AIMC Topic: Machine Learning

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A Novel Hybrid Deep Learning Model for Metastatic Cancer Detection.

Computational intelligence and neuroscience
Cancer has been found as a heterogeneous disease with various subtypes and aims to destroy the body's normal cells abruptly. As a result, it is essential to detect and prognosis the distinct type of cancer since they may help cancer survivors with tr...

Multi-channel GCN ensembled machine learning model for molecular aqueous solubility prediction on a clean dataset.

Molecular diversity
This study constructed a new aqueous solubility dataset and a solubility regression model which was ensembled by GCN and machine learning models. Aqueous solubility is a key physiochemical property of small molecules in drug discovery. In the past fe...

Classification models and SAR analysis on HDAC1 inhibitors using machine learning methods.

Molecular diversity
Histone deacetylase (HDAC) 1, a member of the histone deacetylases family, plays a pivotal role in various tumors. In this study, we collected 7313 human HDAC1 inhibitors with bioactivities to form a dataset. Then, the dataset was divided into a trai...

Application of Artificial Intelligence Methodologies to Chronic Wound Care and Management: A Scoping Review.

Advances in wound care
As the number of hard-to-heal wound cases rises with the aging of the population and the spread of chronic diseases, health care professionals struggle to provide safe and effective care to all their patients simultaneously. This study aimed at prov...

A novel machine learning approach to shorten depression risk assessment for convenient uses.

Journal of affective disorders
BACKGROUND: Depression is a mental disorder affecting many people worldwide which has been exacerbated by the current pandemic. There is an urgent need for a reliable yet short scale for individuals to self-assess the risk of depression conveniently ...

DARC: Deep adaptive regularized clustering for histopathological image classification.

Medical image analysis
In recent years, deep learning as a state-of-the-art machine learning technique has made great success in histopathological image classification. However, most of deep learning approaches rely heavily on the substantial task-specific annotations, whi...

Machine Learning Models Predict Calculation Outcomes with the Transferability Necessary for Computational Catalysis.

Journal of chemical theory and computation
Virtual high-throughput screening (VHTS) and machine learning (ML) have greatly accelerated the design of single-site transition-metal catalysts. VHTS of catalysts, however, is often accompanied with a high calculation failure rate and wasted computa...

Predicting In Vivo Compound Brain Penetration Using Multi-task Graph Neural Networks.

Journal of chemical information and modeling
Assessing whether compounds penetrate the brain can become critical in drug discovery, either to prevent adverse events or to reach the biological target. Generally, pre-clinical in vivo studies measuring the ratio of brain and blood concentrations (...

A Survey on Machine Learning and Internet of Medical Things-Based Approaches for Handling COVID-19: Meta-Analysis.

Frontiers in public health
Early diagnosis, prioritization, screening, clustering, and tracking of patients with COVID-19, and production of drugs and vaccines are some of the applications that have made it necessary to use a new style of technology to involve, manage, and dea...