Oncology/Hematology

Machine Learning-Driven Discovery of Synergistic Protein Interactions Identifies ATL3 as a Putative Biomarker for Cancer Drug Response

Resistance to targeted molecular therapies—both primary and acquired—remains a major obstacle to effective cancer treatment. Despite extensive research, the molecular determinants of treatment resistance are still incompletely understood, underscoring the need to identify robust resistance drivers to improve therapeutic outcomes. Recently, the ProCan and Wellcome Sanger Institute released the worl...

Batch-Harmonized Machine Learning Framework for Cross-Cohort RNA Biomarker Discovery in Pancreatic Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) lacks reliable prognostic biomarkers. RNA-based signatures suffer from poor reproducibility due to batch effects and platform heterogeneity between microarray and RNA-seq data, limiting machine learning applications. We developed a computational pipeline harmonizing RNA-seq data from multiple repositories using ComBat batch correction, followed by Random For...

Deep Learning links TP53 genotype to expression-defined transcriptional program in Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is a hematological cancer characterized by genetic diversity and poor clinical outcomes. Among various genetic mutations ...

Explainable Machine Learning for Preoperative Relapse Prediction in Molecularly Stratified Endometrial Cancer: A Single-Center Finnish Cohort Study

Relapse risk in endometrial carcinoma (EC) is strongly influenced by molecular subtype, yet current WHO/ESGO classifications rely on postoperative dat...

Diffusion Models vs. DCGANs for Class-Imbalanced Lung Cancer CT Classification: A Comparative Study

Effective lung cancer detection from CT scans remains critically challenged by class imbalance where benign and normal cases are underrepresented, lea...

Deep learning linking mechanistic models to single-cell transcriptomics data reveals transcriptional bursting in response to DNA damage

Cells must adopt flexible regulatory strategies to make decisions regarding their fate, including differentiation, apoptosis, or survival in the face ...

TEIP: A Compact, Open-Source Framework for Predicting Tumor Epitope Immunogenicity in Glioblastoma Using Deep Learning and Multi-Modal Biological Features

This work introduces a modular, open-source computational pipeline for glioblastoma (GBM) vaccine design that integrates omics-based OIP5 target disco...

Single recipient cell tracking of tellurium-labeled extracellular vesicle proteomes (TeLEV) identifies EV-driven immunomodulation

Extracellular vesicles (EVs) mediate tumor-immune cell communication by carrying protein cargo that can immediately modulate signaling and antigen pre...

Revealing the Paper Mill Iceberg: AI-Based Screening of Cancer Research Publications

To train and validate a machine learning model to distinguish paper mill publications from genuine cancer research articles, and to screen the cancer ...

A Multi-Modal Transfer Learning Framework to Reduce Health Disparities in Prostate Adenocarcinoma

Prostate cancer is the second most common cancer in men across the United States, of which prostate adenocarcinoma (PRAD) is the most common subtype. ...

Unraveling miRNA-Driven DNA Damage Response Networks in Pancreatic Adenocarcinoma: A Multi-Omics and Machine Learning Approach

Due to the late detection, aggressive nature, and paucity of treatment options, pancreatic adenocarcinoma (PAAD) remains one of the most lethal cancer...

Colon-Specific Epigenetic Clocks from Minimal Features Reveal Disease-Driven Aging

Epigenetic clocks estimate chronological and biological age from DNA methylation patterns, but conventional models typically train on hundreds of thou...

Machine-learning-based determination of sex-related bladder cancer biomarkers

Bladder cancer exhibits sex-specific behavior, occurring more frequently in males but progressing to advanced stages more commonly in females. The act...

A transcription factor-responsive enhancer discovery platform for targeted immunotherapy

Synthetic enhancers with high specificity are crucial for therapeutic gene control. Although existing experimental screens and machine learning-based ...

Dysregulated Microglial Synaptic Engulfment in Diffuse Midline Glioma

Diffuse midline glioma (DMG) is a near-universally lethal form of pediatric high-grade glioma, driven by neuronal activity-regulated paracrine signali...

Ensemble-Based Deep Learning for Breast Cancer Detection and Classification in Histopathological Images

Breast cancer remains one of the leading causes of cancer-related mortality worldwide, with early detection being crucial for improved patient outcome...

VIP-OT: Dissecting Single-Cell Biochemical State Dynamics under Perturbation via Vibrational Painting and Optimal Transport

Dissecting the heterogeneous response of individual cells towards genetic and chemical perturbations is central to understanding the dynamic functions...

Multiple instance learning on tile level-pathology images provides accurate and interpretable classification for breast cancer molecular subtypes

Accurate breast cancer molecular subtyping is critical for treatment decisions, yet standard methods such as immunohistochemistry and gene expression ...

A Computational Pipeline for Glioblastoma Vaccine Development: Integrating Novel Omics-Driven OIP5 Target Discovery to Create a Deep Learning-Based Immunogenicity Framework for Personalized Immunotherapy

This work introduces a modular, open-source computational pipeline for glioblastoma (GBM) vaccine design that integrates omics-based OIP5 target disco...

Hierarchical Machine Learning Uncovers Topological Signatures of Autophagy Regulation by Oral Bacteria in Oral Squamous Cell Carcinoma

Oral squamous cell carcinoma (OSCC) progression has been increasingly linked to dysbiosis of the oral microbiome. We hypothesized that pathogenic vers...