Background Atheoretical large-scale data mining techniques using machine learning
algorithms have promise in the analysis of large epidemiological datasets. This study …

In recent years, the study of biological networks has increased dramatically. These problems
have piqued the interest of researchers in many disciplines from biology to mathematics. In …

Background Depression is commonly comorbid with many other somatic diseases and
symptoms. Identification of individuals in clusters with comorbid symptoms may reveal new …

Absence seizures are characterized by sudden loss of consciousness and interruption of
ongoing motor activities for a brief period of time lasting few to several seconds and up to …

Abstract In Machine Learning algorithms, one of the crucial issues is the representation of
the data. As the given data source become heterogeneous and the data are large-scale …

Animal models are used extensively in basic epilepsy research. In many studies, there is a
need to accurately score and quantify all epileptic spike and wave discharges (SWDs) as …

Mathematical theory of optimization has found many applications in the area of medicine
over the last few decades. Several data analysis and decision making problems in medicine …

Discrimination of different cell types is very important in many medical and biological
applications. Existing methodologies are based on cost inefficient technologies or tedious …

Neurologists typically study the brain activity through acquired biomarker signals such as
Electroencephalograms (EEGs) which have been widely used to capture the interactions …

Results: The designed prediction model was implemented on the data in this study. To do
this, the data were divided into two sets: training and testing. The prediction model was …