The automatic induction of machine learning models capable of addressing supervised
learning, feature selection, clustering and reinforcement learning problems requires …

High throughput biological data need to be processed, analyzed, and interpreted to address
problems in life sciences. Bioinformatics, computational biology, and systems biology deal …

Knowledge gained through classification of microarray gene expression data is increasingly
important as they are useful for phenotype classification of diseases. Different from black box …

Naïve–Bayes Classifier (NBC) is widely used for classification in machine learning. It is
considered as the first choice for many classification problems because of its simplicity and …

Properly designing a wavelet neural network (WNN) is crucial for achieving the optimal
generalization performance. In this paper, two different approaches were proposed for …

In this article, performance of gene expression data is analyzed using statistical method
resembling Mann–Whitney U test. Biological field investigation of gene expression data is …

In this paper we show that frequent closed itemset mining and biclustering, the two most
prominent application fields in pattern discovery, can be reduced to the same problem when …

A microarray image is used as an accurate method for diagnosis of cancerous diseases. The
aim of this research is to provide an approach for detection of breast cancer type. First, raw …

Although we have seen increased spending on drug research and development, the
submission of new drug applications to the US Food and Drug Administration (FDA) has …