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

摘要 我们资助的R01题为《通过创新机制克服肺癌方面的种族健康差距- 基于治疗策略“提出了产生高分辨率空间基因表达和单细胞 黑人和白人非小细胞肺癌患者肿瘤的单链RNA-序列分析。除了数据之外 R01中建议的分析以描述白人和黑人患者之间的差异，这些数据提供了 开发AI-ML模型的全面资源，可以帮助我们进一步了解 肺癌及其肿瘤微环境的细胞景观，从而为我们提供了新的组合 将克服健康差距并在不同种族/族裔群体之间实现平等的治疗方法。在……里面 对非OD-23-082号文件的回应，标题为《支持协作以改进 AI/ML-NIH支持的数据的就绪性，我们寻求开发管道将数据转换为AI/DL-Ready 格式，以便在高分辨率单细胞测序数据中应用人工智能/深度学习研究 R01等研究。此外，我们将探索计算方法，以克服普遍存在的 现有问题-抽样偏差，如不平衡的种族或性别，并确保数据准备好进行更公平的 以公平的方式进行AI/DL模型培训和预测。我们将通过两个具体目标来实现这些目标1) 发展一条“公平”管道，以减轻因黑人和黑人人口不平等而产生的抽样偏差的影响。 白人、男性和女性患者，并将scRNA-Seq数据转换为人工智能/深度学习模型就绪格式， 以及2)利用附加的scRNA-Seq数据在SCDL管道上验证特定目标1中的预处理数据 收集自亲本R01和公开可用的不平衡scRNA-Seq数据集。公平管道 将利用Gerchberg-Saxton算法(GS)将原始数据转换为AI/DL就绪 更适合人工智能调查的格式，通过纠正Black和 白人，男性和女性患者。我们假设，一旦GS转换完成，其余的 ScRNA-Seq数据集的特征在DL模型训练阶段将具有更均匀的贡献，以缓解 了解和研究与治疗相关的基因调控网络、细胞间相互作用 路径，并确定潜在目标。拟议管道的应用将克服偏见问题。 在公开可用的数据集和未来生成的数据中。所有数据都将在CSV中得到很好的记录 使用唯一列标签的格式，包括细胞标签和患者ID。其他信息，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，这一努力可以使关于癌症差异的生物学意义的发现成为可能，而不需要 数据偏差的影响，这与美国国立卫生研究院(NIH)促进健康的使命非常一致 并缩小健康差距。

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.