Neurotransmitter receptor genotypes associated with mental and behavioral disorders.

Aim: Investigation of association studies within the field of mental and behavioral disorders is of value given their complex molecular etiology including epistatic interactions of multiple genes with small effects.

Materials & Methods: Utilizing biomedical text mining, associations are uncovered for all mental and behavioral conditions listed in Diagnostic and Statistical Manual of Mental Disorders Text Revision. Specifically, a computational pipeline is designed to retrieve neurotransmitter receptor variations from biomedical literature with a text mining approach, where unique polymorphisms are also mined.

Improving Biochemical Named Entity Recognition Using PSO Classifier Selection and Bayesian Combination Methods.

Named Entity Recognition (NER) is a basic step for large number of consequent text mining tasks in the biochemical domain. Increasing the performance of such recognition systems is of high importance and always poses a challenge. In this study, a new community based decision making system is proposed which aims at increasing the efficiency of NER systems in the chemical/drug name context.

ChemTok: A New Rule Based Tokenizer for Chemical Named Entity Recognition.

Named Entity Recognition (NER) from text constitutes the first step in many text mining applications. The most important preliminary step for NER systems using machine learning approaches is tokenization where raw text is segmented into tokens. This study proposes an enhanced rule based tokenizer, ChemTok, which utilizes rules extracted mainly from the train data set.