Xiao Li

Chlorobenzene compounds are environmentally relevant chlorinated chemicals for which efficient acute-hazard screening can complement experimental assessment. Reliable prediction of acute oral toxicity can support chemical prioritization, while conventional in vivo testing is costly and ethically constrained. Here, we compare global and structure-based stratified machine learning strategies under a common validation protocol. The fixed modeling dataset contained 540 mouse oral LD50 records, including 225 carbon-only and 315 heteroatom-containing compounds. We evaluated conventional RDKit descriptors (F1, 209 dimensions), KPGT-derived representations (F2, 2304 dimensions), and their combination (F3, 2513 dimensions) across Random Forest, LightGBM, CatBoost, and voting ensembles. A toxicity-stratified global hold-out test set comprising 20% of the data was reserved for final evaluation, while subgroup models were trained only on the corresponding subsets of the training partition. On this common test set, the global LightGBM model with F1 descriptors achieved the highest reported point estimates (accuracy = 0.889, balanced accuracy = 0.877, MCC = 0.753). Stratification did not establish a statistically significant improvement over the global strategy. SHAP analysis showed stable feature-importance rankings and subgroup-specific predictive associations, but these patterns do not establish distinct toxicity mechanisms. A Mahalanobis-distance applicability domain was used as a conservative extrapolation warning. The selected model is deployed through a public web platform for rapid screening of the study-specific higher acute-toxicity group (LD50 < 500 mg/kg) versus the at-or-above-threshold group (LD50 ≥ 500 mg/kg). The framework supports murine acute-hazard screening and prioritization; it does not constitute a complete environmental or human-health risk assessment.

Gene expression involves complex interactions between DNA, RNA, proteins, and small molecules. However, most existing molecular networks are built on limited interaction types, resulting in a fragmented understanding of gene regulation. Here, we present MulNet, a framework that organizes diverse molecular interactions underlying gene expression data into a scalable multilayer network. Additionally, MulNet can accurately identify gene modules and key regulators within this network. When applied across diverse cancer datasets, MulNet outperformed state-of-the-art methods in identifying biologically relevant modules. MulNet analysis of RNA-seq data from colon cancer revealed numerous well-established cancer regulators and a promising new therapeutic target, miR-8485, along with several downstream pathways it governs to inhibit tumor growth. MulNet analysis of single-cell RNA-seq data from head and neck cancer revealed intricate communication networks between fibroblasts and malignant cells mediated by transcription factors and cytokines. Overall, MulNet enables high-resolution reconstruction of intra- and intercellular communication from both bulk and single-cell data. The MulNet code and application are available at https://github.com/free1234hm/MulNet.

As a highly heterogeneous and complex disease, the identification of cancer’s molecular subtypes is crucial for accurate diagnosis and personalized treatment. The integration of multi-omics data enables a comprehensive interpretation of the molecular characteristics of cancer at various biological levels. In recent years, an increasing number of multi-omics clustering algorithms for cancer molecular subtyping have been proposed. However, the absence of a definitive gold standard makes it challenging to evaluate and compare these methods effectively. In this study, we developed a general framework for the comprehensive evaluation of multi-omics clustering algorithms and introduced an innovative metric, the accuracy-weighted average index, which simultaneously considers both clustering performance and clinical relevance. Using this framework, we performed a thorough evaluation and comparison of 11 state-of-the-art multi-omics clustering algorithms, including deep learning-based methods. By integrating the accuracy-weighted average index with computational efficiency, our analysis reveals that PIntMF demonstrates the best overall performance, making it a promising tool for molecular subtyping across a wide range of cancers.