AIMC Topic: Machine Learning

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Sex-related patterns in the electroencephalogram and their relevance in machine learning classifiers.

Human brain mapping
Deep learning is increasingly being proposed for detecting neurological and psychiatric diseases from electroencephalogram (EEG) data but the method is prone to inadvertently incorporate biases from training data and exploit illegitimate patterns. Th...

Toward safer ophthalmic artificial intelligence via distributed validation on real-world data.

Current opinion in ophthalmology
PURPOSE OF REVIEW: The current article provides an overview of the present approaches to algorithm validation, which are variable and largely self-determined, as well as solutions to address inadequacies.

Improving Compound-Protein Interaction Prediction by Self-Training with Augmenting Negative Samples.

Journal of chemical information and modeling
Identifying compound-protein interactions (CPIs) is crucial for drug discovery. Since experimentally validating CPIs is often time-consuming and costly, computational approaches are expected to facilitate the process. Rapid growths of available CPI d...

Individual health-disease phase diagrams for disease prevention based on machine learning.

Journal of biomedical informatics
Early disease detection and prevention methods based on effective interventions are gaining attention worldwide. Progress in precision medicine has revealed that substantial heterogeneity exists in health data at the individual level and that complex...

Elucidating the Role of Hydrogen Bonding in the Optical Spectroscopy of the Solvated Green Fluorescent Protein Chromophore: Using Machine Learning to Establish the Importance of High-Level Electronic Structure.

The journal of physical chemistry letters
Hydrogen bonding interactions with chromophores in chemical and biological environments play a key role in determining their electronic absorption and relaxation processes, which are manifested in their linear and multidimensional optical spectra. Fo...

Machine learning applications for early detection of esophageal cancer: a systematic review.

BMC medical informatics and decision making
INTRODUCTION: Esophageal cancer (EC) is a significant global health problem, with an estimated 7th highest incidence and 6th highest mortality rate. Timely diagnosis and treatment are critical for improving patients' outcomes, as over 40% of patients...

Quantum recurrent neural networks for sequential learning.

Neural networks : the official journal of the International Neural Network Society
Quantum neural network (QNN) is one of the promising directions where the near-term noisy intermediate-scale quantum (NISQ) devices could find advantageous applications against classical resources. Recurrent neural networks are the most fundamental n...

Efficient machine learning of solute segregation energy based on physics-informed features.

Scientific reports
Machine learning models solute segregation energy based on appropriate features of segregation sites. Lumping many features together can give a decent accuracy but may suffer the curse of dimensionality. Here, we modeled the segregation energy with e...

Machine learning-based guilt detection in text.

Scientific reports
We introduce a novel Natural Language Processing (NLP) task called guilt detection, which focuses on detecting guilt in text. We identify guilt as a complex and vital emotion that has not been previously studied in NLP, and we aim to provide a more f...