Algorithm-based prevention and reduction of cancer health disparity arising from data inequality

少数民族群体在生物医学研究和临床中具有长期累积的数据劣势 问题研究统计显示，在癌症相关的GWAS和临床组学项目中， 是从欧洲血统的个体中收集的。少数民族的这一严重数据劣势 随着数据驱动的、基于算法的生物医学研究和临床研究的发展， 决策变得越来越普遍。由数据不平等引起的新的癌症差异可能会 在存在数据不平等的所有类型癌症中影响所有少数民族群体。其负面影响是 不限于已经明显存在显著种族差异的癌症类型或亚型。的 拟议研究的长期目标是防止或减少由数据引起的健康差异 少数民族群体的弱势。这项工作的总体目标是获得关键知识， 开放资源，建立一个新的范例，机器学习与多民族临床组学数据。我们的中央 一个假设是，从大多数人口的数据中学到的知识可以转移到改善 机器学习在数据弱势少数民族群体上的表现。以强有力的初步指导 数据，我们将追求两个具体目标：1）从癌症临床组学数据和基因型-表型中发现 数据：在什么条件下，迁移学习方案在多大程度上改善了机器学习模型 数据弱势少数民族群体的表现; 2）创建一个开放的资源系统， 多种族机器学习，以防止或减少由于数据劣势而产生的新的健康差异。 少数民族。这一方法是创新的，因为它代表着对现状的实质性背离 通过将多民族机器学习的范式从混合学习和独立学习转变为 迁移学习方案。这项研究意义重大，因为它有望建立 一个无偏见的多种族机器学习的新范式，并提供一个开放的资源系统，以促进 模式的转变，从而防止或减少由于少数民族的数据劣势而产生的健康差距， 少数群体

Ethnic minority groups have a long-term cumulative data disadvantage in biomedical research and clinical studies. Statistics have shown that over 90% of the samples in cancer-related GWAS and clinical omics projects were collected from Individuals of European ancestry. This severe data disadvantage of the ethnic minority groups is set to produce new health disparities as data-driven, algorithm-based biomedical research and clinical decisions become increasingly common. The new cancer disparity arising from data inequality can potentially impact all ethnic minority groups in all types of cancers where data inequality exists. Thus, its negative impact is not limited to the cancer types or subtypes for which significant ethnic disparities have already been evident. The long-term goal of the proposed research is to prevent or reduce the heath disparities arising from the data disadvantage of ethnic minority groups. The overall objective of this work is to obtain key knowledge and create open resources to establish a new paradigm for machine learning with multiethnic clinical omics data. Our central hypothesis is that the knowledge learned from data of the majority population can be transferred to improve machine learning performance on the data-disadvantaged ethnic minority groups. Guided by strong preliminary data, we will pursuit two specific aims to 1) Discover from cancer clinical omics data and genotype-phenotype data: under what conditions and to what extent the transfer learning scheme improves machine learning model performance on data-disadvantaged ethnic minority groups; 2) Create an open resource system for unbiased multiethnic machine learning to prevent or reduce new health disparities arising from the data disadvantage of ethnic minorities. The approach is innovative because it represents a substantive departure from the status quo by shifting the paradigm of multiethnic machine learning from mixture learning and independent learning schemes to a transfer learning scheme. The proposed research is significant, because it is expected to establish a new paradigm for unbiased multiethnic machine learning and to provide an open resource system to facilitate the paradigm shift, and thus to prevent or reduce health disparities arising from the data disadvantage of ethnic minorities.