International Journal for Computational Biology (IJCB)
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DNA Microarray Data Analysis: A New Survey on Biclustering
Get PDFThere are subsets of genes that have similar behavior under subsets of conditions, so we say that they coexpress, but behave independently under other subsets of conditions. Discovering such coexpressions can be helpful to uncover genomic knowledge such as gene networks or gene interactions. That is why, it is of utmost importance to make a simultaneous clustering of genes and conditions to identify clusters of genes that are coexpressed under clusters of conditions. This type of clustering is called biclustering.Biclustering is an NP-hard problem. Consequently, heuristic algorithms are typically used to approximate this problem by finding suboptimal solutions. In this paper, we make a new survey on biclustering of gene expression data, also called microarray data
One who shares, wins
Get PDFThere has recently been an increase in the number of open access journals showcasing the results of research, free of charge, in an affordable and easy to access online publication. In fact, it's a paradigm shift in publishing, and it has gained so much momentum and has become so favored institutionally that perhaps we can say that the one who shares, wins. We do wish to acknowledge the good that open access has achieved through journal readership, but we also want to mention here some of the problems and challenges brought about by these changes. These are issues we think authors should be aware of before submitting to open access journals
Guest Editorial: Computational Biology
Get PDFComputational biology has been used to help sequence the human genome, create accurate models of the human brain, and assist in modeling biological systems. With the availability of massive datasets, it has also become possible to study different systems in an organism in its entirety, thus aiding the sciences of systems biology and integrative biology
Determination of protein-protein interaction through Artificial Neural Network and Support Vector Machine: A Comparative study
Get PDFProtein-protein interactions (PPI) plays considerable role in most of the cellular processes and study of PPI enhances understanding of molecular mechanism of the cells. After emergence of proteomics, huge amount of protein sequences were generated but there interaction patterns are still unrevealed. Traditionally various techniques were used to predict PPI but are deficient in terms of accuracy. To overcome the limitations of experimental approaches numerous computational approaches were developed to find PPI. However previous computational approaches were based on descriptors, various external factors and protein sequences. In this article, a sequence based prediction model is proposed by using various machine learning approaches. A comparative study was done to understand efficiency of various machine learning approaches. Large amount of yeast PPI data have been analyzed. Same data has been incorporated for different classification approach like Artificial Neural Network (ANN) and Support Vector Machine (SVM), and compared their results. Existing methods with additional features were implemented to enhance the accuracy of the result. Thus it was concluded that efficiency of this model was more admirable than those existing sequence-based methods; therefore it can be effective for future proteomics research work
Molecular docking studies towards development of novel Gly-Phe analogs for potential inhibition of Cathepsin C (dipeptidyl peptidase I)
Get PDFCathepsin C is a cysteine protease required for activation of various pro-inflammatory serine proteases and, essentially, is of interest as a therapeutic target. Cathepsin C coordinate system was employed as a model to study the interaction of some already available inhibitors of Cathepsin C. Compounds containing Gly-Phe fragment with functional groups at its ends were designed by knowledge based approach. Using AutoDock and Discovery Studio Client 3.1 software packages, binding energy of different conformations and ten scoring functions (LigScore1, LigScore2, PLP1, PLP2, JAIN, PMF, PMF04, LUDI_1, LUDI_2 and LUDI_3) were calculated for newly designed compounds. These docking studies revealed favorable energy scores which also helps to understand interaction of ligands with enzyme
Screening of Bioactive Compounds against Nonreceptor Fyn Kinase: Virtual Screening and Network Approach
Get PDFTyrosine phosphorylation is a key controlling mechanism in signal transduction and enzyme activity regulation. Dysfunction of Fyn kinase, a unique member of non-receptor Src kinase family is implicated in oncogenesis, T-cell mediated diseases and neuronal disorders. Fyn kinase has been recognized as an important target for anti-cancer therapeutics. An insilico virtual screening of open and closed states of Fyn with seventy phytochemicals used in cancer treatment was carried out. Molecular properties and bioactive spectrum analysis further improved the screening process by forming a data set containing seven potential hits. Ligand efficiency score which combines biological and chemical space together identified three secondary metabolites apigenin, genistein and quercetin as efficient inhibitors of Fyn kinase. A reverse virtual screening approach validated the target selection by identifying Src kinase family members as potential drug targets. Drug-target interaction network based on feature scores of 22 phytochemicals which survived the initial screening process further validated our findings. A conceptual optimization may be required to reduce attrition and increase the activity of the lead
Evolutionary Perspective of Fungal Pathogenic Genes
Get PDFFungal pathogenesis has been vastly investigated in recent years and the phylogenic studies of fungal genome reveal that unique genes are responsible for pathogenesis. It has been found that the pathogenesis is caused by genes responsible for DNA repair, vegetative growth and sporulation. In the recent past, studies on filamentous pathogenic fungi playing an important role in establishing a pathogenic relationship with the host was well described
A Markov Model of Cell Membrane Potassium Channel and Prediction of Channel Status
Get PDFThe most cell electrophysiological models are not able to predict the channel state, which it can be very helpful in patient treatment. So in this research we intend to represent a model for predicting it based on Markov model for ion channel. To obtain the data, we used a software environment consistent with the cellular conditions. Next step is estimation the model parameters. In order to achieve this goal, there are some sub-step. First, it is essential to specify channel states. In addition, it is used a method of linearization of channel macroscopic current for states Distinction. After Distinction of different channel states, finding the stopping times in each state and calculating the model parameters by use of relations between continuous- time Markov systems is done. Then the probabilities of transfer from one state to another are calculated in terms of time and voltage. Consequently, we could find probability matrix of state changes of Markov, which made it possible to predict the channel state in different voltages. The results obviously show the dependence of model parameters to the voltage of two sides of channel. This method of modeling is able to predict channel state in each voltage. Furthermore, the predictions of channel states for 100 future states are shown. Assessment criteria for accuracy of this model, is measured by comparison between actual channel conductance that is obtained from macroscopic current of the software in different voltages and conductance that is obtained from the considered model
Computer-aided design of Organophosphorus inhibitors of Urease
Get PDFBased on the structure of the most potential inhibitor diamidophosphate, various novel groups of inhibitors were developed by knowledge-based design approach with covalent carbon-phosphorus or carbon-phosphorus-carbon bond to improve hydrolytic stability to inhibit the microbial ureases. Designed compounds were evaluated with 10 (LigScore1, LigScore2, PLP1, PLP2, JAIN, PMF, PMF04, LUDI_1, LUDI_2 and LUDI_3) different scoring functions implemented in Discovery Studio and conformation analysis by AutoDock package
A Novel DNA Sequence Compression Method Based on Chaos Game Representation
Get PDFUnique signature images derived out of Chaos Game Representation of bio-sequences is an area of research that has been confined to pattern recognition applications. In this paper we pose and answer an interesting question – can we reproduce a bio-sequence in a lossless way given the co-ordinates of the final point in its CGR image? We show that it is possible in principle, but would need enormous resolution for representation of coordinates, roughly corresponding to the information content of direct binary coding of the sequence. We go on to show that we can code nucleotide codon triplets using this method in which 16 codons can be coded using 4 bits, the remaining 48 using 6 bits. Theoretically up to 11% compression is possible with this method. However, algorithm overheads reduce this to very nominal compression percentage of less than 4% for human genome and 9% for bacterial genome. We report the results on a subset of standard test sequences and also an independent wider data set