Pub Date : 2017-01-01DOI: 10.1504/IJBRA.2017.10013389
M. Amraoui, T. B. Stambouli, B. Alshaqaqi
{"title":"On using the wisdom of the crowd principles in classification, Application on breast cancer diagnosis and prognosis.","authors":"M. Amraoui, T. B. Stambouli, B. Alshaqaqi","doi":"10.1504/IJBRA.2017.10013389","DOIUrl":"https://doi.org/10.1504/IJBRA.2017.10013389","url":null,"abstract":"","PeriodicalId":35444,"journal":{"name":"International Journal of Bioinformatics Research and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66702634","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 : 2015-11-01DOI: 10.1504/IJBRA.2015.073237
A. Anitha, D. Acharjya
Currently, internet is the best tool for distributed computing, which involves spreading of data geographically. But, retrieving information from huge data is critical and has no relevance unless it provides certain information. Prediction of missing associations can be viewed as fundamental problems in machine learning where the main objective is to determine decisions for the missing associations. Mathematical models such as naive Bayes structure, human composed network structure, Bayesian network modelling, etc., were developed to this end. But, it has certain limitations and failed to include uncertainties. Therefore, effort has been made to process inconsistencies in the data with the introduction of rough set theory. This paper uses two processes, pre-process and post-process, to predict the decisions for the missing associations in the attribute values. In preprocess, rough set is used to reduce the dimensionality, whereas neural network is used in postprocess to explore the decision for the missing associations. A real-life example is provided to show the viability of the proposed research.
{"title":"Neural network and rough set hybrid scheme for prediction of missing associations","authors":"A. Anitha, D. Acharjya","doi":"10.1504/IJBRA.2015.073237","DOIUrl":"https://doi.org/10.1504/IJBRA.2015.073237","url":null,"abstract":"Currently, internet is the best tool for distributed computing, which involves spreading of data geographically. But, retrieving information from huge data is critical and has no relevance unless it provides certain information. Prediction of missing associations can be viewed as fundamental problems in machine learning where the main objective is to determine decisions for the missing associations. Mathematical models such as naive Bayes structure, human composed network structure, Bayesian network modelling, etc., were developed to this end. But, it has certain limitations and failed to include uncertainties. Therefore, effort has been made to process inconsistencies in the data with the introduction of rough set theory. This paper uses two processes, pre-process and post-process, to predict the decisions for the missing associations in the attribute values. In preprocess, rough set is used to reduce the dimensionality, whereas neural network is used in postprocess to explore the decision for the missing associations. A real-life example is provided to show the viability of the proposed research.","PeriodicalId":35444,"journal":{"name":"International Journal of Bioinformatics Research and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/IJBRA.2015.073237","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66702073","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 : 2015-11-01DOI: 10.1504/IJBRA.2015.073238
J. Vargas, J. A. Velasco, G. Alvarez, Diego Linares, E. Bravo
We describe an automatic segmentation method for polyproteins of the viruses belonging to the Potyviridae family. It uses machine learning techniques in order to predict the cleavage site which define the segments in which said polyproteins are cut in their process of functional maturation. The segmentation application is publicly available for use on a website and it can be accessed through the web service interface too. The prediction models have an average sensitivity of 0.79 and a Matthews correlation coefficient average of 0.23. This method is capable of predicting correctly (coinciding with previously published segmentation) the segmentation of sequences which come from Potyvirus and Rymovirus, genera. However accurate prediction capabilities are affected when faced with either atypical sequences or viruses belonging to less common genera in the Potyviridae family. Future work will focus on establishing greater flexibility in this sense.
{"title":"Automatic segmentation of Potyviridae family polyproteins","authors":"J. Vargas, J. A. Velasco, G. Alvarez, Diego Linares, E. Bravo","doi":"10.1504/IJBRA.2015.073238","DOIUrl":"https://doi.org/10.1504/IJBRA.2015.073238","url":null,"abstract":"We describe an automatic segmentation method for polyproteins of the viruses belonging to the Potyviridae family. It uses machine learning techniques in order to predict the cleavage site which define the segments in which said polyproteins are cut in their process of functional maturation. The segmentation application is publicly available for use on a website and it can be accessed through the web service interface too. The prediction models have an average sensitivity of 0.79 and a Matthews correlation coefficient average of 0.23. This method is capable of predicting correctly (coinciding with previously published segmentation) the segmentation of sequences which come from Potyvirus and Rymovirus, genera. However accurate prediction capabilities are affected when faced with either atypical sequences or viruses belonging to less common genera in the Potyviridae family. Future work will focus on establishing greater flexibility in this sense.","PeriodicalId":35444,"journal":{"name":"International Journal of Bioinformatics Research and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/IJBRA.2015.073238","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66702110","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 : 2015-11-01DOI: 10.1504/IJBRA.2015.073236
C. Guerra
Protein complexes are key molecular entities that perform a variety of essential cellular functions. The connectivity of proteins within a complex has been widely investigated with both experimental and computational techniques. We developed a computational approach to identify and characterise proteins that play a role in interconnecting complexes. We computed a measure of inter-complex centrality, the crossroad index, based on disjoint paths connecting proteins in distinct complexes and identified inter-complex hubs as proteins with a high value of the crossroad index. We applied the approach to a set of stable complexes in Saccharomyces cerevisiae and in Homo sapiens. Just as done for hubs, we evaluated the topological and biological properties of inter-complex hubs addressing the following questions. Do inter-complex hubs tend to be evolutionary conserved? What is the relation between crossroad index and essentiality? We found a good correlation between inter-complex hubs and both evolutionary conservation and essentiality.
{"title":"On the interconnection of stable protein complexes: inter-complex hubs and their conservation in Saccharomyces cerevisiae and Homo sapiens networks","authors":"C. Guerra","doi":"10.1504/IJBRA.2015.073236","DOIUrl":"https://doi.org/10.1504/IJBRA.2015.073236","url":null,"abstract":"Protein complexes are key molecular entities that perform a variety of essential cellular functions. The connectivity of proteins within a complex has been widely investigated with both experimental and computational techniques. We developed a computational approach to identify and characterise proteins that play a role in interconnecting complexes. We computed a measure of inter-complex centrality, the crossroad index, based on disjoint paths connecting proteins in distinct complexes and identified inter-complex hubs as proteins with a high value of the crossroad index. We applied the approach to a set of stable complexes in Saccharomyces cerevisiae and in Homo sapiens. Just as done for hubs, we evaluated the topological and biological properties of inter-complex hubs addressing the following questions. Do inter-complex hubs tend to be evolutionary conserved? What is the relation between crossroad index and essentiality? We found a good correlation between inter-complex hubs and both evolutionary conservation and essentiality.","PeriodicalId":35444,"journal":{"name":"International Journal of Bioinformatics Research and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/IJBRA.2015.073236","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66702063","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 : 2015-11-01DOI: 10.1504/IJBRA.2015.073235
S. Dey, A. Nandy, P. Nandy, Sukhen Das
Dengue viral attacks have been reported in various parts of India in recent years. In this paper we report on our studies of the characterisation and evolutionary aspects of gene sequences of the envelope glycoprotein of the prevalent Indian dengue virus type 1. Comparison with sequences from other countries shows that the envelope genes identified in India are closely related to strains from Malaysia. From the evolutionary point of view the envelope gene sequences of this dengue virus of India for past few years show that a marked mutational shift in the nucleotide sequences of the envelope gene have taken place from around the year 2000. Also, phylogenetic relationship with other three sera of dengue virus reported in India from 2005 shows that the dengue virus 1 is more closely related to dengue viruses 3 and 4 and relatively distantly to dengue virus 2.
{"title":"Diversity and evolution of the envelope gene of dengue virus type 1 circulating in India in recent times","authors":"S. Dey, A. Nandy, P. Nandy, Sukhen Das","doi":"10.1504/IJBRA.2015.073235","DOIUrl":"https://doi.org/10.1504/IJBRA.2015.073235","url":null,"abstract":"Dengue viral attacks have been reported in various parts of India in recent years. In this paper we report on our studies of the characterisation and evolutionary aspects of gene sequences of the envelope glycoprotein of the prevalent Indian dengue virus type 1. Comparison with sequences from other countries shows that the envelope genes identified in India are closely related to strains from Malaysia. From the evolutionary point of view the envelope gene sequences of this dengue virus of India for past few years show that a marked mutational shift in the nucleotide sequences of the envelope gene have taken place from around the year 2000. Also, phylogenetic relationship with other three sera of dengue virus reported in India from 2005 shows that the dengue virus 1 is more closely related to dengue viruses 3 and 4 and relatively distantly to dengue virus 2.","PeriodicalId":35444,"journal":{"name":"International Journal of Bioinformatics Research and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/IJBRA.2015.073235","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66702052","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 : 2015-11-01DOI: 10.1504/IJBRA.2015.073240
Gobind Ram, A. Sharma
Owing to their sessile nature, plants experience a variety of environmental stresses, but tolerance to these adverse conditions is a very complex phenomenon. Among all stresses, heat stress is the most important constraint that affects plant yield in rain-fed areas. To shed some light on candidate genes involved in heat stress, sequences potentially associated with heat shock resistance were retrieved and identified by in silico analysis using the public sequence database of various plants. A total of 30,000 EST sequences were mined and 24 putative ESTs associated with heat stress were picked up for further studies. In silico analysis revealed that all ESTs were linked with the HSP family. Gene Ontology (GO) analysis revealed that the deduced protein sequences of the heat-linked 24 ESTs were involved in various biological pathways regulating heat stress response. Hydropathy analysis revealed that all protein sequences were hydrophilic in nature. Based on the phylogenetic analysis, all HSP-related protein sequences were divided into seven groups. Analysis of cis-elements provides molecular evidence for the possible involvement of hydrophilic ESTs in the process of abiotic stress tolerance in sorghum. Based on these results, it was suggested that putative ESTs may play an important role in heat stress tolerance.
{"title":"In silico analysis, annotation and characterisation of putative ESTs from Sorghum bicolor associated with heat stress","authors":"Gobind Ram, A. Sharma","doi":"10.1504/IJBRA.2015.073240","DOIUrl":"https://doi.org/10.1504/IJBRA.2015.073240","url":null,"abstract":"Owing to their sessile nature, plants experience a variety of environmental stresses, but tolerance to these adverse conditions is a very complex phenomenon. Among all stresses, heat stress is the most important constraint that affects plant yield in rain-fed areas. To shed some light on candidate genes involved in heat stress, sequences potentially associated with heat shock resistance were retrieved and identified by in silico analysis using the public sequence database of various plants. A total of 30,000 EST sequences were mined and 24 putative ESTs associated with heat stress were picked up for further studies. In silico analysis revealed that all ESTs were linked with the HSP family. Gene Ontology (GO) analysis revealed that the deduced protein sequences of the heat-linked 24 ESTs were involved in various biological pathways regulating heat stress response. Hydropathy analysis revealed that all protein sequences were hydrophilic in nature. Based on the phylogenetic analysis, all HSP-related protein sequences were divided into seven groups. Analysis of cis-elements provides molecular evidence for the possible involvement of hydrophilic ESTs in the process of abiotic stress tolerance in sorghum. Based on these results, it was suggested that putative ESTs may play an important role in heat stress tolerance.","PeriodicalId":35444,"journal":{"name":"International Journal of Bioinformatics Research and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/IJBRA.2015.073240","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66702609","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 : 2015-11-01DOI: 10.1504/IJBRA.2015.073239
Anjali Singh, T. Pal
HIV-1 Protease (HIV-1 PR) enzymes are essential for accurate assembly and maturation of infectious HIV retroviruses. The significant role of HIV-1 protease in viral replication has made it a potential drug target. In the recent past, phytochemical Gallic Acid (GA) derivatives have been screened for protease inhibitor activity. The present work aims to design and evaluate potential GA-based HIV-1 PR phytoinhibitors by docking approach. The ligands were prepared by ChemDraw and docking was performed in HEX software. In this present study, one of the GA analogues (GA4) emerged as a potent drug candidate for HIV-1 PR inhibition, and docking results showed it to be comparable with anti-HIV drugs, darunavir and amprenavir. The GA4 derivative provided a lead for designing more effective HIV-1 PR inhibitors.
{"title":"Docking analysis of gallic acid derivatives as HIV-1 protease inhibitors","authors":"Anjali Singh, T. Pal","doi":"10.1504/IJBRA.2015.073239","DOIUrl":"https://doi.org/10.1504/IJBRA.2015.073239","url":null,"abstract":"HIV-1 Protease (HIV-1 PR) enzymes are essential for accurate assembly and maturation of infectious HIV retroviruses. The significant role of HIV-1 protease in viral replication has made it a potential drug target. In the recent past, phytochemical Gallic Acid (GA) derivatives have been screened for protease inhibitor activity. The present work aims to design and evaluate potential GA-based HIV-1 PR phytoinhibitors by docking approach. The ligands were prepared by ChemDraw and docking was performed in HEX software. In this present study, one of the GA analogues (GA4) emerged as a potent drug candidate for HIV-1 PR inhibition, and docking results showed it to be comparable with anti-HIV drugs, darunavir and amprenavir. The GA4 derivative provided a lead for designing more effective HIV-1 PR inhibitors.","PeriodicalId":35444,"journal":{"name":"International Journal of Bioinformatics Research and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/IJBRA.2015.073239","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66702123","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 : 2015-09-01DOI: 10.1504/IJBRA.2015.071944
N. Aggarwal, Bharti, R. Agrawal, S. Kumaran
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. The decision model is built with features so obtained, using a classifier. To evaluate the effectiveness of the proposed method, experiments are performed with four well-known classifiers on four data sets, built from a publicly available OASIS database. The performance is evaluated in terms of sensitivity, specificity and classification accuracy. It was observed that the proposed method outperforms existing methods in terms of all three performance measures. This is further validated with statistical tests.
{"title":"A combination of dual-tree discrete wavelet transform and minimum redundancy maximum relevance method for diagnosis of Alzheimer's disease","authors":"N. Aggarwal, Bharti, R. Agrawal, S. Kumaran","doi":"10.1504/IJBRA.2015.071944","DOIUrl":"https://doi.org/10.1504/IJBRA.2015.071944","url":null,"abstract":"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. The decision model is built with features so obtained, using a classifier. To evaluate the effectiveness of the proposed method, experiments are performed with four well-known classifiers on four data sets, built from a publicly available OASIS database. The performance is evaluated in terms of sensitivity, specificity and classification accuracy. It was observed that the proposed method outperforms existing methods in terms of all three performance measures. This is further validated with statistical tests.","PeriodicalId":35444,"journal":{"name":"International Journal of Bioinformatics Research and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/IJBRA.2015.071944","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66702006","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 : 2015-09-01DOI: 10.1504/IJBRA.2015.071945
Shipra Gupta, G. Misra, S. Khurana
Bioinformatics is a multidisciplinary science that solves and analyzes biological problems. With the quantum explosion in biomedical data, the demand of bioinformatics has increased gradually. Present paper provides an overview of various ways through which the biologists or biological researchers in the domain of neurology, structural and functional biology, evolutionary biology, clinical science, etc., use bioinformatics applications for data analysis to summarise their research. A new perspective is used to classify the knowledge available in the field thus will help general audience to understand the application of bioinformatics.
{"title":"Bioinformatics: promises and progress","authors":"Shipra Gupta, G. Misra, S. Khurana","doi":"10.1504/IJBRA.2015.071945","DOIUrl":"https://doi.org/10.1504/IJBRA.2015.071945","url":null,"abstract":"Bioinformatics is a multidisciplinary science that solves and analyzes biological problems. With the quantum explosion in biomedical data, the demand of bioinformatics has increased gradually. Present paper provides an overview of various ways through which the biologists or biological researchers in the domain of neurology, structural and functional biology, evolutionary biology, clinical science, etc., use bioinformatics applications for data analysis to summarise their research. A new perspective is used to classify the knowledge available in the field thus will help general audience to understand the application of bioinformatics.","PeriodicalId":35444,"journal":{"name":"International Journal of Bioinformatics Research and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/IJBRA.2015.071945","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66702018","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 : 2015-09-01DOI: 10.1504/IJBRA.2015.071938
Herbert H. Tsang, K. Wiese
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. We measured the sensitivity and specificity of nine prediction algorithms. Three of these are dynamic programming algorithms: Pseudoknot (pknotsRE), NUPACK, and pknotsRG-mfe. One is using the statistical clustering approach: Sfold and the other five are heuristic algorithms: SARNA-Predict-pk, ILM, STAR, IPknot and HotKnots algorithms. The results presented in this paper demonstrate that SARNA-Predict-pk can out-perform other state-of-the-art algorithms in terms of prediction accuracy. This supports the use of the proposed method on pseudoknotted RNA secondary structure prediction of other known structures.
{"title":"A permutation based simulated annealing algorithm to predict pseudoknotted RNA secondary structures","authors":"Herbert H. Tsang, K. Wiese","doi":"10.1504/IJBRA.2015.071938","DOIUrl":"https://doi.org/10.1504/IJBRA.2015.071938","url":null,"abstract":"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. We measured the sensitivity and specificity of nine prediction algorithms. Three of these are dynamic programming algorithms: Pseudoknot (pknotsRE), NUPACK, and pknotsRG-mfe. One is using the statistical clustering approach: Sfold and the other five are heuristic algorithms: SARNA-Predict-pk, ILM, STAR, IPknot and HotKnots algorithms. The results presented in this paper demonstrate that SARNA-Predict-pk can out-perform other state-of-the-art algorithms in terms of prediction accuracy. This supports the use of the proposed method on pseudoknotted RNA secondary structure prediction of other known structures.","PeriodicalId":35444,"journal":{"name":"International Journal of Bioinformatics Research and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/IJBRA.2015.071938","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66702443","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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