Oncology/Hematology

Skin Cancer

Latest AI and machine learning research in skin cancer for healthcare professionals.

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SpaPheno: Linking Spatial Transcriptomics to Clinical Phenotypes with Interpretable Machine Learning

Linking spatial transcriptomic data to clinically relevant phenotypes is essential for advancing spa...

BLMPred: predicting linear B-cell epitopes using pre-trained protein language models and machine learning

B-cells get activated through interaction with B-cell epitopes, a specific portion of the antigen. I...

CryoPhold: CryoEM meets AlphaFold and molecular simulation to reveal protein dynamics

Here we are introducing CryoPhold, a modular workflow that unifies AlphaFold-based ensemble generati...

Tricked by Edge Cases: Can Current Approaches Lead to Accurate Prediction of T-Cell Specificity with Machine Learning?

The ability to predict T cell receptor (TCR) specificity from sequence could transform immunotherapy...

LoFT-TCR: A LoRA-based Fine-tuning Framework for TCR-Antigen Binding Prediction

T cells recognize and eliminate diseased cells by binding their T cell receptors (TCRs) to short end...

CNN-based learning of single-cell transcriptomes reveals a blood-detectable multi-cancer signature of brain metastasis

Brain metastasis (BrM) is a serious complication of advanced cancers and remains difficult to predic...

FFixR: A Machine Learning Framework for Accurate Somatic Mutation Calling from FFPE RNA-Seq Data in Cancer

Formalin-fixed paraffin-embedded (FFPE) tissues are widely used in clinical and research settings, y...

Integrated analysis implicates novel insights of NMB into lactate metabolism and immune response prediction in primary glioblastoma

Glioblastoma (GBM), the most aggressive primary brain tumor in adults, exhibits profound treatment r...

An Immuno-Linguistic Transformer for Multi-Scale Modeling of T-Cell Spatiotemporal Dynamics

Understanding the spatiotemporal dynamics of T-cell clones is a critical challenge in immunology and...

Base-editing a single missense mutation in A20 enhances CAR-T cell efficacy

T cell exhaustion limits the efficacy of cancer immunotherapies. Here, we performed genome-wide loss...

mosna reveals different types of cellular interactions predictive of response to immunotherapies and survival in cancer

Spatially resolved omics enable the discovery of tissue organization of biological or clinical impor...

AlphaMissense pathogenicity scores predict response to immunotherapy and enhances the predictive capability of tumor mutation burden

Tumor Mutational Burden (TMB) is a widely used biomarker for selecting cancer patients for immune ch...

RoBep: A Region-Oriented Deep Learning Model for B-Cell Epitope Prediction

Accurate in silico identification of B-cell epitope residues is crucial for antibody design and stru...

Predictive power of different Akkermansia phylogroups in clinical response to PD-1 blockade against non-small cell lung cancer

Immune checkpoint blockade has emerged as a promising form of cancer therapy. However, only some pat...

Language may be all omics needs: Harmonizing multimodal data for omics understanding with CellHermes

Decoding cellular systems requires integrating diverse omics data, yet most models are trained from ...

IMMUNIA: A Multi-LLM Reasoning Agent for Immunoregulatory Surfaceome Discovery

Biomarker discovery for immunotherapy often requires reasoning across complex immune contexts. We pr...

Targeting peptide–MHC complexes with designed T cell receptors and antibodies

Class I major histocompatibility complexes (MHCs), expressed on the surface of all nucleated cells, ...

OS2CR-Diff: A Self-Refining Diffusion Framework for CD8 Imputation from One-Step Inference to Conditional Representation

Stain imputation in multiplex immunofluorescence (mIF) imaging addresses the challenge of missing or...

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