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Comparison of Walking Protocols and Gait Assessment Systems for Machine Learning-Based Classification of Parkinson's Disease.

Sensors (Basel, Switzerland)
Early diagnosis of Parkinson's diseases (PD) is challenging; applying machine learning (ML) models to gait characteristics may support the classification process. Comparing performance of ML models used in various studies can be problematic due to di...

Machine Learning to Detect Alzheimer's Disease from Circulating Non-coding RNAs.

Genomics, proteomics & bioinformatics
Blood-borne small non-coding (sncRNAs) are among the prominent candidates for blood-based diagnostic tests. Often, high-throughput approaches are applied to discover biomarker signatures. These have to be validated in larger cohorts and evaluated by ...

Computer-Aided Diagnosis of Multiple Sclerosis Using a Support Vector Machine and Optical Coherence Tomography Features.

Sensors (Basel, Switzerland)
The purpose of this paper is to evaluate the feasibility of diagnosing multiple sclerosis (MS) using optical coherence tomography (OCT) data and a support vector machine (SVM) as an automatic classifier. Forty-eight MS patients without symptoms of op...

Multi-view ensemble learning with empirical kernel for heart failure mortality prediction.

International journal for numerical methods in biomedical engineering
Heart failure (HF) refers to the heart's inability to pump sufficient blood to maintain the body's needs, which has a very serious impact on human health. In recent years, the prevalence of HF has remained high. This paper proposes a multi-view ensem...

Multimodal deep representation learning for protein interaction identification and protein family classification.

BMC bioinformatics
BACKGROUND: Protein-protein interactions(PPIs) engage in dynamic pathological and biological procedures constantly in our life. Thus, it is crucial to comprehend the PPIs thoroughly such that we are able to illuminate the disease occurrence, achieve ...

Usefulness of presepsin as diagnostic and prognostic marker of sepsis in daily clinical practice.

Journal of infection in developing countries
INTRODUCTION: Sepsis represents a major cause of morbidity and mortality in critically ill patients. Early diagnosis and appropriate treatment have a crucial influence on survival. The aim of this study was to evaluate the diagnostic and prognostic r...

Prediction of lithium response using clinical data.

Acta psychiatrica Scandinavica
OBJECTIVE: Promptly establishing maintenance therapy could reduce morbidity and mortality in patients with bipolar disorder. Using a machine learning approach, we sought to evaluate whether lithium responsiveness (LR) is predictable using clinical ma...

Triaging ophthalmology outpatient referrals with machine learning: A pilot study.

Clinical & experimental ophthalmology
IMPORTANCE: Triaging of outpatient referrals to ophthalmology services is required for the maintenance of patient care and appropriate resource allocation. Machine learning (ML), in particular natural language processing, may be able to assist with t...

The assessment of efficient representation of drug features using deep learning for drug repositioning.

BMC bioinformatics
BACKGROUND: De novo drug discovery is a time-consuming and expensive process. Nowadays, drug repositioning is utilized as a common strategy to discover a new drug indication for existing drugs. This strategy is mostly used in cases with a limited num...