AIMC Topic: Machine Learning

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Graph Neural Networks for Learning Molecular Excitation Spectra.

Journal of chemical theory and computation
Machine learning (ML) approaches have demonstrated the ability to predict molecular spectra at a fraction of the computational cost of traditional theoretical chemistry methods while maintaining high accuracy. Graph neural networks (GNNs) are particu...

Machine Learning Study of Metabolic Networks ChEMBL Data of Antibacterial Compounds.

Molecular pharmaceutics
Antibacterial drugs (AD) change the metabolic status of bacteria, contributing to bacterial death. However, antibiotic resistance and the emergence of multidrug-resistant bacteria increase interest in understanding metabolic network (MN) mutations an...

Simplified Machine Learning Models Can Accurately Identify High-Need High-Cost Patients With Inflammatory Bowel Disease.

Clinical and translational gastroenterology
INTRODUCTION: Hospitalization is the primary driver of inflammatory bowel disease (IBD)-related healthcare costs and morbidity. Traditional prediction models have poor performance at identifying patients at highest risk of unplanned healthcare utiliz...

OptNCMiner: a deep learning approach for the discovery of natural compounds modulating disease-specific multi-targets.

BMC bioinformatics
BACKGROUND: Due to their diverse bioactivity, natural product (NP)s have been developed as commercial products in the pharmaceutical, food and cosmetic sectors as natural compound (NC)s and in the form of extracts. Following administration, NCs typic...

An artificial intelligence-based risk prediction model of myocardial infarction.

BMC bioinformatics
BACKGROUND: Myocardial infarction can lead to malignant arrhythmia, heart failure, and sudden death. Clinical studies have shown that early identification of and timely intervention for acute MI can significantly reduce mortality. The traditional MI ...

QMugs, quantum mechanical properties of drug-like molecules.

Scientific data
Machine learning approaches in drug discovery, as well as in other areas of the chemical sciences, benefit from curated datasets of physical molecular properties. However, there currently is a lack of data collections featuring large bioactive molecu...

Machine Learning-Based Prediction Models for Different Clinical Risks in Different Hospitals: Evaluation of Live Performance.

Journal of medical Internet research
BACKGROUND: Machine learning algorithms are currently used in a wide array of clinical domains to produce models that can predict clinical risk events. Most models are developed and evaluated with retrospective data, very few are evaluated in a clini...

Breast Tumor Detection Using Robust and Efficient Machine Learning and Convolutional Neural Network Approaches.

Computational intelligence and neuroscience
Breast cancer develops when cells in the breast expand and divide uncontrollably, resulting in a lump of tissue known as a tumor. This lump of tissue is called a tumor. After skin cancer, breast cancer is the second most common cancer among women. It...

Empirical Method for Thyroid Disease Classification Using a Machine Learning Approach.

BioMed research international
There are many thyroid diseases affecting people all over the world. Many diseases affect the thyroid gland, like hypothyroidism, hyperthyroidism, and thyroid cancer. Thyroid inefficiency can cause severe symptoms in patients. Effective classificatio...