AIMC Topic: Machine Learning

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Development of a machine learning-based target-specific scoring function for structure-based binding affinity prediction for human dihydroorotate dehydrogenase inhibitors.

Journal of computational chemistry
Human dihydroorotate dehydrogenase (hDHODH) is a flavin mononucleotide-dependent enzyme that can limit de novo pyrimidine synthesis, making it a therapeutic target for diseases such as autoimmune disorders and cancer. In this study, using the docking...

Multi-relational graph contrastive learning with learnable graph augmentation.

Neural networks : the official journal of the International Neural Network Society
Multi-relational graph learning aims to embed entities and relations in knowledge graphs into low-dimensional representations, which has been successfully applied to various multi-relationship prediction tasks, such as information retrieval, question...

Investigation of a surrogate measure-based safety index for predicting injury crashes at signalized intersections.

Traffic injury prevention
OBJECTIVES: The paper develops a machine learning-based safety index for classifying traffic conflicts that can be used to estimate the frequency of signalized intersection crashes, with a focus on the more severe ones that result in fatal and severe...

Evaluating the effectiveness of safety countermeasures at highway-railway grade crossing based on a machine learning framework.

Traffic injury prevention
OBJECTIVE: This research aims to cluster similar highway-railway grade crossings (HRGCs) to examine the safety countermeasures at HRGCs.

Task decomposition strategy based on machine learning for boosting performance and identifying mechanisms in heterogeneous activation of peracetic acid process.

Water research
Heterogeneous activation of peracetic acid (PAA) process is a promising method for removing organic pollutants from water. Nevertheless, this process is constrained by several complex factors, such as the selection of catalysts, optimization of react...

Utilizing machine learning and hemagglutinin sequences to identify likely hosts of influenza H3Nx viruses.

Preventive veterinary medicine
Influenza is a disease that represents both a public health and agricultural risk with pandemic potential. Among the subtypes of influenza A virus, H3 influenza virus can infect many avian and mammalian species and is therefore a virus of interest to...

Explainable biology for improved therapies in precision medicine: AI is not enough.

Best practice & research. Clinical rheumatology
Technological advances and high-throughput bio-chemical assays are rapidly changing ways how we formulate and test biological hypotheses, and how we treat patients. Most complex diseases arise on a background of genetics, lifestyle and environment fa...

Identification of Key Genes in Fetal Gut Development at Single-Cell Level by Exploiting Machine Learning Techniques.

Proteomics
The study of fetal gut development is critical due to its substantial influence on immediate neonatal and long-term adult health. Current research largely focuses on microbiome colonization, gut immunity, and barrier function, alongside the impact of...

Machine learning algorithms and biomarkers identification for pancreatic cancer diagnosis using multi-omics data integration.

Pathology, research and practice
PURPOSE: Pancreatic cancer is a lethal type of cancer with most of the cases being diagnosed in an advanced stage and poor prognosis. Developing new diagnostic and prognostic markers for pancreatic cancer can significantly improve early detection and...

Determination of spectroscopy marker of atherosclerotic carotid stenosis using FTIR-ATR combined with machine learning and chemometrics analyses.

Nanomedicine : nanotechnology, biology, and medicine
Atherosclerotic carotid stenosis (ACS) is a recognized risk factor for ischemic stroke. Currently, the gold diagnostic standard is Doppler ultrasound, the results of which do not provide certainty whether a given person should be qualified for surger...