Leukemia

Know Your Scientist: KYC as Biosecurity Infrastructure

Biological AI tools for protein design and structure prediction are advancing rapidly, creating dual-use risks that existing safeguards cannot adequately address. Current model-level restrictions, including keyword filtering, output screening, and content-based access denials, are fundamentally ill-suited to biology, where reliable function prediction remains beyond reach and novel threats evade d...

Selective Glucocorticoid Receptor Modulators of Immune Checkpoint Function

Glucocorticoids (GCs) coordinate immunity, inflammation and metabolism through allosteric regulation of the glucocorticoid receptor (GR) transcription factor. GCs are indispensable anti-inflammatory drugs yet linking specific ligand and receptor structural states to specific biological outcomes has remained a major barrier to designing safer, more selective therapies. Using structure based design,...

Prediction of Mutations and Outcome in Gastrointestinal Stromal Tumors with Deep Learning: A Multicenter, Multinational Study

Background: Gastrointestinal stromal tumor (GIST) is the most common gastrointestinal mesenchymal tumor, driven by tyrosine-protein kinase KIT and pla...

clinTALL: machine learning-driven multimodal subtypeclassification and treatment outcome prediction in pediatric T-ALL

Background: Childhood T-lineage acute lymphoblastic leukemia (T-ALL) is an aggressive hematologic malignancy with poor prognosis. Differently from B-c...

A systematic assessment of machine learning for structural variant filtering

Background: Accurate discrimination of true structural variants (SVs) from artifacts in long-read sequencing data remains a critical bottleneck. Numer...

Nab-paclitaxel fused with the de novo designed receptor binder exhibits enhanced tumor targeting and therapeutic efficacy

Chemotherapy has been widely used in cancer treatment, but most of the chemotherapeutic drugs rely mainly on passive accumulation due to lack of targe...

Scalable Batch Correction for Cell Painting via Batch-Dependent Kernels and Adaptive Sampling

Cell Painting is a microscopy-based, high-content imaging assay that produces rich morphological profiles of cells and can support drug discovery by q...

A neural network model delivers a highly prognostic protein signature in cancer stem cells that identifies relapse in stage III colorectal cancer patients.

Background Stage III colorectal cancer poses a significant threat of metastasis development, as tumour resection and adjuvant chemotherapy do not guar...

Prioritizing DNA methylation biomarkers using graph neural networks and explainable AI

DNA methylation is a significant epigenetic modification involving the addition of a methyl group to the position 5' of the cytosine residues. The mod...

Analyzing Images of Blood Cells with Quantum Machine Learning Methods: Equilibrium Propagation and Variational Quantum Circuits to Detect Acute Myeloid Leukemia

This paper presents a feasibility study demonstrating that quantum machine learning (QML) algorithms achieve competitive performance on real-world med...

FoldVision: A compute-efficient atom-level 3D protein encoder

Protein function emerges from three-dimensional structure, yet many large-scale protein prediction pipelines still rely solely on linear sequence embe...

Generative modeling reveals the connection between cellular morphology and gene expression

The understanding of how transcriptional programs give rise to cellular morphology, and how morphological features reflect and influence cell identity...

Retrospective multi-cohort validation of a real-world transcriptomics-guided machine learning model for treatment response prediction in breast cancer

Selection of systemic therapy for breast cancer remains largely empirical, particularly for chemotherapy, due to the lack of robust biomarkers that pr...

Gene-exposure interactions regulate cytokine-mediated chronic inflammation and cardiac remodeling

Background: Chronic inflammation predicts adverse cardiovascular outcomes, but mechanisms linking systemic inflammation to cardiac remodeling remain i...

Peripheral blood profiles reflecting progenitor lineage balance predict treatment response in chronic myeloid leukemia

Early achievement of deep remission improves patients' outcome in chronic myeloid leukemia (CML) treatment, highlighting the need for predictive indic...

An efficient heuristic for geometric analysis of cell deformations

Sickle cell disease causes erythrocytes to become sickle-shaped, affecting their movement in the bloodstream and reducing oxygen delivery. It has a hi...

A predicted cancer dependency map for paralog pairs

Background Genome-wide CRISPR screening has enabled the development of dependency maps in hundreds of cancer cell lines, facilitating the identificati...

Radiomics-Integrated Deep Learning with Hierarchical Loss for Osteosarcoma Histology Classification

Osteosarcoma (OS) is an aggressive primary bone malignancy. Accurate histopathological assessment of viable versus non-viable tumor regions after neoa...

Machine learning driven prediction of drug efficacy in lung cancer: based on protein biomarkers and clinical features.

Currently, chemotherapy drugs are the first-line treatment for lung cancer patients, and evaluating their efficacy is of utmost significance. However,...

Deep learning predicts the effect of neoadjuvant chemotherapy for patients with triple negative breast cancer.

BACKGROUND: Triple negative breast cancer (TNBC) is an aggressive subcategory of breast cancer with poor prognosis and high risk of recurrence after t...