Hematology

Leukemia

Latest AI and machine learning research in leukemia for healthcare professionals.

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Enhancing Drug Repositioning Through Local Interactive Learning With Bilinear Attention Networks.

Drug repositioning has emerged as a promising strategy for identifying new therapeutic applications ...

Enhancing Personalized Chemotherapy for Ovarian Cancer: Integrating Gene Expression Data with Machine Learning.

OBJECTIVE:  Ovarian cancer's complexity and heterogeneity pose significant challenges in treatment, ...

ieGENES: A machine learning method for selecting differentially expressed genes in cancer studies.

Gene selection is crucial for cancer classification using microarray data. In the interests of impro...

Proposing a short version of the Unesp-Botucatu pig acute pain scale using a novel application of machine learning technique.

Surgical castration of males is carried out on a large scale in the US swine industry and the pain r...

Predicting the efficacy of neoadjuvant chemotherapy in breast cancer patients based on ultrasound longitudinal temporal depth network fusion model.

OBJECTIVE: The aim of this study was to develop and validate a deep learning radiomics (DLR) model b...

Evaluating feature extraction in ovarian cancer cell line co-cultures using deep neural networks.

Single-cell image analysis is crucial for studying drug effects on cellular morphology and phenotypi...

Impact of Sepsis Onset Timing on All-Cause Mortality in Acute Pancreatitis: A Multicenter Retrospective Cohort Study.

BackgroundSepsis complicates acute pancreatitis (AP), increasing mortality risk. Few studies have ex...

Deep Learning Protocol for Predicting Full-Spectrum Infrared and Raman Spectra of Polypeptides and Proteins Using All-Atom Models.

Infrared (IR) spectroscopy and Raman spectroscopy are powerful tools for probing protein and peptide...

Integration of 101 machine learning algorithm combinations to unveil m6A/m1A/m5C/m7G-associated prognostic signature in colorectal cancer.

Colorectal cancer (CRC) is the most common malignancy in the digestive system, with a lower 5-year o...

CT-Based Deep Learning Predicts Prognosis in Esophageal Squamous Cell Cancer Patients Receiving Immunotherapy Combined with Chemotherapy.

RATIONALE AND OBJECTIVES: Immunotherapy combined with chemotherapy has improved outcomes for some es...

Stress hyperglycemia ratio and machine learning model for prediction of all-cause mortality in patients undergoing cardiac surgery.

BACKGROUND: The stress hyperglycemia ratio (SHR) was developed to reduce the effects of long-term ch...

A deep-learning model for predicting tyrosine kinase inhibitor response from histology in gastrointestinal stromal tumor.

Over 90% of gastrointestinal stromal tumors (GISTs) harbor mutations in KIT or PDGFRA that can predi...

Use of machine learning algorithms to construct models of symptom burden cluster risk in breast cancer patients undergoing chemotherapy.

PURPOSE: To develop models using different machine learning algorithms to predict high-risk symptom ...

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