Displaying publications 81 - 100 of 119 in total

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  1. A context evaluation approach for structural comparison of proteins using cross entropy over n-gram modelling
    Razmara J, Deris SB, Parvizpour S
    Comput Biol Med, 2013 Oct;43(10):1614-21.
    PMID: 24034753 DOI: 10.1016/j.compbiomed.2013.07.022
    The structural comparison of proteins is a vital step in structural biology that is used to predict and analyse a new unknown protein function. Although a number of different techniques have been explored, the study to develop new alternative methods is still an active research area. The present paper introduces a text modelling-based technique for the structural comparison of proteins. The method models the secondary and tertiary structure of proteins in two linear sequences and then applies them to the comparison of two structures. The technique used for pairwise comparison of the sequences has been adopted from computational linguistics and its well-known techniques for analysing and quantifying textual sequences. To this end, an n-gram modelling technique is used to capture regularities between sequences, and then, the cross-entropy concept is employed to measure their similarities. Several experiments are conducted to evaluate the performance of the method and compare it with other commonly used programs. The assessments for information retrieval evaluation demonstrate that the technique has a high running speed, which is similar to other linear encoding methods, such as 3D-BLAST, SARST, and TS-AMIR, whereas its accuracy is comparable to CE and TM-align, which are high accuracy comparison tools. Accordingly, the results demonstrate that the algorithm has high efficiency compared with other state-of-the-art methods.
    Matched MeSH terms: Computational Biology/methods*
  2. Utilizing shared interacting domain patterns and Gene Ontology information to improve protein-protein interaction prediction
    Roslan R, Othman RM, Shah ZA, Kasim S, Asmuni H, Taliba J, et al.
    Comput Biol Med, 2010 Jun;40(6):555-64.
    PMID: 20417930 DOI: 10.1016/j.compbiomed.2010.03.009
    Protein-protein interactions (PPIs) play a significant role in many crucial cellular operations such as metabolism, signaling and regulations. The computational methods for predicting PPIs have shown tremendous growth in recent years, but problem such as huge false positive rates has contributed to the lack of solid PPI information. We aimed at enhancing the overlap between computational predictions and experimental results in an effort to partially remove PPIs falsely predicted. The use of protein function predictor named PFP() that are based on shared interacting domain patterns is introduced in this study with the purpose of aiding the Gene Ontology Annotations (GOA). We used GOA and PFP() as agents in a filtering process to reduce false positive pairs in the computationally predicted PPI datasets. The functions predicted by PFP() were extracted from cross-species PPI data in order to assign novel functional annotations for the uncharacterized proteins and also as additional functions for those that are already characterized by the GO (Gene Ontology). The implementation of PFP() managed to increase the chances of finding matching function annotation for the first rule in the filtration process as much as 20%. To assess the capability of the proposed framework in filtering false PPIs, we applied it on the available S. cerevisiae PPIs and measured the performance in two aspects, the improvement made indicated as Signal-to-Noise Ratio (SNR) and the strength of improvement, respectively. The proposed filtering framework significantly achieved better performance than without it in both metrics.
    Matched MeSH terms: Computational Biology/methods*
  3. Identification of informative genes and pathways using an improved penalized support vector machine with a weighting scheme
    Chan WH, Mohamad MS, Deris S, Zaki N, Kasim S, Omatu S, et al.
    Comput Biol Med, 2016 10 01;77:102-15.
    PMID: 27522238 DOI: 10.1016/j.compbiomed.2016.08.004
    Incorporation of pathway knowledge into microarray analysis has brought better biological interpretation of the analysis outcome. However, most pathway data are manually curated without specific biological context. Non-informative genes could be included when the pathway data is used for analysis of context specific data like cancer microarray data. Therefore, efficient identification of informative genes is inevitable. Embedded methods like penalized classifiers have been used for microarray analysis due to their embedded gene selection. This paper proposes an improved penalized support vector machine with absolute t-test weighting scheme to identify informative genes and pathways. Experiments are done on four microarray data sets. The results are compared with previous methods using 10-fold cross validation in terms of accuracy, sensitivity, specificity and F-score. Our method shows consistent improvement over the previous methods and biological validation has been done to elucidate the relation of the selected genes and pathway with the phenotype under study.
    Matched MeSH terms: Computational Biology/methods*
  4. Immunoinformatics-driven In silico vaccine design for Nipah virus (NPV): Integrating machine learning and computational epitope prediction
    Shahab M, Iqbal MW, Ahmad A, Alshabrmi FM, Wei DQ, Khan A, et al.
    Comput Biol Med, 2024 Mar;170:108056.
    PMID: 38301512 DOI: 10.1016/j.compbiomed.2024.108056
    The Nipah virus (NPV) is a highly lethal virus, known for its significant fatality rate. The virus initially originated in Malaysia in 1998 and later led to outbreaks in nearby countries such as Bangladesh, Singapore, and India. Currently, there are no specific vaccines available for this virus. The current work employed the reverse vaccinology method to conduct a comprehensive analysis of the entire proteome of the NPV virus. The aim was to identify and choose the most promising antigenic proteins that could serve as potential candidates for vaccine development. We have also designed B and T cell epitopes-based vaccine candidate using immunoinformatics approach. We have identified a total of 5 novel Cytotoxic T Lymphocytes (CTL), 5 Helper T Lymphocytes (HTL), and 6 linear B-cell potential antigenic epitopes which are novel and can be used for further vaccine development against Nipah virus. Then we performed the physicochemical properties, antigenic, immunogenic and allergenicity prediction of the designed vaccine candidate against NPV. Further, Computational analysis indicated that these epitopes possessed highly antigenic properties and were capable of interacting with immune receptors. The designed vaccine were then docked with the human immune receptors, namely TLR-2 and TLR-4 showed robust interaction with the immune receptor. Molecular dynamics simulations demonstrated robust binding and good dynamics. After numerous dosages at varied intervals, computational immune response modeling showed that the immunogenic construct might elicit a significant immune response. In conclusion, the immunogenic construct shows promise in providing protection against NPV, However, further experimental validation is required before moving to clinical trials.
    Matched MeSH terms: Computational Biology/methods
  5. A biological inspired fuzzy adaptive window median filter (FAWMF) for enhancing DNA signal processing
    Ahmad M, Jung LT, Bhuiyan AA
    Comput Methods Programs Biomed, 2017 Oct;149:11-17.
    PMID: 28802326 DOI: 10.1016/j.cmpb.2017.06.021
    BACKGROUND AND OBJECTIVE: Digital signal processing techniques commonly employ fixed length window filters to process the signal contents. DNA signals differ in characteristics from common digital signals since they carry nucleotides as contents. The nucleotides own genetic code context and fuzzy behaviors due to their special structure and order in DNA strand. Employing conventional fixed length window filters for DNA signal processing produce spectral leakage and hence results in signal noise. A biological context aware adaptive window filter is required to process the DNA signals.

    METHODS: This paper introduces a biological inspired fuzzy adaptive window median filter (FAWMF) which computes the fuzzy membership strength of nucleotides in each slide of window and filters nucleotides based on median filtering with a combination of s-shaped and z-shaped filters. Since coding regions cause 3-base periodicity by an unbalanced nucleotides' distribution producing a relatively high bias for nucleotides' usage, such fundamental characteristic of nucleotides has been exploited in FAWMF to suppress the signal noise.

    RESULTS: Along with adaptive response of FAWMF, a strong correlation between median nucleotides and the Π shaped filter was observed which produced enhanced discrimination between coding and non-coding regions contrary to fixed length conventional window filters. The proposed FAWMF attains a significant enhancement in coding regions identification i.e. 40% to 125% as compared to other conventional window filters tested over more than 250 benchmarked and randomly taken DNA datasets of different organisms.

    CONCLUSION: This study proves that conventional fixed length window filters applied to DNA signals do not achieve significant results since the nucleotides carry genetic code context. The proposed FAWMF algorithm is adaptive and outperforms significantly to process DNA signal contents. The algorithm applied to variety of DNA datasets produced noteworthy discrimination between coding and non-coding regions contrary to fixed window length conventional filters.

    Matched MeSH terms: Computational Biology/methods*
  6. Fulltext First comprehensive in silico analysis of the functional and structural consequences of SNPs in human GalNAc-T1 gene
    Mohamoud HS, Hussain MR, El-Harouni AA, Shaik NA, Qasmi ZU, Merican AF, et al.
    Comput Math Methods Med, 2014;2014:904052.
    PMID: 24723968 DOI: 10.1155/2014/904052
    GalNAc-T1, a key candidate of GalNac-transferases genes family that is involved in mucin-type O-linked glycosylation pathway, is expressed in most biological tissues and cell types. Despite the reported association of GalNAc-T1 gene mutations with human disease susceptibility, the comprehensive computational analysis of coding, noncoding and regulatory SNPs, and their functional impacts on protein level, still remains unknown. Therefore, sequence- and structure-based computational tools were employed to screen the entire listed coding SNPs of GalNAc-T1 gene in order to identify and characterize them. Our concordant in silico analysis by SIFT, PolyPhen-2, PANTHER-cSNP, and SNPeffect tools, identified the potential nsSNPs (S143P, G258V, and Y414D variants) from 18 nsSNPs of GalNAc-T1. Additionally, 2 regulatory SNPs (rs72964406 and #x26; rs34304568) were also identified in GalNAc-T1 by using FastSNP tool. Using multiple computational approaches, we have systematically classified the functional mutations in regulatory and coding regions that can modify expression and function of GalNAc-T1 enzyme. These genetic variants can further assist in better understanding the wide range of disease susceptibility associated with the mucin-based cell signalling and pathogenic binding, and may help to develop novel therapeutic elements for associated diseases.
    Matched MeSH terms: Computational Biology/methods
  7. Fulltext An Improved Strategy for Task Scheduling in the Parallel Computational Alignment of Multiple Sequences
    Ishaq M, Khan A, Su'ud MM, Alam MM, Bangash JI, Khan A
    Comput Math Methods Med, 2022;2022:8691646.
    PMID: 35126641 DOI: 10.1155/2022/8691646
    Task scheduling in parallel multiple sequence alignment (MSA) through improved dynamic programming optimization speeds up alignment processing. The increased importance of multiple matching sequences also needs the utilization of parallel processor systems. This dynamic algorithm proposes improved task scheduling in case of parallel MSA. Specifically, the alignment of several tertiary structured proteins is computationally complex than simple word-based MSA. Parallel task processing is computationally more efficient for protein-structured based superposition. The basic condition for the application of dynamic programming is also fulfilled, because the task scheduling problem has multiple possible solutions or options. Search space reduction for speedy processing of this algorithm is carried out through greedy strategy. Performance in terms of better results is ensured through computationally expensive recursive and iterative greedy approaches. Any optimal scheduling schemes show better performance in heterogeneous resources using CPU or GPU.
    Matched MeSH terms: Computational Biology/methods*
  8. Detection of copy number variations in epilepsy using exome data
    Tsuchida N, Nakashima M, Kato M, Heyman E, Inui T, Haginoya K, et al.
    Clin Genet, 2018 03;93(3):577-587.
    PMID: 28940419 DOI: 10.1111/cge.13144
    Epilepsies are common neurological disorders and genetic factors contribute to their pathogenesis. Copy number variations (CNVs) are increasingly recognized as an important etiology of many human diseases including epilepsy. Whole-exome sequencing (WES) is becoming a standard tool for detecting pathogenic mutations and has recently been applied to detecting CNVs. Here, we analyzed 294 families with epilepsy using WES, and focused on 168 families with no causative single nucleotide variants in known epilepsy-associated genes to further validate CNVs using 2 different CNV detection tools using WES data. We confirmed 18 pathogenic CNVs, and 2 deletions and 2 duplications at chr15q11.2 of clinically unknown significance. Of note, we were able to identify small CNVs less than 10 kb in size, which might be difficult to detect by conventional microarray. We revealed 2 cases with pathogenic CNVs that one of the 2 CNV detection tools failed to find, suggesting that using different CNV tools is recommended to increase diagnostic yield. Considering a relatively high discovery rate of CNVs (18 out of 168 families, 10.7%) and successful detection of CNV with <10 kb in size, CNV detection by WES may be able to surrogate, or at least complement, conventional microarray analysis.
    Matched MeSH terms: Computational Biology/methods
  9. Identification of metabolic pathways using pathfinding approaches: a systematic review
    Abd Algfoor Z, Shahrizal Sunar M, Abdullah A, Kolivand H
    Brief Funct Genomics, 2017 03 01;16(2):87-98.
    PMID: 26969656 DOI: 10.1093/bfgp/elw002
    Metabolic pathways have become increasingly available for various microorganisms. Such pathways have spurred the development of a wide array of computational tools, in particular, mathematical pathfinding approaches. This article can facilitate the understanding of computational analysis of metabolic pathways in genomics. Moreover, stoichiometric and pathfinding approaches in metabolic pathway analysis are discussed. Three major types of studies are elaborated: stoichiometric identification models, pathway-based graph analysis and pathfinding approaches in cellular metabolism. Furthermore, evaluation of the outcomes of the pathways with mathematical benchmarking metrics is provided. This review would lead to better comprehension of metabolism behaviors in living cells, in terms of computed pathfinding approaches.
    Matched MeSH terms: Computational Biology/methods*
  10. Fulltext Identification of independent association signals and putative functional variants for breast cancer risk through fine-scale mapping of the 12p11 locus
    Zeng C, Guo X, Long J, Kuchenbaecker KB, Droit A, Michailidou K, et al.
    Breast Cancer Res, 2016 06 21;18(1):64.
    PMID: 27459855 DOI: 10.1186/s13058-016-0718-0
    BACKGROUND: Multiple recent genome-wide association studies (GWAS) have identified a single nucleotide polymorphism (SNP), rs10771399, at 12p11 that is associated with breast cancer risk.

    METHOD: We performed a fine-scale mapping study of a 700 kb region including 441 genotyped and more than 1300 imputed genetic variants in 48,155 cases and 43,612 controls of European descent, 6269 cases and 6624 controls of East Asian descent and 1116 cases and 932 controls of African descent in the Breast Cancer Association Consortium (BCAC; http://bcac.ccge.medschl.cam.ac.uk/ ), and in 15,252 BRCA1 mutation carriers in the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA). Stepwise regression analyses were performed to identify independent association signals. Data from the Encyclopedia of DNA Elements project (ENCODE) and the Cancer Genome Atlas (TCGA) were used for functional annotation.

    RESULTS: Analysis of data from European descendants found evidence for four independent association signals at 12p11, represented by rs7297051 (odds ratio (OR) = 1.09, 95 % confidence interval (CI) = 1.06-1.12; P = 3 × 10(-9)), rs805510 (OR = 1.08, 95 % CI = 1.04-1.12, P = 2 × 10(-5)), and rs1871152 (OR = 1.04, 95 % CI = 1.02-1.06; P = 2 × 10(-4)) identified in the general populations, and rs113824616 (P = 7 × 10(-5)) identified in the meta-analysis of BCAC ER-negative cases and BRCA1 mutation carriers. SNPs rs7297051, rs805510 and rs113824616 were also associated with breast cancer risk at P 

    Matched MeSH terms: Computational Biology/methods
  11. Fulltext Evidence-based gene models for structural and functional annotations of the oil palm genome
    Chan KL, Tatarinova TV, Rosli R, Amiruddin N, Azizi N, Halim MAA, et al.
    Biol. Direct, 2017 Sep 08;12(1):21.
    PMID: 28886750 DOI: 10.1186/s13062-017-0191-4
    BACKGROUND: Oil palm is an important source of edible oil. The importance of the crop, as well as its long breeding cycle (10-12 years) has led to the sequencing of its genome in 2013 to pave the way for genomics-guided breeding. Nevertheless, the first set of gene predictions, although useful, had many fragmented genes. Classification and characterization of genes associated with traits of interest, such as those for fatty acid biosynthesis and disease resistance, were also limited. Lipid-, especially fatty acid (FA)-related genes are of particular interest for the oil palm as they specify oil yields and quality. This paper presents the characterization of the oil palm genome using different gene prediction methods and comparative genomics analysis, identification of FA biosynthesis and disease resistance genes, and the development of an annotation database and bioinformatics tools.

    RESULTS: Using two independent gene-prediction pipelines, Fgenesh++ and Seqping, 26,059 oil palm genes with transcriptome and RefSeq support were identified from the oil palm genome. These coding regions of the genome have a characteristic broad distribution of GC3 (fraction of cytosine and guanine in the third position of a codon) with over half the GC3-rich genes (GC3 ≥ 0.75286) being intronless. In comparison, only one-seventh of the oil palm genes identified are intronless. Using comparative genomics analysis, characterization of conserved domains and active sites, and expression analysis, 42 key genes involved in FA biosynthesis in oil palm were identified. For three of them, namely EgFABF, EgFABH and EgFAD3, segmental duplication events were detected. Our analysis also identified 210 candidate resistance genes in six classes, grouped by their protein domain structures.

    CONCLUSIONS: We present an accurate and comprehensive annotation of the oil palm genome, focusing on analysis of important categories of genes (GC3-rich and intronless), as well as those associated with important functions, such as FA biosynthesis and disease resistance. The study demonstrated the advantages of having an integrated approach to gene prediction and developed a computational framework for combining multiple genome annotations. These results, available in the oil palm annotation database ( http://palmxplore.mpob.gov.my ), will provide important resources for studies on the genomes of oil palm and related crops.

    REVIEWERS: This article was reviewed by Alexander Kel, Igor Rogozin, and Vladimir A. Kuznetsov.

    Matched MeSH terms: Computational Biology/methods
  12. Fulltext birgHPC: creating instant computing clusters for bioinformatics and molecular dynamics
    Chew TH, Joyce-Tan KH, Akma F, Shamsir MS
    Bioinformatics, 2011 May 1;27(9):1320-1.
    PMID: 21398666 DOI: 10.1093/bioinformatics/btr109
    birgHPC, a bootable Linux Live CD has been developed to create high-performance clusters for bioinformatics and molecular dynamics studies using any Local Area Network (LAN)-networked computers. birgHPC features automated hardware and slots detection as well as provides a simple job submission interface. The latest versions of GROMACS, NAMD, mpiBLAST and ClustalW-MPI can be run in parallel by simply booting the birgHPC CD or flash drive from the head node, which immediately positions the rest of the PCs on the network as computing nodes. Thus, a temporary, affordable, scalable and high-performance computing environment can be built by non-computing-based researchers using low-cost commodity hardware.
    Matched MeSH terms: Computational Biology/methods*
  13. Fulltext Impacts of Nonsynonymous Single Nucleotide Polymorphisms of Adiponectin Receptor 1 Gene on Corresponding Protein Stability: A Computational Approach
    Saleh MA, Solayman M, Paul S, Saha M, Khalil MI, Gan SH
    Biomed Res Int, 2016;2016:9142190.
    PMID: 27294143 DOI: 10.1155/2016/9142190
    Despite the reported association of adiponectin receptor 1 (ADIPOR1) gene mutations with vulnerability to several human metabolic diseases, there is lack of computational analysis on the functional and structural impacts of single nucleotide polymorphisms (SNPs) of the human ADIPOR1 at protein level. Therefore, sequence- and structure-based computational tools were employed in this study to functionally and structurally characterize the coding nsSNPs of ADIPOR1 gene listed in the dbSNP database. Our in silico analysis by SIFT, nsSNPAnalyzer, PolyPhen-2, Fathmm, I-Mutant 2.0, SNPs&GO, PhD-SNP, PANTHER, and SNPeffect tools identified the nsSNPs with distorting functional impacts, namely, rs765425383 (A348G), rs752071352 (H341Y), rs759555652 (R324L), rs200326086 (L224F), and rs766267373 (L143P) from 74 nsSNPs of ADIPOR1 gene. Finally the aforementioned five deleterious nsSNPs were introduced using Swiss-PDB Viewer package within the X-ray crystal structure of ADIPOR1 protein, and changes in free energy for these mutations were computed. Although increased free energy was observed for all the mutants, the nsSNP H341Y caused the highest energy increase amongst all. RMSD and TM scores predicted that mutants were structurally similar to wild type protein. Our analyses suggested that the aforementioned variants especially H341Y could directly or indirectly destabilize the amino acid interactions and hydrogen bonding networks of ADIPOR1.
    Matched MeSH terms: Computational Biology/methods*
  14. Fulltext Prediction of antimicrobial peptides based on sequence alignment and support vector machine-pairwise algorithm utilizing LZ-complexity
    Ng XY, Rosdi BA, Shahrudin S
    Biomed Res Int, 2015;2015:212715.
    PMID: 25802839 DOI: 10.1155/2015/212715
    This study concerns an attempt to establish a new method for predicting antimicrobial peptides (AMPs) which are important to the immune system. Recently, researchers are interested in designing alternative drugs based on AMPs because they have found that a large number of bacterial strains have become resistant to available antibiotics. However, researchers have encountered obstacles in the AMPs designing process as experiments to extract AMPs from protein sequences are costly and require a long set-up time. Therefore, a computational tool for AMPs prediction is needed to resolve this problem. In this study, an integrated algorithm is newly introduced to predict AMPs by integrating sequence alignment and support vector machine- (SVM-) LZ complexity pairwise algorithm. It was observed that, when all sequences in the training set are used, the sensitivity of the proposed algorithm is 95.28% in jackknife test and 87.59% in independent test, while the sensitivity obtained for jackknife test and independent test is 88.74% and 78.70%, respectively, when only the sequences that has less than 70% similarity are used. Applying the proposed algorithm may allow researchers to effectively predict AMPs from unknown protein peptide sequences with higher sensitivity.
    Matched MeSH terms: Computational Biology/methods
  15. Fulltext Pattern Matching for DNA Sequencing Data Using Multiple Bloom Filters
    Najam M, Rasool RU, Ahmad HF, Ashraf U, Malik AW
    Biomed Res Int, 2019;2019:7074387.
    PMID: 31111064 DOI: 10.1155/2019/7074387
    Storing and processing of large DNA sequences has always been a major problem due to increasing volume of DNA sequence data. However, a number of solutions have been proposed but they require significant computation and memory. Therefore, an efficient storage and pattern matching solution is required for DNA sequencing data. Bloom filters (BFs) represent an efficient data structure, which is mostly used in the domain of bioinformatics for classification of DNA sequences. In this paper, we explore more dimensions where BFs can be used other than classification. A proposed solution is based on Multiple Bloom Filters (MBFs) that finds all the locations and number of repetitions of the specified pattern inside a DNA sequence. Both of these factors are extremely important in determining the type and intensity of any disease. This paper serves as a first effort towards optimizing the search for location and frequency of substrings in DNA sequences using MBFs. We expect that further optimizations in the proposed solution can bring remarkable results as this paper presents a proof of concept implementation for a given set of data using proposed MBFs technique. Performance evaluation shows improved accuracy and time efficiency of the proposed approach.
    Matched MeSH terms: Computational Biology/methods*
  16. Converting differential-equation models of biological systems to membrane computing
    Muniyandi RC, Zin AM, Sanders JW
    Biosystems, 2013 Dec;114(3):219-26.
    PMID: 24120990 DOI: 10.1016/j.biosystems.2013.09.008
    This paper presents a method to convert the deterministic, continuous representation of a biological system by ordinary differential equations into a non-deterministic, discrete membrane computation. The dynamics of the membrane computation is governed by rewrite rules operating at certain rates. That has the advantage of applying accurately to small systems, and to expressing rates of change that are determined locally, by region, but not necessary globally. Such spatial information augments the standard differentiable approach to provide a more realistic model. A biological case study of the ligand-receptor network of protein TGF-β is used to validate the effectiveness of the conversion method. It demonstrates the sense in which the behaviours and properties of the system are better preserved in the membrane computing model, suggesting that the proposed conversion method may prove useful for biological systems in particular.
    Matched MeSH terms: Computational Biology/methods*
  17. An improved hybrid of particle swarm optimization and the gravitational search algorithm to produce a kinetic parameter estimation of aspartate biochemical pathways
    Ismail AM, Mohamad MS, Abdul Majid H, Abas KH, Deris S, Zaki N, et al.
    Biosystems, 2017 Dec;162:81-89.
    PMID: 28951204 DOI: 10.1016/j.biosystems.2017.09.013
    Mathematical modelling is fundamental to understand the dynamic behavior and regulation of the biochemical metabolisms and pathways that are found in biological systems. Pathways are used to describe complex processes that involve many parameters. It is important to have an accurate and complete set of parameters that describe the characteristics of a given model. However, measuring these parameters is typically difficult and even impossible in some cases. Furthermore, the experimental data are often incomplete and also suffer from experimental noise. These shortcomings make it challenging to identify the best-fit parameters that can represent the actual biological processes involved in biological systems. Computational approaches are required to estimate these parameters. The estimation is converted into multimodal optimization problems that require a global optimization algorithm that can avoid local solutions. These local solutions can lead to a bad fit when calibrating with a model. Although the model itself can potentially match a set of experimental data, a high-performance estimation algorithm is required to improve the quality of the solutions. This paper describes an improved hybrid of particle swarm optimization and the gravitational search algorithm (IPSOGSA) to improve the efficiency of a global optimum (the best set of kinetic parameter values) search. The findings suggest that the proposed algorithm is capable of narrowing down the search space by exploiting the feasible solution areas. Hence, the proposed algorithm is able to achieve a near-optimal set of parameters at a fast convergence speed. The proposed algorithm was tested and evaluated based on two aspartate pathways that were obtained from the BioModels Database. The results show that the proposed algorithm outperformed other standard optimization algorithms in terms of accuracy and near-optimal kinetic parameter estimation. Nevertheless, the proposed algorithm is only expected to work well in small scale systems. In addition, the results of this study can be used to estimate kinetic parameter values in the stage of model selection for different experimental conditions.
    Matched MeSH terms: Computational Biology/methods*
  18. Fulltext Identification of putative drug targets for human sperm-egg interaction defect using protein network approach
    Sabetian S, Shamsir MS
    BMC Syst Biol, 2015;9:37.
    PMID: 26187737 DOI: 10.1186/s12918-015-0186-7
    Sperm-egg interaction defect is a significant cause of in-vitro fertilization failure for infertile cases. Numerous molecular interactions in the form of protein-protein interactions mediate the sperm-egg membrane interaction process. Recent studies have demonstrated that in addition to experimental techniques, computational methods, namely protein interaction network approach, can address protein-protein interactions between human sperm and egg. Up to now, no drugs have been detected to treat sperm-egg interaction disorder, and the initial step in drug discovery research is finding out essential proteins or drug targets for a biological process. The main purpose of this study is to identify putative drug targets for human sperm-egg interaction deficiency and consider if the detected essential proteins are targets for any known drugs using protein-protein interaction network and ingenuity pathway analysis.
    Matched MeSH terms: Computational Biology/methods*
  19. Fulltext Computational identification and binding analysis of orphan human cytochrome P450 4X1 enzyme with substrates
    Kumar S
    BMC Res Notes, 2015;8:9.
    PMID: 25595103 DOI: 10.1186/s13104-015-0976-4
    Cytochrome P450s (CYPs) are important heme-containing proteins, well known for their monooxygenase reaction. The human cytochrome P450 4X1 (CYP4X1) is categorized as "orphan" CYP because of its unknown function. In recent studies it is found that this enzyme is expressed in neurovascular functions of the brain. Also, various studies have found the expression and activity of orphan human cytochrome P450 4X1 in cancer. It is found to be a potential drug target for cancer therapy. However, three-dimensional structure, the active site topology and substrate specificity of CYP4X1 remain unclear.
    Matched MeSH terms: Computational Biology/methods*
  20. Fulltext An efficient clustering algorithm for partitioning Y-short tandem repeats data
    Seman A, Bakar ZA, Isa MN
    BMC Res Notes, 2012;5:557.
    PMID: 23039132 DOI: 10.1186/1756-0500-5-557
    Y-Short Tandem Repeats (Y-STR) data consist of many similar and almost similar objects. This characteristic of Y-STR data causes two problems with partitioning: non-unique centroids and local minima problems. As a result, the existing partitioning algorithms produce poor clustering results.
    Matched MeSH terms: Computational Biology/methods*
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