Oncology/Hematology

Latest AI and machine learning research in oncology/hematology for healthcare professionals.

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Showing 14501-14520 of 19,058 articles

Single-and double-strand circulating DNA fragmentomics for enhanced cancer detection performance

In early detection of cancer, the use of circulating cell-free DNA (cirDNA) obtained from blood samples is notable for its minimally invasive nature. We have developed an algorithm designed to discriminate cancer patients and healthy individuals based on cirDNA fragment end motif analysis assisted by machine learning, using data obtained from shallow whole genome sequencing (a method we call EMA)....

scAgeClock: a single-cell transcriptome based human aging clock model using gated multi-head attention neural networks

Aging Clock models have emerged as a crucial tool for measuring biological age, with significant implications for anti-aging interventions and disease risk assessment. However, human aging clock models that offer single-cell resolution and account for cell and tissue heterogeneities remain underdeveloped. This study introduces scAgeClock, a novel gated multi-head attention (GMA) neural network-bas...

FAST: Filamentous Actin Segmentation Tool for quantifying cytoskeletal organization

Studying how actin filaments are assembled into different subcellular structures can provide insights into both physiological processes and the mechan...

Conformal Prediction of Molecule-induced Cancer Cell Growth Inhibition Challenged by Strong Distribution Shifts

The drug discovery process often employs phenotypic and target-based virtual screening to identify potential drug candidates. Despite the longstanding...

Gene-Family Encoding Boosts Domain-Adapted Single-Cell Language Models

Transformer-based single-cell foundation models often rely on ranked-gene (RG) sequences where genes, ranked by expression, are often not functionally...

Accurate and scalable multi-disease classification from adaptive immune repertoires

Machine learning models trained on paratope-similarity networks have shown superior accuracy compared with clonotype-based models in binary disease cl...

AMICI: Attention Mechanism Interpretation of Cell-cell Interactions

Spatial transcriptomic data enable study of cell–cell communication, yet current analysis tools often fail to provide dynamic, interpretable estimates...

Predicting Clinical Outcomes in Helicobacter pylori-positive Patients using Supervised Learning through the Integration of Demographic and Genomic Features

Helicobacter pylori (H. pylori) infection is widespread globally and is linked to outcomes ranging from chronic gastritis to gastric cancer. However, ...

SynGlue: AI-Driven Designer for Clinically Actionable Multi-Target Therapeutics

The rational design of multi-targeting compounds and targeted protein degraders, such as PROTACs, remains a significant challenge in drug discovery. H...

Recovery of human upper airway epithelium after smoking cessation is driven by a slow-cycling stem cell population and immune surveillance

The upper airway epithelium in humans is maintained in homeostasis by a resident population of basal stem cells. In the presence of tobacco smoke thes...

Discovery and performance of DNA methylation panels for cancer detection and classification in blood

Examining DNA in a liquid biopsy for non-invasive cancer detection relies on identifying dilute signal in a high background. This study aims to identi...

Postprocessing-Enhanced Machine Learning for Reliable Real-Time Sleep Staging in Closed-Loop Neuromodulation

Real-time sleep stage classification is important for closed-loop neuromodulation at certain stages during sleep, yet current models often yield noisy...

Inferring spatial single-cell-level interactions through interpreting cell state and niche correlations learned by self-supervised graph transformer

Cell-cell interactions (CCI), driven by distance-dependent signaling, are important for tissue development and organ function. While imaging-based spa...

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, vaccine development, and our understanding of im...

Functional autophagy gene set signature and state classification reveal a link between autophagy induction, lysosomal activity, and poor prognosis in glioblastoma

Autophagy is an essential mechanism for maintaining cell homeostasis and, when dysregulated, is related to various pathologies. In cancer, it function...

Development of an EMT-related exosomal miRNA signature that can predict prognosis in hepatocellular carcinoma

Chemoresistance and epithelial-mesenchymal transition (EMT) are associated with failure of cancer chemotherapy and poor survival of patients. We have ...

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 endogenous peptides (antigens) presented on the cell ...

scPortrait integrates single-cell images into multimodal modeling

Machine learning increasingly uncovers rules of biology directly from data, enabled by large, standardized datasets. Microscopy images provide rich in...

STRUMP-I: Structure-based machine learning approach to pMHC-I binding prediction using force field energy features

The adaptive immune system monitors cellular integrity by recognizing short peptides from intracellular proteins presented on Major Histocompatibility...

DELPHAI, AI Agent for Predicting Drug Response and Resistance

Patient-derived organoids preserve critical tumor features and drug sensitivity patterns that mirror patient clinical responses, enabling single-cell ...

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