Proteins are studied by measuring different properties, typically the chemical structure and biochemical activity. Given that these measurements are done on the same protein molecule, they must be related. Despite the fact that this relationship exists, the mathematical nature of this relationship has remained elusive to our understanding, and is not commonly considered in the so called “struct...

Improving the performance of protein function prediction is the ultimate goal for a bioinformatician working in functional genomics. The classical prediction approach is to employ pairwise sequence alignments. However this method often faces difficulties when no statistically significant homologous sequences are identified. An alternative way is to predict protein function from sequence-derived...

One of the most important goals of bioinformatics is the ability to identify genes in uncharacterized DNA sequences. Improved promoter prediction methods can be one step towards developing more reliable ab initio gene prediction methods. In this paper, we present an empirical comparison of machine learning techniques such as Naive Bayes, Decision Trees, Support Vector Machines and Neural Networ...

Gene sequences in DNA are punctuated with regions of “junk” that are not used during expression of the gene. Identifying these regions is a complex task and many computational techniques have been devised to solve the gene finding problem. DNA can be described as a sequence over an alphabet of four letters {A, G,C, T} and so a sequence can be considered as a text written in some language. Gene ...

This study provides an effective design of search technique of a distributed bioinformatics computing system for analysis of DNA sequences using OPTSDNA algorithm. This system could be used for disease detection, criminal forensic analysis, gene prediction, genetic system and protein analysis. Different types of distributed algorithms for the search and identification for DNA segments and repea...

* 1 CUBIC, Department of Biochemistry and Molecular Biophysics, Columbia University, 650 West 168 Street BB217, New York, NY 10032, USA; 2 Columbia University Center for Computational Biology and Bioinformatics (C2B2), Russ Berrie Pavilion, 1150 St. Nicholas Avenue, New York, NY 10032, USA; 3 North East Structural Genomics Consortium (NESG), Department of Biochemistry and Molecular Biophysics, ...

Dynamic programming is a method for solving complex problems by breaking them down into simpler subproblems. This idea is very insightful for solving bioinformatics problems. Aligning distantly related protein sequences is a long-standing problem in bioinformatics and a key for successful protein structure prediction. A fast and valid algorithm can benefit the whole process of biology research....

MOTIVATION Recent studies have shown that the methods for predicting secondary structures of RNAs on the basis of posterior decoding of the base-pairing probabilities has an advantage with respect to prediction accuracy over the conventionally utilized minimum free energy methods. However, there is room for improvement in the objective functions presented in previous studies, which are maximize...

In recent years, there have been many studies focusing on improving the accuracy of prediction of transmembrane segments, and many significant results have been achieved. In spite of these considerable results, the existing methods lack the ability to explain the process of how a learning result is reached and why a prediction decision is made. The explanation of a decision made is important fo...

Predicting protein secondary structure using lattice model is one of the most studied computational problem in bioinformatics. Here secondary structure or three dimensional structure of protein is predicted from its amino acid sequence. Secondary structure refers to local sub-structures of protein. Mostly founded secondary structures are alpha helix and beta sheets. Since, it is a problem of gr...