AIMC Topic: Algorithms

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SGL-SVM: A novel method for tumor classification via support vector machine with sparse group Lasso.

Journal of theoretical biology
At present, with the in-depth study of gene expression data, the significant role of tumor classification in clinical medicine has become more apparent. In particular, the sparse characteristics of gene expression data within and between groups. Ther...

SemBioNLQA: A semantic biomedical question answering system for retrieving exact and ideal answers to natural language questions.

Artificial intelligence in medicine
BACKGROUND AND OBJECTIVE: Question answering (QA), the identification of short accurate answers to users questions written in natural language expressions, is a longstanding issue widely studied over the last decades in the open-domain. However, it s...

Prediction of the Health Effects of Food Peptides and Elucidation of the Mode-of-action Using Multi-task Graph Convolutional Neural Network.

Molecular informatics
Food proteins work not only as nutrients but also modulators for the physiological functions of the human body. The physiological functions of food proteins are basically regulated by peptides encrypted in food protein sequences (food peptides). In t...

[Predicting atrial fibrillation through a sinus-rhythm electrocardiogram; useful or not?].

Nederlands tijdschrift voor geneeskunde
In patients with cryptogenic stroke, the detection of atrial fibrillation (AF) is important, since it is an indication for the prescription of oral anticoagulation, instead of anti-platelet therapy, to decrease the chance of a recurrent ischaemic cer...

Estimating Biomechanical Time-Series with Wearable Sensors: A Systematic Review of Machine Learning Techniques.

Sensors (Basel, Switzerland)
Wearable sensors have the potential to enable comprehensive patient characterization and optimized clinical intervention. Critical to realizing this vision is accurate estimation of biomechanical time-series in daily-life, including joint, segment, a...

Machine learning with sparse nutrition data to improve cardiovascular mortality risk prediction in the USA using nationally randomly sampled data.

BMJ open
OBJECTIVES: We aimed to test whether or not adding (1) nutrition predictor variables and/or (2) using machine learning models improves cardiovascular death prediction versus standard Cox models without nutrition predictor variables.

Dissection of gene expression datasets into clinically relevant interaction signatures via high-dimensional correlation maximization.

Nature communications
Gene expression is controlled by many simultaneous interactions, frequently measured collectively in biology and medicine by high-throughput technologies. It is a highly challenging task to infer from these data the generating effects and cooperating...

Deep learning to detect Alzheimer's disease from neuroimaging: A systematic literature review.

Computer methods and programs in biomedicine
Alzheimer's Disease (AD) is one of the leading causes of death in developed countries. From a research point of view, impressive results have been reported using computer-aided algorithms, but clinically no practical diagnostic method is available. I...

An interpretation algorithm for molecular diagnosis of bacterial vaginosis in a maternity hospital using machine learning: proof-of-concept study.

Diagnostic microbiology and infectious disease
Allplex Bacterial vaginosis assay (Seegene, South Korea) is a molecular test for bacterial vaginosis (BV). A machine learning algorithm was devised on 200 samples (BV = 23, non-BV = 177) converting 7 identified bacterial strains polymerase chain reac...

Fully-automated deep learning-powered system for DCE-MRI analysis of brain tumors.

Artificial intelligence in medicine
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays an important role in diagnosis and grading of brain tumors. Although manual DCE biomarker extraction algorithms boost the diagnostic yield of DCE-MRI by providing quantitative infor...