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A set of solvents were classified into the switchable-hydrophilicity solvents (SHSs) and non-switchable-hydrophilicity solvents based on forming or not forming a biphasic mixture with water. SHSs have been developed to make the reaction and product separation processes easier. Herein, three classifier algorithms and various feature selection techniques relay on 3D-molecular descriptors to characterize chemicals and forecast their classes were employed. Cfs-SVM method was employed to perform a classification study. The importance of this study helps to understand more about the presence of hydrophobic groups, their position, and their shape in the molecule. © 2020 Taylor & Francis Group, LLC  

Crohn's disease and celiac disease belong to a group of autoimmune conditions that affect the digestive system, specifically the small intestine. They both attack the digestive tract and share many symptoms. Thus, the discovery of proper methods would be a major step toward differentiating celiac disease from Crohn's disease. The aim of this study was to search for the metabolic biomarkers to differentiate between these two diseases. Proton nuclear magnetic resonance spectroscopy (1H NMR) was employed as the metabolic profiling method to look for serum metabolites that differentiate between celiac disease and Crohn's disease. Classification of celiac disease and Crohn's disease was done... 

Parkinson's disease (PD) is a neurodegenerative disease, which is not easily diagnosed using clinical tests and the discovery of proper methods would be a major step towards a successful diagnosis. In the present study, we employed metabolic profiling using proton nuclear magnetic resonance spectroscopy to find metabolites in serum, which are helpful for the diagnosis of PD. Classification of PD and healthy subject was done using random forest. Serum levels of selenium measured by atomic absorption spectrometry in PD group were lower than the serum selenium levels in the control group. The metabolites causing selenium changes in PD patients were identified using random forest, and a model... 

A set of solvents were classified into the switchable-hydrophilicity solvents (SHSs) and non-switchable-hydrophilicity solvents based on forming or not forming a biphasic mixture with water. SHSs have been developed to make the reaction and product separation processes easier. Herein, three classifier algorithms and various feature selection techniques relay on 3D-molecular descriptors to characterize chemicals and forecast their classes were employed. Cfs-SVM method was employed to perform a classification study. The importance of this study helps to understand more about the presence of hydrophobic groups, their position, and their shape in the molecule. © 2020, © 2020 Taylor & Francis... 

Community Question Answering platforms such as Stack Overflow help a wide range of users solve their challenges on-line. As the popularity of these communities has grown over the years, both the number of members and posts have escalated. Also, due to the diverse backgrounds, skills, expertise, and viewpoints of users, each question may obtain more than one answer. Therefore, the focus has changed toward producing posts that have a set of answers more valuable for the community as a whole, not just one accepted-answer aimed at satisfying only the question-asker. Same as every universal community, a large number of low-quality posts on Stack Overflow require improvement. We call these posts... 

Early diagnosis of any disease at a lower cost is preferable. Automatic medical diagnosis classification tools reduce financial burden on health care systems. In medical diagnosis, patterns consist of observable symptoms and the results of diagnostic tests, which have various associated costs and risks. In this paper, we have experimented and suggested an automated pattern classification method for classifying four diseases into two classes. In the literature on machine learning or data mining, regression and classification problems are typically viewed as two distinct problems differentiated by continuous or categorical dependent variables. There are endeavors to use regression methods to... 

Background: The abundance of gene expression microarray data has led to the development of machine learning algorithms applicable for tackling disease diagnosis, disease prognosis, and treatment selection problems. However, these algorithms often produce classifiers with weaknesses in terms of accuracy, robustness, and interpretability. This paper introduces fuzzy support vector machine which is a learning algorithm based on combination of fuzzy classifiers and kernel machines for microarray classification.Results: Experimental results on public leukemia, prostate, and colon cancer datasets show that fuzzy support vector machine applied in combination with filter or wrapper feature selection...