AIMC Topic: Machine Learning

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Prediction of Multiple sclerosis disease using machine learning classifiers: a comparative study.

Journal of preventive medicine and hygiene
INTRODUCTION: Hamedan Province is one of Iran's high-risk regions for Multiple Sclerosis (MS). Early diagnosis of MS based on an accurate system can control the disease. The aim of this study was to compare the performance of four machine learning te...

A computational method for drug sensitivity prediction of cancer cell lines based on various molecular information.

PloS one
Determining sensitive drugs for a patient is one of the most critical problems in precision medicine. Using genomic profiles of the tumor and drug information can help in tailoring the most efficient treatment for a patient. In this paper, we propose...

Probabilistic learning vector quantization on manifold of symmetric positive definite matrices.

Neural networks : the official journal of the International Neural Network Society
In this paper, we develop a new classification method for manifold-valued data in the framework of probabilistic learning vector quantization. In many classification scenarios, the data can be naturally represented by symmetric positive definite matr...

Adverse Outcomes Prediction for Congenital Heart Surgery: A Machine Learning Approach.

World journal for pediatric & congenital heart surgery
OBJECTIVE: Risk assessment tools typically used in congenital heart surgery (CHS) assume that various possible risk factors interact in a linear and additive fashion, an assumption that may not reflect reality. Using artificial intelligence technique...

Improved Discrimination of Disease States Using Proteomics Data with the Updated Aristotle Classifier.

Journal of proteome research
Mass spectrometry data sets from omics studies are an optimal information source for discriminating patients with disease and identifying biomarkers. Thousands of proteins or endogenous metabolites can be queried in each analysis, spanning several or...

Advances in Predictions of Oral Bioavailability of Candidate Drugs in Man with New Machine Learning Methodology.

Molecules (Basel, Switzerland)
Oral bioavailability (F) is an essential determinant for the systemic exposure and dosing regimens of drug candidates. F is determined by numerous processes, and computational predictions of human estimates have so far shown limited results. We descr...

From the Laboratory to the Field: IMU-Based Shot and Pass Detection in Football Training and Game Scenarios Using Deep Learning.

Sensors (Basel, Switzerland)
The applicability of sensor-based human activity recognition in sports has been repeatedly shown for laboratory settings. However, the transferability to real-world scenarios cannot be granted due to limitations on data and evaluation methods. On the...

Opportunities and challenges for artificial intelligence in clinical cardiovascular genetics.

Trends in genetics : TIG
A combination of emerging genomic and artificial intelligence (AI) techniques may ultimately unlock a deeper understanding of heterogeneity and biological complexities in cardiovascular diseases (CVDs), leading to advances in prognostic guidance and ...