Leukemia

Large Language Model Agent for Modular Task Execution in Drug Discovery

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...

Use of machine learning techniques to predict poor survival after hematopoietic cell transplantation for myelofibrosis.

With the incorporation of effective therapies for myelofibrosis (MF), accurately predicting outcomes after allogeneic hematopoietic cell transplantation (allo-HCT) is crucial for determining the optimal timing for this procedure. Using data from 5183 patients with MF who underwent first allo-HCT between 2005 and 2020 at European Society for Blood and Marrow Transplantation centers, we examined dif...

Blood-based proteomic profiling identifies OSMR as a novel biomarker of AML outcomes.

Inflammation is increasingly recognized as a critical factor in acute myeloid leukemia (AML) pathogenesis. We performed blood-based proteomic profilin...

Quantitative and qualitative metrics of tumor stroma in predicting ovarian cancer outcomes and expansion of its study with AI-based tools.

Epithelial ovarian cancer remains one of the deadliest gynecologic malignancies, with late-stage diagnosis, high recurrence rates, and resistance to p...

Prediction of bacterial and fungal bloodstream infections using machine learning in patients undergoing chemotherapy.

PURPOSE: This study aimed to develop a machine learning (ML) model to predict bloodstream infection (BSI) in chemotherapy patients.

Exploring Non-contrastive Self-supervised Representation Learning for Image-based Profiling

Image-based cell profiling aims to create informative representations of cell images. This technique is critical in drug discovery and has greatly a...

Nanotechnology-Enhanced siRNA Delivery: Revolutionizing Cancer Therapy.

RNA interference (RNAi) has emerged as a transformative approach for cancer therapy, enabling precise gene silencing through small interfering RNA (si...

Predicting Genetic Mutations from Single-Cell Bone Marrow Images in Acute Myeloid Leukemia Using Noise-Robust Deep Learning Models

In this study, we propose a robust methodology for identification of myeloid blasts followed by prediction of genetic mutation in single-cell images...

Segment-Weighting Similarity-Based Fragment-Learning Model for Single-Cell Raman Spectral Analysis.

Raman spectroscopy provides intrinsic biochemical profiles of all cellular biomolecules in a segmented manner, promising nondestructive and label-free...

HIEGNet: A Heterogenous Graph Neural Network Including the Immune Environment in Glomeruli Classification

Graph Neural Networks (GNNs) have recently been found to excel in histopathology. However, an important histopathological task, where GNNs have not ...

Boolean-network simplification and rule fitting to unravel chemotherapy resistance in non-small cell lung cancer

Boolean networks are powerful frameworks for capturing the logic of gene-regulatory circuits, yet their combinatorial explosion hampers exhaustive a...

scDataset: Scalable Data Loading for Deep Learning on Large-Scale Single-Cell Omics

Modern single-cell datasets now comprise hundreds of millions of cells, presenting significant challenges for training deep learning models that req...

Chinese acupoint therapies in cancer pain management: research advances and future perspectives.

Cancer pain management (CPM) is crucial in oncology care, with current approaches including pharmacotherapy, radiotherapy, chemotherapy, nerve blocks,...

A Combined-Mode Machine Learning Model for Predicting Stroke Recurrence During Hospitalization in Patients with Acute Minor Ischemic Stroke.

Acute minor ischemic stroke patients often experience recurrence shortly after symptom onset, highlighting the importance of predicting stroke recurre...

Characterization of ROMK cellular heterogeneity along the mouse kidney thick ascending limb.

The renal thick ascending limb (TAL) plays a key role in water and ion homeostasis. Apical potassium secretion via the renal outer medullary potassium...

An approach to screen susceptible rats and efficacy of an antidepressive treatment after chronic stress.

Chronic stress during life has been considered a risk factor for the development of psychiatric illness in humans. Chronic unpredictable stress batter...

An enhanced harmonic densely connected hybrid transformer network architecture for chronic wound segmentation utilising multi-colour space tensor merging.

Chronic wounds and associated complications present ever growing burdens for clinics and hospitals world wide. Venous, arterial, diabetic, and pressur...

Advancing label-free cell classification with connectome-inspired explainable models and a novel LIVECell-CLS dataset.

Deep learning label-free cell imaging has become essential in modern medical applications, enabling precise cell analysis while preserving natural bio...

Explainable transformer-based deep survival analysis in childhood acute lymphoblastic leukemia.

BACKGROUND: Acute lymphoblastic leukemia (ALL) is the most common type of leukemia among children and adolescents and can be life-threatening. The inc...

Deciphering the mechanism of baicalein in cervical cancer via bioinformatics, machine learning and computational simulations: PIM1 and CDK2 are key target proteins.

Cervical cancer is one of the leading causes of death among women worldwide. Current treatments are limited by chemoresistance and chemotherapeutic ag...