Latest AI and machine learning research in oncology/hematology for healthcare professionals.
Pancreatic cancer (PC) patient-derived organoids (PDOs) faithfully recapitulate therapeutic responses but face clinical translation barriers, including high costs and technical complexity. To address these problems and the lack of frameworks for PDO-based drug-response assays, we developed ODFormer, a computational framework that simulates PC PDOs to predict clinically actionable, patient-specific...
We present a modular framework powered by large language models (LLMs) that automates and streamlines key tasks across the early-stage computational drug discovery pipeline. By combining LLM reasoning with domain-specific tools, the framework performs biomedical data retrieval, domain-specific question answering, molecular generation, property prediction, property-aware molecular refinement, and 3...
Cancer is a major source of mortality and morbidity all over the world that has caused more than 19 million new cases and nearly 10 million deaths in ...
Fluorescent microscopy imaging is vital to capturing single-cell spatial data, characterizing tissue organization and facilitating comprehensive analy...
Multiplexed immunofluorescence microscopy offers detailed insights into the spatial architecture of cancer tissue. However, classical single-cell anal...
Spatial transcriptomics (ST) technologies provide genome-wide mRNA profiles in tissue context but lack direct protein-level measurements, which are cr...
We introduce HLAIIPred, a deep learning model to predict peptides presented by class II human leukocyte antigens (HLAII) on the surface of antigen pre...
Despite advances in artificial intelligence (AI) within cancer research, its application toward realizing differentiation therapy in solid tumors rema...
A durable organotypic epithelial raft culture was established as a model of cervical precancer. Plausible time- and dose-dependent effects of cisplati...
Fragmentomics of plasma cell-free DNA (cfDNA) are emerging diagnostic biomarkers in cancer liquid biopsy, while the molecular regulations of cfDNA fra...
Liquid biopsies detect disease noninvasively by profiling cell-free nucleic acids that are secreted into the circulation. However, existing methods ex...
In cancer, intra- and inter-patient heterogeneity presents a significant challenge for therapeutic management, as patients with apparently similar pro...
Tissue microenvironments reprogram local cellular states in disease, yet current computational spatial methods remain descriptive and do not simulate ...
Ovarian cancer remains a danger to women’s health, and accurate screening tests would likely increase survival. Two established protein biomarkers, CA...
A robust artificial intelligence-assisted workflow for tumor assessment in pathology requires not only accurate classification but also precise lesion...
Accurate single-cell phenotypic classification in histopathological tissue sections is essential for understanding tumor behavior, identifying potenti...
The tumor microenvironment (TME) is a complex ecosystem of diverse cell types whose interactions govern tumor growth and clinical outcome. While multi...
Drug resistance in cancer is shaped not only by evolutionary processes but also by eco-evolutionary interactions between tumor subpopulations. These i...
Atopic dermatitis (AD) is a chronic inflammatory skin disease driven by complex interactions among genetic, environmental, and microbial factors; howe...
Deep learning (DL) has excelled in tissue image classification, presenting opportunities to discover biological behaviors escaping visual inspection. ...