Leukemia

Developing a prognostic model using machine learning for disulfidptosis related lncRNA in lung adenocarcinoma.

Disulfidptosis represents a novel cell death mechanism triggered by disulfide stress, with potential...

Application of molecular dynamics-based pharmacophore and machine learning approaches to identify novel Mcl1 inhibitors through drug repurposing and mechanics research.

Myeloid cell leukemia 1 (Mcl1), a critical protein that regulates apoptosis, has been considered as ...

Applying an explainable machine learning model might reduce the number of negative appendectomies in pediatric patients with a high probability of acute appendicitis.

The diagnosis of acute appendicitis and concurrent surgery referral is primarily based on clinical p...

Deep learning-based quantification of osteonecrosis using magnetic resonance images in Gaucher disease.

Gaucher disease is one of the most common lysosomal storage disorders. Osteonecrosis is a principal ...

Machine learning-based model for predicting outcomes in cerebral hemorrhage patients with leukemia.

BACKGROUND AND PURPOSE: Intracranial hemorrhage (ICH) in leukemia patients progresses rapidly with h...

Machine learning-derived immunosenescence index for predicting outcome and drug sensitivity in patients with skin cutaneous melanoma.

The functions of immunosenescence are closely related to skin cutaneous melanoma (SKCM). The aim of ...

Machine Learning-Enhanced Quantum Chemistry-Assisted Refinement of the Active Site Structure of Metalloproteins.

Understanding the fine structural details of inhibitor binding at the active site of metalloenzymes ...

An artificial intelligence-assisted clinical framework to facilitate diagnostics and translational discovery in hematologic neoplasia.

BACKGROUND: The increasing volume and intricacy of sequencing data, along with other clinical and di...

Deep learning nomogram for predicting neoadjuvant chemotherapy response in locally advanced gastric cancer patients.

PURPOSE: Developed and validated a deep learning radiomics nomogram using multi-phase contrast-enhan...

Exploring T-cell exhaustion features in Acute myocardial infarction for a Novel Diagnostic model and new therapeutic targets by bio-informatics and machine learning.

BACKGROUND: T-cell exhaustion (TEX), a condition characterized by impaired T-cell function, has been...

Machine learning-based integration develops an immunogenic cell death-derived lncRNA signature for predicting prognosis and immunotherapy response in lung adenocarcinoma.

Accumulating evidence demonstrates that lncRNAs are involved in the regulation of the immune microen...

Interpretable machine learning for predicting the response duration to Sintilimab plus chemotherapy in patients with advanced gastric or gastroesophageal junction cancer.

BACKGROUND: Sintilimab plus chemotherapy has proven effective as a combination immunotherapy for pat...

Hyperspectral imaging with machine learning for in vivo skin carcinoma margin assessment: a preliminary study.

Surgical excision is the most effective treatment of skin carcinomas (basal cell carcinoma or squamo...

A novel machine learning model for efficacy prediction of immunotherapy-chemotherapy in NSCLC based on CT radiomics.

Lung cancer is categorized into two main types: non-small cell lung cancer (NSCLC) and small cell lu...

Machine learning-based model to predict delirium in patients with advanced cancer treated with palliative care: a multicenter, patient-based registry cohort.

This study aimed to present a new approach to predict to delirium admitted to the acute palliative c...

A multimodal Transformer Network for protein-small molecule interactions enhances predictions of kinase inhibition and enzyme-substrate relationships.

The activities of most enzymes and drugs depend on interactions between proteins and small molecules...

Machine Learning-Based Mortality Prediction in Chronic Kidney Disease among Heart Failure Patients: Insights and Outcomes from the Jordanian Heart Failure Registry.

Heart failure (HF) is a prevalent and debilitating condition that imposes a significant burden on h...

Machine learning analysis of gene expression reveals TP53 Mutant-like AML with wild type TP53 and poor prognosis.

Algorithms for predicting COVID outcome using ready-to-use laboratorial and clinical data.

The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an emerging c...

Improving Anticancer Drug Selection and Prioritization via Neural Learning to Rank.

Personalized cancer treatment requires a thorough understanding of complex interactions between drug...

Effect of chemotherapy on prognosis in patients with primary pancreatic signet ring cell carcinoma: A large real-world study based on machine learning.

BACKGROUND: Primary pancreatic signet ring cell carcinoma (PSRCC), an extremely rare histologic vari...