AIMC Topic: Machine Learning

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SProtFP: a machine learning-based method for functional classification of small ORFs in prokaryotes.

NAR genomics and bioinformatics
Small proteins (≤100 amino acids) play important roles across all life forms, ranging from unicellular bacteria to higher organisms. In this study, we have developed SProtFP which is a machine learning-based method for functional annotation of prokar...

Prediction of mortality in intensive care unit with short-term heart rate variability: Machine learning-based analysis of the MIMIC-III database.

Computers in biology and medicine
BACKGROUND: Prognosis prediction in the intensive care unit (ICU) traditionally relied on physiological scoring systems based on clinical indicators at admission. Electrocardiogram (ECG) provides easily accessible information, with heart rate variabi...

Enhancing door-to-door waste collection forecasting through ML.

Waste management (New York, N.Y.)
We explore the application of machine learning (ML) techniques to forecast door-to-door waste collection, addressing the challenges in municipal solid waste (MSW) management. ML models offer a promising solution to optimize waste collection operation...

MAGPIE: A Machine Learning Approach to Decipher Protein-Protein Interactions in Human Plasma.

Journal of proteome research
Immunoprecipitation coupled to tandem mass spectrometry (IP-MS/MS) methods are often used to identify protein-protein interactions (PPIs). While these approaches are prone to false positive identifications through contamination and antibody nonspecif...

A Plasma Proteomics-Based Model for Identifying the Risk of Postpartum Depression Using Machine Learning.

Journal of proteome research
Postpartum depression (PPD) poses significant risks to maternal and infant health, yet proteomic analyses of PPD-risk women remain limited. This study analyzed plasma samples from 30 healthy postpartum women and 30 PPD-risk women using mass spectrome...

Computational approaches for decoding structure-saltiness enhancement and aroma perception mechanisms of odorants: From machine learning to molecular simulation.

Food research international (Ottawa, Ont.)
The unclear relationship between structure and saltiness enhancement limits the development and application of savory odorants. The structure characteristic-saltiness enhancement perception (SEP) mechanisms of savory odorants were investigated by mac...

EEG microstate analysis and machine learning classification in patients with obsessive-compulsive disorder.

Journal of psychiatric research
BACKGROUND: Microstate characterization of electroencephalogram (EEG) is a data-driven approach to explore the functional changes and interrelationships of multiple brain networks on a millisecond scale. This study aimed to explore the pathological c...

MO-GCN: A multi-omics graph convolutional network for discriminative analysis of schizophrenia.

Brain research bulletin
The methodology of machine learning with multi-omics data has been widely adopted in the discriminative analyses of schizophrenia, but most of these studies ignored the cooperative interactions and topological attributes of multi-omics networks. In t...

A qualitative, quantitative and dosimetric evaluation of a machine learning-based automatic segmentation method in treatment planning for gastric cancer.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: To investigate the performance of a machine learning-based segmentation method for treatment planning of gastric cancer.

Liquid-Metal-Based Multichannel Strain Sensor for Sign Language Gesture Classification Using Machine Learning.

ACS applied materials & interfaces
Liquid metals are highly conductive like metallic materials and have excellent deformability due to their liquid state, making them rather promising for flexible and stretchable wearable sensors. However, patterning liquid metals on soft substrates h...