Abstract Motivation Transcriptomic biomarker studies often require separate tools for normalization, classification, feature ranking, and visualization. MLHeatmap was developed to integrate these steps into a cross-platform, browser-based workflow and to support compact marker-panel discovery directly from count matrices. Results MLHeatmap accepts a count matrix and sample-group labels and performs gene mapping, normalization, multiclass classification with nested cross-validation, feature attribution, differential expression analysis, and interactive heatmap visualization, with support for multiple classifiers and panel-selection methods in the same workflow. As a case study, we applied MLHeatmap to colorectal cancer consensus molecular subtype (CMS) classification using 511 primary tumors from The Cancer Genome Atlas (TCGA) with published Colorectal Cancer Subtyping Consortium (CRCSC) labels. Random Forest with forward selection achieved 89.8% out-of-fold accuracy and a macro-averaged area under the receiver operating characteristic curve (macro AUC) of 0.973, yielding a 13-gene compact panel with a held-out AUC of 0.960. External validation of the compact panel gave 74.8% accuracy and a macro AUC of 0.917 in GSE39582, an independent Affymetrix GPL570 microarray cohort evaluated in 500 tumors with confident CMScaller-derived CMS assignments, and 69.18% accuracy and a macro AUC of 0.909 in CMCBSN, an independent Korean RNA-seq cohort with 159 confidently labeled tumors. Across the five classifiers and four panel-selection methods evaluated, compact-panel AUCs ranged from 0.895 to 0.970, with tree-ensemble classifiers reaching the highest values. Availability and implementation MLHeatmap is freely available at https://github.com/kangk1204/MLHeatmap and can be installed and run locally on Windows 11, macOS, and Ubuntu.
Eun Young Lee, Jihye Park, S. Youn et al.· Bioinformatics Advances· 0 citations
Phthalates and phenolic compounds are ubiquitous consumer chemicals, and children are particularly vulnerable to their potential health effects. In Thailand, however, information on children's exposure and regional variation remains limited. We recruited 250 children from two distinct regions of Thailand (Suphan Buri and Chiang Rai) in 2023, measured urinary concentrations of major metabolites of phthalates and alternative plasticizers as well as parent phenolic compounds, and administered questionnaires. The highest median concentrations were observed for the sum of di(2-ethylhexyl) phthalate metabolites (∑DEHP, 73.2 ng/mL), followed by mono-n-butyl phthalate (MnBP, 72.2 ng/mL) and the sum of diisononyl phthalate metabolites (∑DiNP, 22.6 ng/mL). These levels were 1.4-3.4 times higher than those in German children (GerES V, 2014-2017) and 2.5-5.7 times higher than those in U.S. children (NHANES, 2017-2018). Significant regional differences were identified, with 16 of 26 metabolites showing higher concentrations in Chiang Rai children. These included metabolites of diisobutyl phthalate (DiBP), di-n-butyl phthalate (DnBP), DEHP, DiNP, and di(2-ethylhexyl) terephthalate (DEHTP) as well as benzophenone-3 and bisphenol S. Notably, DiNP metabolite levels in Chiang Rai were 1.9-2.6-fold higher than the highest levels reported elsewhere. Risk assessment showed 69.3% of children in Chiang Rai and 16.4% in Suphan Buri exceeded a cumulative hazard index of 1. Regardless of region, DnBP, DEHP, DiBP, and di(2-ethylhexyl) adipate (DEHA) were the primary contributors to risk. Sauce consumption and medication use were associated with higher phthalate levels. These findings highlight the need to develop targeted exposure-reduction strategies and regulatory interventions for Thai children.
Ah-Reum Jo, P. Laor, Joeun Jung et al.· International Journal of Hyg...· 0 citations
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