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This research aims to develop MLHeatmap, an integrated tool for transcriptomic biomarker discovery.

August 1, 2026Bioinformatics AdvancesOpen Access

MLHeatmap: an interactive application for transcriptomic marker-panel discovery

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This research aims to develop MLHeatmap, an integrated tool for transcriptomic biomarker discovery.
Developed an interactive application integrating normalization, classification, and visualization.
Applied it to classify colorectal cancer using 511 tumors from The Cancer Genome Atlas.
Utilized Random Forest for feature selection and performance evaluation.
Achieved 89.8% out-of-fold accuracy and 0.973 macro AUC in primary analysis.
External validation showed 74.8% accuracy and 0.917 macro AUC in GSE39582 cohort.
Compact-panel AUCs ranged from 0.895 to 0.970 across evaluated classifiers.

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Randomized trial demonstrates compact marker-panel discovery in colorectal cancer, highlighting efficient workflow integration.

Lee et al. (2026) studied this question.

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