Applied Bioinformatics

Comparing various attributes of prolactin hormones in different species: application of bioinformatics tools

Prolactin is mainly secreted by the anterior pituitary and is able to stimulate mammary gland development and lactation in mammalians. Although prolactins share a common ancestral gene encoding, they show species specific characteristics and their efficiency may be different in various mammals. The importance of protein structures of all sequences of this hormone have been studied by various bi...

Novel Applications of Immuno-bioinformatics in Vaccine and Bio-product Developments at Research Institutes

There are many challenges in the field of public health sciences. Rational decisions are required in order to treat different diseases, gain knowledge and wealth regarding research, and produce biological or synthetic products. Various advances in the basic laboratory science, computer science, and the engineering of biological production processes can help solve the occurring problems. Bioinfo...

Finding Exact and Solo LTR-Retrotransposons in Biological Sequences Using SVM

Finding repetitive subsequences in genome is a challengeable problem in bioinformatics research area. A lot of approaches have been proposed to solve the problem, which could be divided to library base and de novo methods. The library base methods use predetermined repetitive genome’s subsequences, where library-less methods attempt to discover repetitive subsequences by analytical approach...

gpALIGNER: A Fast Algorithm for Global Pairwise Alignment of DNA Sequences

Bioinformatics, through the sequencing of the full genomes for many species, is increasingly relying on efficient global alignment tools exhibiting both high sensitivity and specificity. Many computational algorithms have been applied for solving the sequence alignment problem. Dynamic programming, statistical methods, approximation and heuristic algorithms are the most common methods appli...

Bioinformatics protocols for analysis of functional genomics data applied to neuropathy microarray datasets

Sequential and Mixed Genetic Algorithm and Learning Automata (SGALA, MGALA) for Feature Selection in QSAR

Feature selection is of great importance in Quantitative Structure-Activity Relationship (QSAR) analysis. This problem has been solved using some meta-heuristic algorithms such as: GA, PSO, ACO, SA and so on. In this work two novel hybrid meta-heuristic algorithms i.e. Sequential GA and LA (SGALA) and Mixed GA and LA (MGALA), which are based on Genetic algorithm and learning automata for QSAR f...