AIMC Topic: Machine Learning

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Artificial Intelligence and Machine Learning in Surgery.

The American surgeon
In the present era, the technology of artificial intelligence has started to rapidly gain popularity as a revolutionary innovation in healthcare. The following article serves as the introduction to our symposium on artificial intelligence in surgery.

3D-RISM-AI: A Machine Learning Approach to Predict Protein-Ligand Binding Affinity Using 3D-RISM.

The journal of physical chemistry. B
Hydration free energy (HFE) is a key factor in improving protein-ligand binding free energy (BFE) prediction accuracy. The HFE itself can be calculated using the three-dimensional reference interaction model (3D-RISM); however, the BFE predictions so...

Model-free prediction test with application to genomics data.

Proceedings of the National Academy of Sciences of the United States of America
Testing the significance of predictors in a regression model is one of the most important topics in statistics. This problem is especially difficult without any parametric assumptions on the data. This paper aims to test the null hypothesis that give...

Trusting our machines: validating machine learning models for single-molecule transport experiments.

Chemical Society reviews
In this tutorial review, we will describe crucial aspects related to the application of machine learning to help users avoid the most common pitfalls. The examples we present will be based on data from the field of molecular electronics, specifically...

A novel speech emotion recognition method based on feature construction and ensemble learning.

PloS one
In the field of Human-Computer Interaction (HCI), speech emotion recognition technology plays an important role. Facing a small number of speech emotion data, a novel speech emotion recognition method based on feature construction and ensemble learni...

Classification model with weighted regularization to improve the reproducibility of neuroimaging signature selection.

Statistics in medicine
Machine learning (ML) has been extensively applied in brain imaging studies to aid the diagnosis of psychiatric disorders and the selection of potential biomarkers. Due to the high dimensionality of imaging data and heterogeneous subtypes of psychiat...

Graph convolutional network based virus-human protein-protein interaction prediction for novel viruses.

Computational biology and chemistry
Computational identification of human-virus protein-protein interactions (PHIs) is a worthwhile step towards understanding infection mechanisms. Analysis of the PHI networks is important for the determination of pathogenic diseases. Prediction of the...

iDRBP-ECHF: Identifying DNA- and RNA-binding proteins based on extensible cubic hybrid framework.

Computers in biology and medicine
Proteins interact with nucleic acids to regulate the life activities of organisms. Therefore, how to accurately and efficiently identify nucleic acid-binding proteins (NABPs) is particularly significant. Some sequence-based computational methods have...

Machine learning in point-of-care automated classification of oral potentially malignant and malignant disorders: a systematic review and meta-analysis.

Scientific reports
Machine learning (ML) algorithms are becoming increasingly pervasive in the domains of medical diagnostics and prognostication, afforded by complex deep learning architectures that overcome the limitations of manual feature extraction. In this system...

Identification of Human Cell Cycle Phase Markers Based on Single-Cell RNA-Seq Data by Using Machine Learning Methods.

BioMed research international
The cell cycle is composed of a series of ordered, highly regulated processes through which a cell grows and duplicates its genome and eventually divides into two daughter cells. According to the complex changes in cell structure and biosynthesis, th...