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基于症状的药物预测与生活方式相关的慢性疾病,使用无监督机器学习技术

Sudipto Bhattacharjee¹, Banani Saha¹, Sudipto Saha²

¹Department of Computer Science and Engineering, University of Calcutta, JD-2, Sector-III, Salt Lake, Kolkata, 700098, India.

Computers in biology and medicine

|April 12, 2024

概括

这项研究开发了一个无监督的机器学习工具,根据症状预测生活方式相关疾病 (LSD) 的药物. 该网络应用程序有助于临床医生在治疗心脏和肺部疾病的早期治疗决策.

科学领域:

计算生物学是一种计算生物学.
生物信息学是一种生物信息学.
机器学习在医疗保健中的应用

背景情况:

与生活方式相关的疾病 (LSD) 带来了重大的经济和健康负担,通常会影响心脏和肺部.
早期治疗LSD至关重要,基于症状的信息是临床医生可获得的首要数据.
在LSD中开发药物发现的预测模型对于及时的治疗干预至关重要.

研究的目的:

应用无监督机器学习 (ML) 技术来预测生活方式相关疾病 (LSD) 症状中的药物.
专注于开发肺部和心脏病的预测模型,这是LSD的一个子集.
为基于症状的药物预测创建一个用户友好的Web应用程序.

主要方法:

利用了143种LSD,1271种药物和305种症状的药物-疾病,疾病-症状关联来计算直接的药物-症状关联.
开发并比较了四种ML集群算法 (K-Means,Bisecting K-Means,Mean Shift,BIRCH) 以根据症状特征分组药物.
保存了最佳的ML模型并开发了一个用于从输入症状预测药物的Web应用程序.

主要成果:

截断K-means模型表现出卓越的性能,实现了0.647的轮系数,并产生了138种不同的药物集群.
鉴定集群中的药物表现出基于基因本体学,化学本体学和最大共同基体结构分析的显著相似性.
该网络应用程序为预测的药物提供了可信度评分,增强了其用于临床决策支持的实用性.

结论:

直接的药物症状关联被计算和利用,以创建一个新的,无监督的基于ML的工具来预测LSD的药物.
这种基于ML的预测工具可以作为临床医生有价值的第二意见,为LSD患者提供更早的治疗启动.
一个公开可访问的Web应用程序 (http://bicresources.jcbose.ac.in/ssaha4/sdldpred) 为最终用户提供了一个简单的界面,可以使用基于ML的药物预测工具.

关键词:

集群集成是指集群集成.药物药物药物是一种药物.与生活方式相关的疾病机器学习是机器学习.症状症状症状

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