Identifying potential drug candidates with deep learning virtual screening

The earliest stage of drug discovery is governed by a simple constraint: there are far more possible drug-like molecules than ...
Technology Networks

Machine Learning Applications in Proteomics

Machine learning enhances proteomics by optimizing peptide identification, structure prediction, and biomarker discovery.
Devdiscourse

Smartphone data and AI reveal true climate toll of urban mobility

The future of sustainable transport planning may already be sitting in people’s pockets. By transforming everyday smartphone signals into high-resolution mobility data, researchers have reconstructed ...
GitHub

Fuzzy Equivalence–Driven Feature Selection with Constrained Sigmoid Similarity for Interpretable Supervised Learning

The code includes the core methodology, experimental results generation, and the script used to produce Table 4 from the manuscript. The framework is designed to be label-agnostic during similarity ...
Scientific Research Publishing

An Explainable Wavelet-Based Feature Decomposition and Machine Learning Framework for Land Cover Classification ()

1 Department of Geomatic Engineering, University of Mines and Technology, Tarkwa, Ghana. 2 Department of Geomatic and Civil Engineering, University of Mines and Technology, Essikado, Ghana. Accurate ...
IEEE

Robust Semi-supervised Feature Selection with Multi-granularity Zentropy Modeling

Abstract: High-dimensional and weakly supervised (HiDWS) data present significant challenges for traditional machine learning and pattern recognition. Although semi-supervised feature selection has ...
Frontiers

Machine learning–guided feature selection and predictive model construction for attention-deficit/hyperactivity disorder

A total of 8,598 children were enrolled and classified into three groups: ADHD (n=3,678), subthreshold ADHD (s-ADHD) (n=1,495), and healthy controls (HC) (n=3,425). Data collection covered 40 ...
IEEE

Unsupervised Feature Selection-Driven Active Learning for Semi-Supervised Automatic ECG Analysis

Abstract: Automatic analysis methods of electrocardiograms (ECGs) usually required large-scale annotated training data, but the annotation process is extremely time-consuming. While semi-supervised ...
INSPIRE

Dynamic graph-based quantum feature selection for accurate fetal plane classification in ultrasound imaging

Accurate classification of fetal biometric planes in ultrasound imaging is more important for effective prenatal screening and early diagnosis of fetal abnormalities. To enhance the diagnostic ...
Frontiers

Advanced Supervised Machine Learning Techniques for Accurate Prediction of Diabetes Mellitus Using Feature Selection

The final, formatted version of the article will be published soon. Background): Diabetes Mellitus (DM) is a chronic metabolic disorder that poses a significant global health challenge, affecting ...