Oncology/Hematology

Cell type–specific functions of nucleic acid-binding proteins revealed by deep learning on co-expression networks

Nucleic acid-binding proteins (NABPs) exhibit cell type–specific regulatory functions, but their target genes and biological roles remain incompletely characterized due to the limitations of current experimental approaches. Here, we present a deep learning framework that integrates gene co-expression correlations to predict NABP regulatory targets and infer their functions across diverse cellular ...

Machine-Learning-Assisted Exploration of High Entropy-Atom Nanozyme for Anti-Tumor Immunotherapy by Enhancing Enzyme Activity and Disrupting Dual Energy Metabolism

Despite its potential in cancer therapy, single-atom nanozyme (SAzyme) faces challenges like low atomic loading and rapid cancer metabolism. Here, a high-entropy atom nanozyme (HEAzyme), PtNiBiSnSb-anti-CD36, is designed for efficient anti-tumor immunotherapy. The PtNiBiSnSb HEAzyme, incorporating five SAzyme, demonstrates enhanced peroxidase (POD)-like activity due to its abundant active sites, s...

Clinical and molecular characterisation of primary refractoriness to atezolizumab plus bevacizumab in patients with unresectable hepatocellular carcinoma

Despite improved outcomes with atezolizumab plus bevacizumab (A+B) in hepatocellular carcinoma (HCC), primary refractoriness (PRef), characterised by ...

AlphaGenome: advancing regulatory variant effect prediction with a unified DNA sequence model

Deep learning models that predict functional genomic measurements from DNA sequence are powerful tools for deciphering the genetic regulatory code. Ex...

CellFuse Enables Multi-modal Integration of Single-cell and Spatial Proteomics data

Single-cell and spatial proteomic technologies capture complementary biological information, yet no single platform can measure all modalities within ...

Decoding Breast Cancer Heterogeneity via Multi-Omics Integration and Language Model-Based Interpretation

We present a novel pipeline combining Multi-Omics Factor Analysis (MOFA) and fine-tuned Large Language Models (LLMs) to predict breast cancer subtypes...

Cancer Alpha: A Production-Ready AI System for Multi-Modal Cancer Genomics Classification

The integration of multi-modal genomic data for cancer classification remains challenging in precision oncology. While machine learning approaches hav...

Allostery is a widespread cause of loss-of-function variant pathogenicity

Allosteric communication between non-contacting sites in proteins plays a fundamental role in biological regulation and drug action. While allosteric ...

LeGO-Teknik reveals that the neurogenesis pathway is a clone-specific hallmark of brain metastasis in breast cancer

Breast cancer exhibits substantial inter- and intra-patient heterogeneity. Yet the molecular features underlying this diversity and their roles in tum...

Quantum Cognition Machine Learning for Forecasting Chromosomal Instability

The accurate prediction of chromosomal instability from the morphology of circulating tumor cells (CTCs) enables real-time detection of CTCs with high...

Mechanistically Informed Machine Learning Links Non-Canonical TCA Cycle Activity to Warburg Metabolism and Hallmarks of Malignancy

Cancer cells undergo extensive metabolic rewiring to support growth, survival, and phenotypic plasticity. A non-canonical variant of the tricarboxylic...

CanID: a robust and accurate RNAseq Expression-based diagnostic classification scheme for pediatric malignancies

Cancer subtype classification is critical for precision therapy and there is a growing trend of augmenting histopathology testing procedures with omic...

Quantum Convolutional HLA Immunogenic Peptide Prediction (Q-CHIPP): Next-Generation Neoantigen Prediction with Quantum Neural Networks

The immune system is an intricately evolved series of cellular and protein-protein interactions, which defend the body against pathogens and abnormal ...

Integrated histopathologic modeling of detailed tumor subtypes and actionable biomarkers

Accurate cancer subtyping with accompanying molecular characterization is critical for precision oncology. While machine learning approaches have been...

GIN-CRC-Pareto: A graph-based Pareto-optimal multi-task learning framework to identify miRNA-target interactions in colorectal cancer

Colorectal cancer (CRC) ranks as the third highest incidence among malignancies in humans and the second most common cause of cancer-related mortality...

Colorectal cancer heterogeneity co-evolves with tumor architecture to determine disease outcome

Intratumoral heterogeneity, originating from genetic, epigenetic, and phenotypic cellular diversity, is pervasive in cancer. As these heterogeneous st...

DeepPROTECTNeo: A Deep learning-based Personalized and RV-guided Optimization tool leveraging TCR Epitope interaction using Context-aware Transformers

The development of personalized cancer vaccines relies heavily on accurately identifying neoepitopes capable of eliciting strong immune responses. T c...

CART-GPT: A T Cell-Informed AI Linguistic Framework for Interpreting Neurotoxicity and Therapeutic Outcomes in CAR-T Therapy

Chimeric antigen receptor (CAR) T cell therapy holds transformative potential for hematologic malignancies, yet predicting patient-specific treatment ...

Detection of prostate cancer in 3D pathology datasets via generative immunolabeling

Recent advancements in nondestructive 3D pathology offer a complement to standard histology by enabling comprehensive volumetric analyses of intact cl...

Generating Bounded Linear Temporal Logic in Systems Biology with Large Language Models

In computational modeling, Bounded Linear Temporal Logic (BLTL) is a valuable formalism for describing and verifying the temporal behavior of biologic...