更统一的贡献了解和研究基因调控网络、细胞间相互作用、治疗相关途径，并确定潜在目标的应用将克服偏差问题。公开可用的数据集中，并且将来生成的数据将详细记录在 CSV 中。具有独特的列标签的格式，包括细胞标签和患者 ID 其他信息，例如样本 ID、种族、性别、癌症类型/亚型、年龄等也将被记录下来并通过数据共享供公众使用。我们相信，这项努力可以在不影响癌症差异的情况下，实现具有生物学意义的发现。数据偏差的影响，这与 NIH（美国国立卫生研究院）促进健康的使命非常吻合并减少健康差距。