AIMC Topic: Machine Learning

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ProtEC: A Transformer Based Deep Learning System for Accurate Annotation of Enzyme Commission Numbers.

IEEE/ACM transactions on computational biology and bioinformatics
The advancements in next-generation sequencing technologies have given rise to large-scale, open-source protein databases consisting of hundreds of millions of sequences. However, to make these sequences useful in biomedical applications, they need t...

DRLM: A Robust Drug Representation Learning Method and its Applications.

IEEE/ACM transactions on computational biology and bioinformatics
Learning representations from data is a fundamental step for machine learning. High-quality and robust drug representations can broaden the understanding of pharmacology, and improve the modeling of multiple drug-related prediction tasks, which furth...

A machine learning-based universal outbreak risk prediction tool.

Computers in biology and medicine
In order to prevent and control the increasing number of serious epidemics, the ability to predict the risk caused by emerging outbreaks is essential. However, most current risk prediction tools, except EPIRISK, are limited by being designed for targ...

Machine learning prediction models for diabetic kidney disease: systematic review and meta-analysis.

Endocrine
BACKGROUND: Machine learning is increasingly recognized as a viable approach for identifying risk factors associated with diabetic kidney disease (DKD). However, the current state of real-world research lacks a comprehensive systematic analysis of th...

DeepGraFT: A novel semantic segmentation auxiliary ROI-based deep learning framework for effective fundus tessellation classification.

Computers in biology and medicine
Fundus tessellation (FT) is a prevalent clinical feature associated with myopia and has implications in the development of myopic maculopathy, which causes irreversible visual impairment. Accurate classification of FT in color fundus photo can help p...

Harnessing Small-Molecule Analyte Detection in Complex Media: Combining Molecularly Imprinted Polymers, Electrolytic Transistors, and Machine Learning.

ACS applied materials & interfaces
Small-molecule analyte detection is key for improving quality of life, particularly in health monitoring through the early detection of diseases. However, detecting specific markers in complex multicomponent media using devices compatible with point-...

Suitability of machine learning for atrophy and fibrosis development in neovascular age-related macular degeneration.

Acta ophthalmologica
PURPOSE: To assess the suitability of machine learning (ML) techniques in predicting the development of fibrosis and atrophy in patients with neovascular age-related macular degeneration (nAMD), receiving anti-VEGF treatment over a 36-month period.

A Machine Learning Analysis of Big Metabolomics Data for Classifying Depression: Model Development and Validation.

Biological psychiatry
BACKGROUND: Many metabolomics studies of depression have been performed, but these have been limited by their scale. A comprehensive in silico analysis of global metabolite levels in large populations could provide robust insights into the pathologic...

Tailored Intraoperative MRI Strategies in High-Grade Glioma Surgery: A Machine Learning-Based Radiomics Model Highlights Selective Benefits.

Operative neurosurgery (Hagerstown, Md.)
BACKGROUND AND OBJECTIVES: In high-grade glioma (HGG) surgery, intraoperative MRI (iMRI) has traditionally been the gold standard for maximizing tumor resection and improving patient outcomes. However, recent Level 1 evidence juxtaposes the efficacy ...

AIDDISON: Empowering Drug Discovery with AI/ML and CADD Tools in a Secure, Web-Based SaaS Platform.

Journal of chemical information and modeling
The widespread proliferation of artificial intelligence (AI) and machine learning (ML) methods has a profound effect on the drug discovery process. However, many scientists are reluctant to utilize these powerful tools due to the steep learning curve...