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

Madhuri Gollapalli1, Harsh Anand1,2, Satish Mahadevan Srinivasan1

  • 1Engineering Department Penn State Great Valley Malvern Pennsylvania USA.

Quantitative Biology (Beijing, China)
|February 12, 2026
PubMed
Summary

Predictive analytics and dimensionality reduction identify key genetic and clinical predictors for classifying diseased tissues in precision medicine, improving diagnostic accuracy.

Keywords:
L1000 dataset analysisclusteringk‐meanslandmark genesmultinomial logistic regressionnon‐landmark genesprincipal component analysistissue classification

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Precision medicine relies on predictive analytics for personalized patient care.
  • Identifying key genetic and clinical predictors is essential for disease classification.

Purpose of the Study:

  • To identify a subset of genetic and clinical variables for classifying diseased tissues.
  • To assess the predictive capabilities of genetic and clinical variables using the L1000 dataset.

Main Methods:

  • Clustering diseased tissue types using k-means.
  • Classification of diseased tissue types using multinomial logistic regression (MLR).
  • Dimensionality reduction using principal component analysis and Boruta.

Main Results:

  • Landmark genes showed statistically significant better performance in clustering diseased tissue types than random genes.
  • Clinical variables (morphology, gender, age of diagnosis) and genetic variables are important predictors.
  • MLR models indicated landmark genes can act as genetic predictors or proxies for clinical variables.

Conclusions:

  • Combining predictive analytics with dimensionality reduction effectively identifies key predictors in precision medicine.
  • This approach enhances diagnostic accuracy for personalized patient care.