Pub Date : 2019-07-07DOI: 10.1504/IJCBDD.2019.10022529
S. Munjal, Gaurav Jaisawal, N. Goel, U. P. Singh, Ajay Vishwakrma, Abhinav K. Srivastava
Haemophilia A has been known as a disease since the late 20th century but still no cure has been developed for it. Temporary treatments include new factor replacement therapies delaying the frequency of blood transfusions. In this study, a new prospective drug molecule was designed in silico. Thirteen target proteins were identified from protein databases and their structures observed. Cavities in the protein were determined using Swiss PDB Viewer. Twelve ligands and its isomers were prepared through Molinspiration. Docking between the ligands and target proteins was performed using Molegro Virtual Docker. Docking studies analysed the MolDock and Hydrogen bond score. The most appropriate values were obtained with protein 1SDD and ligand 1. Therefore, Ligand 1 can be proceeded with more studies and developed into a potential drug for Haemophilia A.
{"title":"An in silico approach to design a potential drug for Haemophilia A","authors":"S. Munjal, Gaurav Jaisawal, N. Goel, U. P. Singh, Ajay Vishwakrma, Abhinav K. Srivastava","doi":"10.1504/IJCBDD.2019.10022529","DOIUrl":"https://doi.org/10.1504/IJCBDD.2019.10022529","url":null,"abstract":"Haemophilia A has been known as a disease since the late 20th century but still no cure has been developed for it. Temporary treatments include new factor replacement therapies delaying the frequency of blood transfusions. In this study, a new prospective drug molecule was designed in silico. Thirteen target proteins were identified from protein databases and their structures observed. Cavities in the protein were determined using Swiss PDB Viewer. Twelve ligands and its isomers were prepared through Molinspiration. Docking between the ligands and target proteins was performed using Molegro Virtual Docker. Docking studies analysed the MolDock and Hydrogen bond score. The most appropriate values were obtained with protein 1SDD and ligand 1. Therefore, Ligand 1 can be proceeded with more studies and developed into a potential drug for Haemophilia A.","PeriodicalId":13612,"journal":{"name":"Int. J. Comput. Biol. Drug Des.","volume":"86 1","pages":"219-229"},"PeriodicalIF":0.0,"publicationDate":"2019-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75160105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-07DOI: 10.1504/IJCBDD.2019.10022512
S. Nathiya, M. Durga, T. Devasena
In this study, a computational approach has been employed to study the interactions of human acetylcholinesterase (AChE), human oxyhaemoglobin and human high-affinity IgE receptor with an organophosphate pesticide, and the comparative binding affinity, interacting residues of protein, H-bond distance and fitness score have been evaluated using GOLD software. Monocrotophos and its analogues bind to AChE with the highest fitness score. The analogue RPR-II binds to the receptor with a highest fitness score: 42.17 when compared with RPR-V (fitness score: 40.62) and monocrotophos (fitness score: 35.25). Monocrotophos, RPR-II and RPR-V interact with oxyhaemoglobin with a fitness score of about 17.68, 20.16 and 24.62, respectively. Monocrotophos, RPR-II and RPR-V interact with human high-affinity IgE receptor with a fitness score of about 18.29, 19.05 and 22.57, respectively. The above-mentioned results indicate that RPR series are highly toxic than monocrotophos, hence there is a need for complete evaluation of the toxicological effect of new pesticides.
{"title":"Computational prediction of binding of monocrotophos and its analogues on human acetylcholinesterase, oxyhaemoglobin and IgE antibody","authors":"S. Nathiya, M. Durga, T. Devasena","doi":"10.1504/IJCBDD.2019.10022512","DOIUrl":"https://doi.org/10.1504/IJCBDD.2019.10022512","url":null,"abstract":"In this study, a computational approach has been employed to study the interactions of human acetylcholinesterase (AChE), human oxyhaemoglobin and human high-affinity IgE receptor with an organophosphate pesticide, and the comparative binding affinity, interacting residues of protein, H-bond distance and fitness score have been evaluated using GOLD software. Monocrotophos and its analogues bind to AChE with the highest fitness score. The analogue RPR-II binds to the receptor with a highest fitness score: 42.17 when compared with RPR-V (fitness score: 40.62) and monocrotophos (fitness score: 35.25). Monocrotophos, RPR-II and RPR-V interact with oxyhaemoglobin with a fitness score of about 17.68, 20.16 and 24.62, respectively. Monocrotophos, RPR-II and RPR-V interact with human high-affinity IgE receptor with a fitness score of about 18.29, 19.05 and 22.57, respectively. The above-mentioned results indicate that RPR series are highly toxic than monocrotophos, hence there is a need for complete evaluation of the toxicological effect of new pesticides.","PeriodicalId":13612,"journal":{"name":"Int. J. Comput. Biol. Drug Des.","volume":"3 1","pages":"268-280"},"PeriodicalIF":0.0,"publicationDate":"2019-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79052731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-07DOI: 10.1504/IJCBDD.2019.10022511
Bin Chen
The colony-based laser scatter imaging for microbial source tracking heavily relies on the power of optical scattering image classification. While carefully handcraft feature extraction achieved excellent results for the colonies with certain sizes for optimal classification results, the classification accuracy drops quickly for smaller or larger colonies outside of the colony size range. In this study, a deep convolutional neural network was implemented for laser scattering image feature extraction and classification. The results show that the deep learning classification method clearly outperforms the traditional clustering methods with high accuracy and consistency for host species with a wide range of colony sizes. It also provides comparable accuracy for the colonies with the optimal sizes.
{"title":"Deep convolutional neural network for laser forward scattering image classification in microbial source tracking","authors":"Bin Chen","doi":"10.1504/IJCBDD.2019.10022511","DOIUrl":"https://doi.org/10.1504/IJCBDD.2019.10022511","url":null,"abstract":"The colony-based laser scatter imaging for microbial source tracking heavily relies on the power of optical scattering image classification. While carefully handcraft feature extraction achieved excellent results for the colonies with certain sizes for optimal classification results, the classification accuracy drops quickly for smaller or larger colonies outside of the colony size range. In this study, a deep convolutional neural network was implemented for laser scattering image feature extraction and classification. The results show that the deep learning classification method clearly outperforms the traditional clustering methods with high accuracy and consistency for host species with a wide range of colony sizes. It also provides comparable accuracy for the colonies with the optimal sizes.","PeriodicalId":13612,"journal":{"name":"Int. J. Comput. Biol. Drug Des.","volume":"10 1","pages":"261-267"},"PeriodicalIF":0.0,"publicationDate":"2019-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87133781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-07DOI: 10.1504/IJCBDD.2019.10022510
Sumaira Kanwal, S. Perveen
Nerve injuries plays a significant role in terms of individual's life style as it can be treatable or vice versa in context of the origin of the nerve. Unluckily, the repairing ability of the central nervous system is very restricted because of reduced intrinsic growth capacity, which is much feasible in PNS. These axonal growth inhibitory proteins are mediated via activation of Rho. In the present study, a hybrid approach of comparative modelling and molecular docking followed by inhibitor identification and structure modelling was employed. Our analysis showed that the two important drugs which are widely used have the potential to block the Rho-Rock pathways. Here, we report inhibitors which showed binding affinity for the three most important axonal regeneration inhibitors. Three step approaches can be used to defeat the axonal neuropathies that especially in the CMT disease. However further studies are required to find the applications of these drugs.
{"title":"Improving the nerve regeneration ability by inhibiting the orchestral activity of the myelin associated repair inhibitors: an in silico approach","authors":"Sumaira Kanwal, S. Perveen","doi":"10.1504/IJCBDD.2019.10022510","DOIUrl":"https://doi.org/10.1504/IJCBDD.2019.10022510","url":null,"abstract":"Nerve injuries plays a significant role in terms of individual's life style as it can be treatable or vice versa in context of the origin of the nerve. Unluckily, the repairing ability of the central nervous system is very restricted because of reduced intrinsic growth capacity, which is much feasible in PNS. These axonal growth inhibitory proteins are mediated via activation of Rho. In the present study, a hybrid approach of comparative modelling and molecular docking followed by inhibitor identification and structure modelling was employed. Our analysis showed that the two important drugs which are widely used have the potential to block the Rho-Rock pathways. Here, we report inhibitors which showed binding affinity for the three most important axonal regeneration inhibitors. Three step approaches can be used to defeat the axonal neuropathies that especially in the CMT disease. However further studies are required to find the applications of these drugs.","PeriodicalId":13612,"journal":{"name":"Int. J. Comput. Biol. Drug Des.","volume":"12 1","pages":"251-260"},"PeriodicalIF":0.0,"publicationDate":"2019-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91303592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-07DOI: 10.20944/PREPRINTS201610.0016.V1
Uzma Khanam, B. K. Malik, P. Mathur, B. Rathi
Caveolin-1 (Cav-1) is 22 kDa caveolae protein, acts as a scaffold within caveolar membranes, interacts with Gα-protein and thereby regulates their activity. Earlier studies reported elevated caveolin-1 levels in the serum of prostate cancer patients. Secreted Cav-1 promotes angiogenesis, cell proliferation and anti-apoptotic activities in prostate cancer patients. This study was designed to explore Cav-1 as a target for prostate cancer therapy using computational approach. Molecular docking, structural base molecular modelling and molecular dynamics simulations were performed to investigate Cav-1 inhibitors. A predictive model was used for virtual screening against ZINC database of biogenic compounds. Stability of the active site residues of Cav-1 was estimated by IFD and 100 ns long molecular dynamic simulations. The reported compounds showed significant binding and thus can be considered potent therapeutic inhibitors of Cav-1. Thus, further investigative studies on the biochemical interactions of Cav-1 would provide a valuable insight into its probable therapeutic applications.
{"title":"Human caveolin-1 a potent inhibitor for prostate cancer therapy: a computational approach","authors":"Uzma Khanam, B. K. Malik, P. Mathur, B. Rathi","doi":"10.20944/PREPRINTS201610.0016.V1","DOIUrl":"https://doi.org/10.20944/PREPRINTS201610.0016.V1","url":null,"abstract":"Caveolin-1 (Cav-1) is 22 kDa caveolae protein, acts as a scaffold within caveolar membranes, interacts with Gα-protein and thereby regulates their activity. Earlier studies reported elevated caveolin-1 levels in the serum of prostate cancer patients. Secreted Cav-1 promotes angiogenesis, cell proliferation and anti-apoptotic activities in prostate cancer patients. This study was designed to explore Cav-1 as a target for prostate cancer therapy using computational approach. Molecular docking, structural base molecular modelling and molecular dynamics simulations were performed to investigate Cav-1 inhibitors. A predictive model was used for virtual screening against ZINC database of biogenic compounds. Stability of the active site residues of Cav-1 was estimated by IFD and 100 ns long molecular dynamic simulations. The reported compounds showed significant binding and thus can be considered potent therapeutic inhibitors of Cav-1. Thus, further investigative studies on the biochemical interactions of Cav-1 would provide a valuable insight into its probable therapeutic applications.","PeriodicalId":13612,"journal":{"name":"Int. J. Comput. Biol. Drug Des.","volume":"1 1","pages":"203-218"},"PeriodicalIF":0.0,"publicationDate":"2019-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89252468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-07DOI: 10.1504/IJCBDD.2019.10022530
Satheeshkumar Sellamuthu, Ashok Kumar, S. Singh
Novel molecules were designed as possible inhibitors of ATP synthase through de novo drug design, but were not drug-like molecules. Hence, ZINC database was searched for drug-like molecules from the common pharmacophore of the designed molecules. A total of 472 hits were obtained, among them, ZINC39552534, ZINC39371747, and ZINC38959526 produced better docking than the standard drug Bedaquiline. The vulnerability of TB and HIV co-infection has necessitated the search for inhibitors effective against both the diseases. Hence, the hits obtained were further screened for possible interaction with HIV reverse transcriptase. ZINC63941671, ZINC05858010, and ZINC05857787 were found better over the standard drug Rilpivirine, but their interaction was least against ATP synthase. Further, ZINC38959526 (lead against ATP synthase) and ZINC05858010 (lead against reverse transcriptase) share some common chemical features and based on this, new hybrid molecules were designed to inhibit both the targets. The possibility of hERG toxicity was also checked to eliminate unwanted cardiotoxicity.
通过从头开始的药物设计，设计了可能作为ATP合酶抑制剂的新分子，但不是药物样分子。因此，在锌数据库中从设计分子的共同药效团中搜索类药物分子。共获得472次命中，其中ZINC39552534、ZINC39371747、ZINC38959526的对接效果优于标准药贝达喹啉。结核病和艾滋病毒合并感染的易感性使人们有必要寻找对这两种疾病都有效的抑制剂。因此，获得的hit进一步筛选可能与HIV逆转录酶相互作用。ZINC63941671、ZINC05858010和ZINC05857787对ATP合酶的相互作用较弱，优于标准药物Rilpivirine。此外，ZINC38959526 (lead against ATP synthase)和ZINC05858010 (lead against逆转录酶)具有一些共同的化学特征，并在此基础上设计了新的杂交分子来抑制这两个靶点。还检查了hERG毒性的可能性，以消除不必要的心脏毒性。
{"title":"De novo drug design, pharmacophore search and molecular docking for inhibitors to treat TB and HIV co-infection","authors":"Satheeshkumar Sellamuthu, Ashok Kumar, S. Singh","doi":"10.1504/IJCBDD.2019.10022530","DOIUrl":"https://doi.org/10.1504/IJCBDD.2019.10022530","url":null,"abstract":"Novel molecules were designed as possible inhibitors of ATP synthase through de novo drug design, but were not drug-like molecules. Hence, ZINC database was searched for drug-like molecules from the common pharmacophore of the designed molecules. A total of 472 hits were obtained, among them, ZINC39552534, ZINC39371747, and ZINC38959526 produced better docking than the standard drug Bedaquiline. The vulnerability of TB and HIV co-infection has necessitated the search for inhibitors effective against both the diseases. Hence, the hits obtained were further screened for possible interaction with HIV reverse transcriptase. ZINC63941671, ZINC05858010, and ZINC05857787 were found better over the standard drug Rilpivirine, but their interaction was least against ATP synthase. Further, ZINC38959526 (lead against ATP synthase) and ZINC05858010 (lead against reverse transcriptase) share some common chemical features and based on this, new hybrid molecules were designed to inhibit both the targets. The possibility of hERG toxicity was also checked to eliminate unwanted cardiotoxicity.","PeriodicalId":13612,"journal":{"name":"Int. J. Comput. Biol. Drug Des.","volume":"67 1","pages":"230-250"},"PeriodicalIF":0.0,"publicationDate":"2019-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91119054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-11DOI: 10.1504/IJCBDD.2019.10021273
N. Wang, Jinghu Wang, Fei Hou, Guang Zheng
Ginger is widely used as both a cooking spice in East/South Asia and a traditional Chinese medicine (TCM) for its warming interior function where mainly refers to the part of stomach and small intestine. However, the underlying mechanism at protein regulating network level is obscure. In this study, within stomach and small intestine, 6-gingerol and 6-shaogaol (ginger's main bio-active compounds), are selected to initialise the underlying protein regulating networks. The initial step is to identify proteins targeted/regulated by ginger which were extracted from PubMed literatures and compound-protein databases. Then, functional protein-protein interactions (FPPI) were fetched to form the underlying regulating networks within stomach and small intestine. Further enrichment analysis of FPPI participating proteins salience five key metabolic processes which can be validated by both PubMed literature and online bioinformatics tools. Thus, ginger's warming interior function is primarily elaborated via FPPI network by enhancing metabolic processes.
{"title":"Networks regulated by ginger towards stomach and small intestine for its warming interior function","authors":"N. Wang, Jinghu Wang, Fei Hou, Guang Zheng","doi":"10.1504/IJCBDD.2019.10021273","DOIUrl":"https://doi.org/10.1504/IJCBDD.2019.10021273","url":null,"abstract":"Ginger is widely used as both a cooking spice in East/South Asia and a traditional Chinese medicine (TCM) for its warming interior function where mainly refers to the part of stomach and small intestine. However, the underlying mechanism at protein regulating network level is obscure. In this study, within stomach and small intestine, 6-gingerol and 6-shaogaol (ginger's main bio-active compounds), are selected to initialise the underlying protein regulating networks. The initial step is to identify proteins targeted/regulated by ginger which were extracted from PubMed literatures and compound-protein databases. Then, functional protein-protein interactions (FPPI) were fetched to form the underlying regulating networks within stomach and small intestine. Further enrichment analysis of FPPI participating proteins salience five key metabolic processes which can be validated by both PubMed literature and online bioinformatics tools. Thus, ginger's warming interior function is primarily elaborated via FPPI network by enhancing metabolic processes.","PeriodicalId":13612,"journal":{"name":"Int. J. Comput. Biol. Drug Des.","volume":"51 1","pages":"189-202"},"PeriodicalIF":0.0,"publicationDate":"2019-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76505654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-11DOI: 10.1504/IJCBDD.2019.10021274
Silu Zhang, Junqing Wang, Keli Xu, Megan M. York, Yingyuan Mo, Yixin Chen, Yunyun Zhou
Gene expression profiles are widely used for identifying phenotype-specific biomarkers in clinical cancer research. By examining important genes expressed in different phenotypes, patients can be classified into different treatment groups. Microarray and RNAseq are the two leading technologies to measure gene expression data. However, due to the heterogeneity of the two platforms, their selected genes are different. In this project, we systematically compared the breast cancer subtype classification accuracies from the selected genes by four popular multiclass feature selection algorithms and discussed the strengths and weakness of selected genes across different platforms and cohorts. Our results showed that the classification of selected genes performs best within the same platform across different cohorts. It suggested that merging the dataset belonging to the same platform will increase the statistical power and improve the prediction accuracy of the selected gene for multiclass classification analysis.
{"title":"A comparative study of multiclass feature selection on RNAseq and microarray data","authors":"Silu Zhang, Junqing Wang, Keli Xu, Megan M. York, Yingyuan Mo, Yixin Chen, Yunyun Zhou","doi":"10.1504/IJCBDD.2019.10021274","DOIUrl":"https://doi.org/10.1504/IJCBDD.2019.10021274","url":null,"abstract":"Gene expression profiles are widely used for identifying phenotype-specific biomarkers in clinical cancer research. By examining important genes expressed in different phenotypes, patients can be classified into different treatment groups. Microarray and RNAseq are the two leading technologies to measure gene expression data. However, due to the heterogeneity of the two platforms, their selected genes are different. In this project, we systematically compared the breast cancer subtype classification accuracies from the selected genes by four popular multiclass feature selection algorithms and discussed the strengths and weakness of selected genes across different platforms and cohorts. Our results showed that the classification of selected genes performs best within the same platform across different cohorts. It suggested that merging the dataset belonging to the same platform will increase the statistical power and improve the prediction accuracy of the selected gene for multiclass classification analysis.","PeriodicalId":13612,"journal":{"name":"Int. J. Comput. Biol. Drug Des.","volume":"72 1","pages":"128-142"},"PeriodicalIF":0.0,"publicationDate":"2019-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84776156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-11DOI: 10.1504/IJCBDD.2019.10021270
Kathryn M. Cooper, Wail M. Hassan, H. Ali
Many bioinformatics algorithms attempt to extract relevant biological information from datasets obtained at specific data points. However, it is critical to identify changing genes in temporal data so that studies can focus on the dynamics of gene expression. While networks continue to play a significant role in characterising biological relationships, most biomedical network modelling studies focus on 'static' network-based analysis. In this study, we use a temporal, network-based approach to identify and rank genes that exhibit variation in short-course gene expression. We use a Caenorhabditis elegans (C. elegans) gene correlation network obtained from mRNA expression to illustrate the value of the proposed approach, and compare the results of this method to results obtained from traditional differential gene expression analysis. We show that temporal network analysis identifies genes that are inherently different from differentially expressed genes, raising new questions about structural meaning in expression networks and how changes in expression are observed.
{"title":"Identification of temporal network changes in short-course gene expression from C. elegans reveals structural volatility","authors":"Kathryn M. Cooper, Wail M. Hassan, H. Ali","doi":"10.1504/IJCBDD.2019.10021270","DOIUrl":"https://doi.org/10.1504/IJCBDD.2019.10021270","url":null,"abstract":"Many bioinformatics algorithms attempt to extract relevant biological information from datasets obtained at specific data points. However, it is critical to identify changing genes in temporal data so that studies can focus on the dynamics of gene expression. While networks continue to play a significant role in characterising biological relationships, most biomedical network modelling studies focus on 'static' network-based analysis. In this study, we use a temporal, network-based approach to identify and rank genes that exhibit variation in short-course gene expression. We use a Caenorhabditis elegans (C. elegans) gene correlation network obtained from mRNA expression to illustrate the value of the proposed approach, and compare the results of this method to results obtained from traditional differential gene expression analysis. We show that temporal network analysis identifies genes that are inherently different from differentially expressed genes, raising new questions about structural meaning in expression networks and how changes in expression are observed.","PeriodicalId":13612,"journal":{"name":"Int. J. Comput. Biol. Drug Des.","volume":"68 1","pages":"171-188"},"PeriodicalIF":0.0,"publicationDate":"2019-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85752863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-05-11DOI: 10.1504/IJCBDD.2019.10021271
Rodrigo Almeida, Waldeyr M. C. Silva, Klayton Castro, Aleteia P. F. Araujo, M. E. Walter, Sérgio Lifschitz, M. Holanda
Scientific experiments in bioinformatics are often executed as computational workflows. Data provenance involves documenting the history, and the paths of the input data, from the beginning to the end of an experiment. AProvBio is an architecture that enables the capture and storage of data provenance for bioinformatics workflows using the PROV-DM standard model. AProvBio works with three types of data provenance: prospect, retrospect, and the user-defined type. Given how graphs conveniently express PROV-DM, we have designed and implemented a simulator for storing the data provenance in a graph database system. This paper presents details and implementation aspects of our architecture, and an evaluation of AProvBio through the carrying out of two real case scenarios.
{"title":"Managing data provenance for bioinformatics workflows using AProvBio","authors":"Rodrigo Almeida, Waldeyr M. C. Silva, Klayton Castro, Aleteia P. F. Araujo, M. E. Walter, Sérgio Lifschitz, M. Holanda","doi":"10.1504/IJCBDD.2019.10021271","DOIUrl":"https://doi.org/10.1504/IJCBDD.2019.10021271","url":null,"abstract":"Scientific experiments in bioinformatics are often executed as computational workflows. Data provenance involves documenting the history, and the paths of the input data, from the beginning to the end of an experiment. AProvBio is an architecture that enables the capture and storage of data provenance for bioinformatics workflows using the PROV-DM standard model. AProvBio works with three types of data provenance: prospect, retrospect, and the user-defined type. Given how graphs conveniently express PROV-DM, we have designed and implemented a simulator for storing the data provenance in a graph database system. This paper presents details and implementation aspects of our architecture, and an evaluation of AProvBio through the carrying out of two real case scenarios.","PeriodicalId":13612,"journal":{"name":"Int. J. Comput. Biol. Drug Des.","volume":"23 1","pages":"153-170"},"PeriodicalIF":0.0,"publicationDate":"2019-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87112424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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