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Characterizing diseases using genetic and clinical variables:A data analytics approach

Quantitative Biology 2024年第3期12卷 271-285页

作者： Madhuri Gollapalli Harsh Anand Satish Mahadevan Srinivasan Engineering Department Penn State Great ValleyMalvernPennsylvaniaUSA Department of Systems and Information Engineering School of Engineering and Applied ScienceUniversity of VirginiaCharlottesvilleVirginiaUSA

Predictive analytics is crucial in precision medicine for personalized patient *** aid in precision medicine,this study identifies a subset of genetic and clinical variables that can serve as predictors for classifying diseased tissues/disease *** achieve this,experiments were performed on diseased tissues obtained from the l1000 dataset to assess differences in the functionality and predictive capabilities of genetic and clinical *** this study,the k-means technique was used for clustering the diseased tissue types,and the multinomial logistic regression(MlR)technique was applied for classifying the diseased tissue *** reduction techniques including principal component analysis and Boruta are used extensively to reduce the dimensionality of genetic and clinical *** results showed that landmark genes performed slightly better in clustering diseased tissue types compared to any random set of 978 non-landmark genes,and the difference is statistically ***,it was evident that both clinical and genetic variables were important in predicting the diseased tissue *** top three clinical predictors for predicting diseased tissue types were identified as morphology,gender,and age of ***,this study explored the possibility of using the latent representations of the clusters of landmark and non-landmark genes as predictors for an MlR *** classification models built using MlR revealed that landmark genes can serve as a subset of genetic variables and/or as a proxy for clinical *** study concludes that combining predictive analytics with dimensionality reduction effectively identifies key predictors in precision medicine,enhancing diagnostic accuracy.

关键词： clustering k-means l1000 dataset analysis landmark genes multinomial logistic regression non-landmark genes principal component analysis tissue classification

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