AIMC Topic: Machine Learning

Clear Filters Showing 29851 to 29860 of 34417 articles

MolMVC: Enhancing molecular representations for drug-related tasks through multi-view contrastive learning.

Bioinformatics (Oxford, England)
MOTIVATION: Effective molecular representation is critical in drug development. The complex nature of molecules demands comprehensive multi-view representations, considering 1D, 2D, and 3D aspects, to capture diverse perspectives. Obtaining represent...

Scalable CNN-based classification of selective sweeps using derived allele frequencies.

Bioinformatics (Oxford, England)
MOTIVATION: Selective sweeps can successfully be distinguished from neutral genetic data using summary statistics and likelihood-based methods that analyze single nucleotide polymorphisms (SNPs). However, these methods are sensitive to confounding fa...

HCS-hierarchical algorithm for simulation of omics datasets.

Bioinformatics (Oxford, England)
MOTIVATION: Analysis of the omics data with the help of machine learning (ML) methods is limited by small sample sizes and a large number of variables. One possible approach to deal with such data is using algorithms for feature selection and reducin...

Automated Interstitial Lung Abnormality Probability Prediction at CT: A Stepwise Machine Learning Approach in the Boston Lung Cancer Study.

Radiology
Background It is increasingly recognized that interstitial lung abnormalities (ILAs) detected at CT have potential clinical implications, but automated identification of ILAs has not yet been fully established. Purpose To develop and test automated I...

Explainable machine learning prediction of edema adverse events in patients treated with tepotinib.

Clinical and translational science
Tepotinib is approved for the treatment of patients with non-small-cell lung cancer harboring MET exon 14 skipping alterations. While edema is the most prevalent adverse event (AE) and a known class effect of MET inhibitors including tepotinib, there...

Explainable Artificial Intelligence for Early Prediction of Pressure Injury Risk.

American journal of critical care : an official publication, American Association of Critical-Care Nurses
BACKGROUND: Hospital-acquired pressure injuries (HAPIs) have a major impact on patient outcomes in intensive care units (ICUs). Effective prevention relies on early and accurate risk assessment. Traditional risk-assessment tools, such as the Braden S...

From organs to algorithms: Redefining cancer classification in the age of artificial intelligence.

Clinical and translational science
Traditional cancer classification based on organ of origin and histology is increasingly at odds with precision oncology. Tumors in different organs can share molecular features, while those in the same organ can be heterogeneous. This disconnect imp...

Recent advances in artificial intelligent strategies for tissue engineering and regenerative medicine.

Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI)
BACKGROUND: Tissue engineering and regenerative medicine (TERM) aim to repair or replace damaged or lost tissues or organs due to accidents, diseases, or aging, by applying different sciences. For this purpose, an essential part of TERM is the design...

Leveraging SEER data through machine learning to predict distant lymph node metastasis and prognosticate outcomes in hepatocellular carcinoma patients.

The journal of gene medicine
OBJECTIVES: This study aims to develop and validate machine learning-based diagnostic and prognostic models to predict the risk of distant lymph node metastases (DLNM) in patients with hepatocellular carcinoma (HCC) and to evaluate the prognosis for ...