Introduction: The early diagnosis of breast cancer as prevalent cancer among women, is a necessity in the research on cancers since it could simplify the clinical management of other patients. The importance of the classification of breast cancer patients into high- or low-risk groups has led research groups in the biomedical and informatics departments to evaluate and use computer techniques s...

OBJECTIVES This paper aims to predict childhood obesity after age two, using only data collected prior to the second birthday by a clinical decision support system called CHICA. METHODS Analyses of six different machine learning methods: RandomTree, RandomForest, J48, ID3, Naïve Bayes, and Bayes trained on CHICA data show that an accurate, sensitive model can be created. RESULTS Of the meth...

BACKGROUND The paper presents a thorough analysis of the influence of the number of negative training examples on the performance of machine learning methods. RESULTS The impact of this rather neglected aspect of machine learning methods application was examined for sets containing a fixed number of positive and a varying number of negative examples randomly selected from the ZINC database. A...

In view of the essential role played by dosRS in the survival of Mycobacterium in the infected granuloma cells, dosRS transcriptional regulatory proteins were considered as a validated target for high throughput screening (HTS). However, the cost and time factor involved in screening large compound libraries are an important hurdle in identifying lead compounds. Therefore, the use of computatio...