Latest AI and machine learning research in oncology/hematology for healthcare professionals.
T cell exhaustion limits the efficacy of cancer immunotherapies. Here, we performed genome-wide loss-of-function screening in repetitively stimulated human T cells and identified the mulitfunctional ubiquitin-modifying protein A20/TNFAIP3 as a major negative regulator of exhausted T cell persistence. Protein large language modeling, deep base-editing mutagenesis, and studies in immunocompetent mic...
Current DNA damage repair (DDR) biomarkers employ binary classifications that fail to capture the molecular complexity of tumors with concurrent repair deficiencies. We used genomics analysis to stratify 672 metastatic prostate cancer patients into 11 DDR subgroups, identifying 51 molecular signatures with weighted roles in class identity. We identified a tumor-mutational-burden very-high subset, ...
Spatially resolved omics enable the discovery of tissue organization of biological or clinical importance. Despite the existence of several methods, p...
The rational design of high-specificity binders to peptide–HLA (pHLA) complexes remains a major challenge in personalized immunotherapy, particularly ...
Triple-negative breast cancer (TNBC) is an aggressive malignancy with limited targeted therapies and variable responses to conventional chemotherapy, ...
Tumor Mutational Burden (TMB) is a widely used biomarker for selecting cancer patients for immune checkpoint inhibitor (ICI) therapy. However, TMB alo...
AGAPE (computational G-quadruplex Affinity Prediction) is a novel machine learning (ML)-based tool designed to predict the binding and stabilizing pot...
Glioblastoma (GBM), the most aggressive primary brain tumor, has a median survival of approximately 15 months. Twenty percent of patients survive beyo...
Foundation models have transformed natural language processing and computer vision, yet their potential in single-cell biology—particularly for comple...
Single-cell perturbation sequencing technologies (e.g., Perturb-seq, CROP-seq), which integrate CRISPR-based gene editing with single-cell transcripto...
The tumor microenvironment (TME) significantly impacts cancer progression and overall patient survival. However, the complexity of tumor cell-TME inte...
Muscle-Invasive Bladder Cancer (MIBC) is a heterogeneous disease with distinct molecular subtypes influencing prognosis and therapeutic response. Howe...
Biologically-informed neural networks (BiNNs) offer interpretable deep learning models for biological data, but the dataset characteristics required f...
Accurate in silico identification of B-cell epitope residues is crucial for antibody design and structure-guided vaccine development. Although recent ...
Precision oncology aims to guide treatment decisions using biomarkers. While DNA-based panels are increasingly applied, RNA transcriptomics remain und...
Female genital tumors (FGTs), including ovarian, endometrial, and cervical cancers, pose a major global health challenge, yet their spatial and molecu...
Primary and metastatic brain tumors are among the deadliest and treatment-resistant cancers, mainly because of their inherent resistance to chemoradia...
Context-specific protein-protein interaction (PPI) drive heterogeneity of primary tumor, forming a formidable challenge to effective cancer therapy. H...
As tumors develop, cancer cells accumulate diverse genomic and phenotypic alterations to meet heightened demands for energy production and biosynthesi...
Traditional machine learning approaches for text or sequence classification rely on converting textual data into numerical representations. In this st...