International Journal of Bioinformatics Research and Applications

In this paper, we propose a three-phased method for diagnosis of Alzheimer's disease using the structural magnetic resonance imaging (MRI). In first phase, gray matter tissue probability map is obtained from every brain MRI volume. Further, five regions of interest (ROIs) are extracted as per prior knowledge. In second phase, features are extracted from each ROI using 3D dual-tree discrete wavelet transform. In third phase, relevant features are selected using minimum redundancy maximum relevance features selection technique...

When a person is emotionally charged, stress could be discerned in his voice. This paper presents a simplified and a non-invasive approach to detect psycho-physiological stress by monitoring the acoustic modifications during a stressful conversation. Voice database consists of audio clips from eight different popular FM broadcasts wherein the host of the show vexes the subjects who are otherwise unaware of the charade. The audio clips are obtained from real-life stressful conversations (no simulated emotions)...

Prostate cancer is among the most common cancer in males and its heterogeneity is well known. The genomic level changes can be detected in gene expression data and those changes may serve as standard model for any random cancer data for class prediction. Various techniques were implied on prostate cancer data set in order to accurately predict cancer class including machine learning techniques. Large number of attributes but few numbers of samples in microarray data leads to poor training; therefore, the most challenging part is attribute reduction or non-significant gene reduction...

Pseudoknots are RNA tertiary structures which perform essential biological functions. This paper discusses SARNA-Predict-pk, a RNA pseudoknotted secondary structure prediction algorithm based on Simulated Annealing (SA). The research presented here extends previous work of SARNA-Predict and further examines the effect of the new algorithm to include prediction of RNA secondary structure with pseudoknots. An evaluation of the performance of SARNA-Predict-pk in terms of prediction accuracy is made via comparison with several state-of-the-art prediction algorithms using 20 individual known structures from seven RNA classes...

In this study the problem of protein fold recognition, that is a classification task, is solved via a hybrid of evolutionary algorithms namely multi-gene Genetic Programming (GP) and Genetic Algorithm (GA). Our proposed method consists of two main stages and is performed on three datasets taken from the literature. Each dataset contains different feature groups and classes. In the first step, multi-gene GP is used for producing binary classifiers based on various feature groups for each class. Then, different classifiers obtained for each class are combined via weighted voting so that the weights are determined through GA...

Hepatitis Delta Virus (HDV) is an RNA virus and causes delta hepatitis in humans. Although a lot of data is available for HDV, but retrieval of information is a complicated task. Current web database 'HDVDB' provides a comprehensive web-resource for HDV. The database is basically concerned with basic information about HDV and disease caused by this virus, genome structure, pathogenesis, epidemiology, symptoms and prevention, etc. Database also supplies sequence data and bibliographic information about HDV. A tool 'siHDV Predict' to design the effective siRNA molecule to control the activity of HDV, is also integrated in database...

Structure prediction of proteins is considered a limiting step and determining factor in drug development and in the introduction of new therapies. Since the 3D structures of proteins determine their functionalities, prediction of dihedral angles remains an open and important problem in bioinformatics, as well as a major step in discovering tertiary structures. This work presents a method that predicts values of the dihedral angles φ and ψ for enzyme loops based on data derived from amino acid sequences. The prediction of dihedral angles is implemented through a neural network based mining mechanism...

The incidence of bacterial disease has increased tremendously in the last decade, because of the emergence of drug resistance strains within the bacterial pathogens. The present study was to investigate the antibacterial compound 2,5-di-tert-butyl-1,4-benzoquinone (DTBBQ) isolated from marine Streptomyces sp. VITVSK1 as a potent antibacterial agent. The antibacterial potential of DTBBQ was investigated against RNA Polymerase (PDB ID-1I6V) by in silico molecular docking tools. Results of our study showed the high affinity interaction between DTBBQ and RNA polymerase and also confirmed the drug likeliness of DTBBQ using ADMET in silico pharmacology tools...

Genome analysis of thermophilic cyanobacterium, Thermosynechococcus elongatus BP-1 revealed factors ruling choices of codons in this organism. Multiple parameters like Nc, GC3s, RSCU, Codon Adaptation Index (CAI), optimal and rare codons, codon-pair context and amino acid usage were analysed and compositional constraint was identified as major factor. Wide range of Nc values for the same GC3 content suggested the role of translational selection. Mutational bias is suggested at synonymous position. Among optimal codons for translation, most were GC-ending...

The human gut is one of the most densely populated microbial communities in the world. The interaction of microbes with human host cells is responsible for several disease conditions and of criticality to human health. It is imperative to understand the relationships between these microbial communities within the human gut and their roles in disease. In this study we analyse the microbial communities within the human gut and their role in Inflammatory Bowel Disease (IBD). The bacterial communities were interrogated using Length Heterogeneity PCR (LH-PCR) fingerprinting of mucosal and luminal associated microbial communities for a class of healthy and diseases patients...

Investigation of essential proteins is significantly valuable for understanding of cellular life, drug design and other practical purposes. In most of current studies, essential proteins are generally mined in protein-protein interaction (PPI) networks with diverse topology features. In this study, we investigate what kind of proteins is inclined to be essential from a new perspective. The investigation implies that protein essentiality is correlated with protein domains, which are functional, structural and evolutionary units of proteins...

Removal of proteins on an essential pathway of a pathogen is expected to prohibit the pathogen from performing a vital function. To disrupt these pathways, we consider a cut set S of simple graph G, where G representing the PPI network of the pathogen. After removing S, if the difference of sizes of two partitions is high, the probability of existence of a functioning pathway is increased. We need to partition the graph into balanced partitions and approximate it with spectral bipartitioning. We consider two scenarios: in the first, we do not have any information on drug targets; in second, we consider information on drug targets...

A number of clustering methods introduced for analysis of gene expression data for extracting potential relationships among the genes are studied and reported in this paper. An effective unsupervised method (TDAC) is proposed for simultaneous detection of outliers and biologically relevant co-expressed patterns. Effectiveness of TDAC is established in comparison to its other competing algorithms over six publicly available benchmark gene expression datasets in terms of both internal and external validity measures...

Identification of protein complexes is crucial to understand principles of cellular organisation and predict protein functions. In this paper, a novel protein complex discovery algorithm IPCIPG is proposed based on the integration of Protein-Protein Interaction network (PPI network) and gene expression data. IPCIPG is a local search algorithm which has two versions: IPCIPG-n for identifying non-overlapping clusters and IPCIPG-o for detecting overlapping clusters. The experimental results on the yeast PPI network show that IPCIPG can identify protein complexes with specific biological meaning more effectively, precisely and comprehensively than six other algorithms: HUNTER, HC-PIN, CMC, SPICi, MOCDE and MCL...

Single nucleotide polymorphism studies have recently received significant amount of attention from researchers in many life science disciplines. Previous researches indicated that a series of SNPs from the same chromosome, called haplotype, contains more information than individual SNPs. Hence, discovering ways to reconstruct reliable Single Individual Haplotypes becomes one of the core issues in the whole-genome research nowadays. However, obtaining sequence from current high-throughput sequencing technologies always contain inevitable sequencing errors and/or missing information...

Helicobacter pylori are one of the most common bacterial pathogens in humans whose seropositivity increases with age and low socio-economic status. Due to presence of its pathogenic-island causes chronic persistent and atrophic gastritis in adults and children that often culminate in development of gastric and duodenal ulcers. Studies indicate that infected individuals have two to sixfold increased risk of developing gastric cancer and mucosal associated lymphoid tissue lymphoma compared to their uninfected counterparts...

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An interactome is defined as a network of protein-protein interactions built from experimentally verified interactions. Basic science as well as application-based research of potential new drugs can be promoted by including proteins that are only predicted into interactomes. The disadvantage of doing so is the risk of devaluing the definition of interactomes. By adding proteins that have only been predicted, an interactome can no longer be classified as experimentally verified and the integrity of the interactome will be endured...

DNA microarray technology can simultaneously screen thousands of gene expression profiles, transforming how genetics is applied in medicine. However, the lack of normality in microarray data renders common statistical methods ineffective. We propose a novel statistical method which does not require stringent assumptions but is still more powerful than some of its competitors. Using both simulation studies and clinical data, we show that our novel method outperforms previous methods. The limiting distribution for the proposed test is obtained for under null and alternative hypotheses...

Missing data problem degrades the statistical power of any analysis made in clinical studies. To infer valid results from such studies, suitable method is required to replace the missing values. There is no method which can be universally applicable for handling missing values and the main objective of this paper is to introduce a common method applicable in all cases of missing data. In this paper, Bayesian Genetic Algorithm (BGA) is proposed to effectively impute both missing continuous and discrete values using heuristic search algorithm called genetic algorithm and Bayesian rule...