AIMC Topic: Machine Learning

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Improving target-disease association prediction through a graph neural network with credibility information.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Identifying effective target-disease associations (TDAs) can alleviate the tremendous cost incurred by clinical failures of drug development. Although many machine learning models have been proposed to predict potential novel TDAs rapidly, their cred...

A benchmark for automatic medical consultation system: frameworks, tasks and datasets.

Bioinformatics (Oxford, England)
MOTIVATION: In recent years, interest has arisen in using machine learning to improve the efficiency of automatic medical consultation and enhance patient experience. In this article, we propose two frameworks to support automatic medical consultatio...

mOWL: Python library for machine learning with biomedical ontologies.

Bioinformatics (Oxford, England)
MOTIVATION: Ontologies contain formal and structured information about a domain and are widely used in bioinformatics for annotation and integration of data. Several methods use ontologies to provide background knowledge in machine learning tasks, wh...

AMP-BERT: Prediction of antimicrobial peptide function based on a BERT model.

Protein science : a publication of the Protein Society
Antimicrobial resistance is a growing health concern. Antimicrobial peptides (AMPs) disrupt harmful microorganisms by nonspecific mechanisms, making it difficult for microbes to develop resistance. Accordingly, they are promising alternatives to trad...

Relevance of Machine Learning to Predict the Inhibitory Activity of Small Thiazole Chemicals on Estrogen Receptor.

Current computer-aided drug design
BACKGROUND: Drug discovery requires the use of hybrid technologies for the discovery of new chemical substances. One of those interesting strategies is QSAR via applying an artificial intelligence system that effectively predicts how chemical alterat...

Differential Expression, Functional and Machine Learning Analysis of High-Throughput -Omics Data Using Open-Source Tools.

Methods in molecular biology (Clifton, N.J.)
Today, -omics analyses, including the systematic cataloging of messenger RNA and microRNA sequences or DNA methylation patterns in a cell population, organ or tissue sample, allow for an unbiased, comprehensive genome-level analysis of complex diseas...

Analyzing Antibody Repertoire Using Next-Generation Sequencing and Machine Learning.

Methods in molecular biology (Clifton, N.J.)
Advances in high-throughput sequencing technologies have enabled comprehensive sequencing of the immune repertoire. Since repertoire analysis can help to explain the relationship between the immune system and diseases, several methods have been devel...

Prediction of Cancer Treatment Using Advancements in Machine Learning.

Recent patents on anti-cancer drug discovery
Many cancer patients die due to their treatment failing because of their disease's resistance to chemotherapy and other forms of radiation therapy. Resistance may develop at any stage of therapy, even at the beginning. Several factors influence curre...

Modeling of the Acute Lymphoblastic Leukemia Detection by Convolutional Neural Networks (CNNs).

Current medical imaging
BACKGROUND: The techniques differed in many of the literature on the detection of Acute Lymphocytic Leukemia from the blood smear pictures, as the cases of infection in the world and the Kingdom of Saudi Arabia were increasing and the causes of this ...