AIMC Topic: Machine Learning

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Predicting chemical ecotoxicity by learning latent space chemical representations.

Environment international
In silico prediction of chemical ecotoxicity (HC) represents an important complement to improve in vivo and in vitro toxicological assessment of manufactured chemicals. Recent application of machine learning models to predict chemical HC yields varia...

Phenotype-Based Threat Assessment.

Proceedings of the National Academy of Sciences of the United States of America
Bacterial pathogen identification, which is critical for human health, has historically relied on culturing organisms from clinical specimens. More recently, the application of machine learning (ML) to whole-genome sequences (WGSs) has facilitated pa...

Machine Learning in Injection Molding: An Industry 4.0 Method of Quality Prediction.

Sensors (Basel, Switzerland)
One of the essential requirements of injection molding is to ensure the stable quality of the parts produced. However, numerous processing conditions, which are often interrelated in quite a complex way, make this challenging. Machine learning (ML) a...

XGB-DrugPred: computational prediction of druggable proteins using eXtreme gradient boosting and optimized features set.

Scientific reports
Accurate identification of drug-targets in human body has great significance for designing novel drugs. Compared with traditional experimental methods, prediction of drug-targets via machine learning algorithms has enhanced the attention of many rese...

Prediction of low Apgar score at five minutes following labor induction intervention in vaginal deliveries: machine learning approach for imbalanced data at a tertiary hospital in North Tanzania.

BMC pregnancy and childbirth
BACKGROUND: Prediction of low Apgar score for vaginal deliveries following labor induction intervention is critical for improving neonatal health outcomes. We set out to investigate important attributes and train popular machine learning (ML) algorit...

The Future Ethics of Artificial Intelligence in Medicine: Making Sense of Collaborative Models.

Science and engineering ethics
This article examines the role of medical doctors, AI designers, and other stakeholders in making applied AI and machine learning ethically acceptable on the general premises of shared decision-making in medicine. Recent policy documents such as the ...

Multi-Task Fusion for Improving Mammography Screening Data Classification.

IEEE transactions on medical imaging
Machine learning and deep learning methods have become essential for computer-assisted prediction in medicine, with a growing number of applications also in the field of mammography. Typically these algorithms are trained for a specific task, e.g., t...

Ab-Initio Membrane Protein Amphipathic Helix Structure Prediction Using Deep Neural Networks.

IEEE/ACM transactions on computational biology and bioinformatics
Amphipathic helix (AH)features the segregation of polar and nonpolar residues and plays important roles in many membrane-associated biological processes through interacting with both the lipid and the soluble phases. Although the AH structure has bee...

CIPHER-SC: Disease-Gene Association Inference Using Graph Convolution on a Context-Aware Network With Single-Cell Data.

IEEE/ACM transactions on computational biology and bioinformatics
Inference of disease-gene associations helps unravel the pathogenesis of diseases and contributes to the treatment. Although many machine learning-based methods have been developed to predict causative genes, accurate association inference remains ch...