Testing and Overcoming the Limitations of Modular Response Analysis

Research Background: New Challenges in Network Inference In the fields of modern molecular biology and systems biology, the precise elucidation of biomolecular networks—such as gene regulatory networks, protein interaction networks, and signaling networks—is regarded as central to understanding cellular processes, disease mechanisms, and drug actio...

A Comparison of Random Forest Variable Selection Methods for Regression Modeling of Continuous Outcomes

Background: The Importance of Variable Selection in Machine Learning Regression Models In recent years, the widespread application of machine learning in the fields of bioinformatics and data science has greatly driven the development of predictive modeling. Random forest (RF) regression, as a commonly used ensemble learning algorithm, has become a...

Exploring Diverse Approaches for Predicting Interferon-Gamma Release: Utilizing MHC Class II and Peptide Sequences

Academic Background and Research Significance In recent decades, therapeutic proteins have gained prominence as a research focus in the biopharmaceutical industry due to their huge potential in medicine. With their high targeting ability, therapeutic protein drugs are considered to offer solutions for many acute or chronic diseases (such as certain...

Regularly Updated Benchmark Sets for Statistically Correct Evaluations of AlphaFold Applications

Academic Background: Crossing into a New Era of Protein Structure Prediction Protein structure determination has long stood as one of the central challenges in molecular biology and life sciences. Traditional experimental methods such as X-ray crystallography, nuclear magnetic resonance (NMR), and cryo-electron microscopy have provided a solid foun...

Consensus Statement on the Credibility Assessment of Machine Learning Predictors

1. Background: Machine Learning in Medicine and the Challenge of Credibility In recent years, the rapid development of Artificial Intelligence (AI) and Machine Learning (ML) technologies has brought about a tremendous transformation in the field of healthcare. Particularly in in silico medicine, machine learning predictors have become vital tools f...

Chemical Space-Property Predictor Model of Perovskite Materials by High-Throughput Synthesis and Artificial Neural Networks

Chemical Space-Property Predictor Model of Perovskite Materials by High-Throughput Synthesis and Artificial Neural Networks

Academic Background Perovskite materials have attracted extensive attention due to their wide applications in solar cells and other electronic devices. Their optical properties (such as bandgap and lattice vibrations) can be flexibly modulated by tuning the chemical composition. Although the prediction of optical properties from perovskite structur...