AIMC Topic: Machine Learning

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pdCSM-PPI: Using Graph-Based Signatures to Identify Protein-Protein Interaction Inhibitors.

Journal of chemical information and modeling
Protein-protein interactions are promising sites for development of selective drugs; however, they have generally been viewed as challenging targets. Molecules targeting protein-protein interactions tend to be larger and more lipophilic than other dr...

A Feature Fusion Predictor for RNA Pseudouridine Sites with Particle Swarm Optimizer Based Feature Selection and Ensemble Learning Approach.

Current issues in molecular biology
RNA pseudouridine modification is particularly important in a variety of cellular biological and physiological processes. It plays a significant role in understanding RNA functions, RNA structure stabilization, translation processes, etc. To understa...

Study the Effect of the Risk Factors in the Estimation of the Breast Cancer Risk Score Using Machine Learning.

Asian Pacific journal of cancer prevention : APJCP
OBJECTIVE: Early prediction of breast cancer is one of the most essential fields of medicine. Many studies have introduced prediction approaches to facilitate the early prediction and estimate the future occurrence based on mammography periodic tests...

Human Activity Recognition: A Dynamic Inductive Bias Selection Perspective.

Sensors (Basel, Switzerland)
In this article, we study activity recognition in the context of sensor-rich environments. In these environments, many different constraints arise at various levels during the data generation process, such as the intrinsic characteristics of the sens...

A Machine-Learning-Based System for Prediction of Cardiovascular and Chronic Respiratory Diseases.

Journal of healthcare engineering
Cardiovascular and chronic respiratory diseases are global threats to public health and cause approximately 19 million deaths worldwide annually. This high mortality rate can be reduced with the use of technological advancements in medical science th...

Developing an Analytical Pipeline to Classify Patient Safety Event Reports Using Optimized Predictive Algorithms.

Methods of information in medicine
BACKGROUND: Patient safety event reports provide valuable insight into systemic safety issues but deriving insights from these reports requires computational tools to efficiently parse through large volumes of qualitative data. Natural language proce...

Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions.

Computational and mathematical methods in medicine
Deep learning (DL) is a branch of machine learning and artificial intelligence that has been applied to many areas in different domains such as health care and drug design. Cancer prognosis estimates the ultimate fate of a cancer subject and provides...

On learning disentangled representations for individual treatment effect estimation.

Journal of biomedical informatics
OBJECTIVE: Estimating the individualized treatment effect (ITE) from observational data is a challenging task due to selection bias, which results from the distributional discrepancy between different treatment groups caused by the dependence between...

Heterogeneous treatment effect analysis based on machine-learning methodology.

CPT: pharmacometrics & systems pharmacology
Heterogeneous treatment effect (HTE) analysis focuses on examining varying treatment effects for individuals or subgroups in a population. For example, an HTE-informed understanding can critically guide physicians to individualize the medical treatme...