In Silico Biology最新文献

{"title":"Analysis of disordered regions in protein kinase subfamilies of Homo sapiens and Coenorhabditis elegans","authors":"K. Kurup, J. Natarajan","doi":"10.1145/1722024.1722028","DOIUrl":"https://doi.org/10.1145/1722024.1722028","url":null,"abstract":"Protein kinase is a kinase enzyme that modifies other proteins by chemically adding phosphate groups to them. In this work, first the protein kinases of Coenorhabditis elegans and Homo sapiens with three or more common domain were grouped and disorder regions of protein kinases in each group were predicted. Then the similarities of the disordered regions among the organisms were found. Linear motifs present in these similar disorder regions were identified and tested for their conservation in both Homo sapiens and Coenorhabditis elegans. It is found that, though the similarities in disorder regions are high, the linear motifs are not conserved much in these distantly related organisms.","PeriodicalId":39379,"journal":{"name":"In Silico Biology","volume":"1 1","pages":"3"},"PeriodicalIF":0.0,"publicationDate":"2010-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/1722024.1722028","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64107973","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}

{"title":"Iterative visual clustering for unstructured text mining","authors":"Qian You, S. Fang, P. Ebright","doi":"10.1145/1722024.1722054","DOIUrl":"https://doi.org/10.1145/1722024.1722054","url":null,"abstract":"This paper proposes the iterative visual clustering (IVC) on unstructured text sequences to form and evaluate keyword clusters, based on which users can use visual analysis, domain knowledge to discover knowledge in the text. The text sequence data are broken down into a list representative keywords after textual evaluation, and the keywords are then grouped to form keyword clusters via an iterative stochastic process and are visualized as distributions over the time lines. The visual evaluation model provides shape evaluations as quantitative tools and users' interactions as qualitative tools to visually investigate the trends, patterns represented by the keyword clusters' distributions. The keyword clustering model, guided by the feedback of visual evaluations, step-wisely enumerates newer generations of keyword clusters and their patterns, therefore narrows down the search space. Then the proposed IVC is applied onto nursing narratives and is able to identify interesting keyword clusters implying hidden knowledge regarding to the working patterns and environment of registered nurses. The loop of producing next generation of keyword clusters in IVC is driven and controlled by users' perception, domain knowledge and interactions, and it is also guided by a stochastic search model. So both semantic and distribution features enable IVC to have significant applications as a text mining tool, on many other data sets, such as biomedical literatures.","PeriodicalId":39379,"journal":{"name":"In Silico Biology","volume":"1 1","pages":"26"},"PeriodicalIF":0.0,"publicationDate":"2010-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/1722024.1722054","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64108347","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}

{"title":"DNA sequence representation methods","authors":"G. Santhosh Kumar, S. Shiji","doi":"10.1145/1722024.1722073","DOIUrl":"https://doi.org/10.1145/1722024.1722073","url":null,"abstract":"DNA sequence representation methods are used to denote a gene structure effectively and help in similarities/dissimilarities analysis of coding sequences. Many different kinds of representations have been proposed in the literature. They can be broadly classified into Numerical, Graphical, Geometrical and Hybrid representation methods. DNA structure and function analysis are made easy with graphical and geometrical representation methods since it gives visual representation of a DNA structure. In numerical method, numerical values are assigned to a sequence and digital signal processing methods are used to analyze the sequence. Hybrid approaches are also reported in the literature to analyze DNA sequences. This paper reviews the latest developments in DNA Sequence representation methods. We also present a taxonomy of various methods. A comparison of these methods where ever possible is also done.","PeriodicalId":39379,"journal":{"name":"In Silico Biology","volume":"1 1","pages":"42"},"PeriodicalIF":0.0,"publicationDate":"2010-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/1722024.1722073","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64108531","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}

{"title":"Conserved orthology in mitochondrial genomes of distantly related nematodes","authors":"P. Nima, A. Riju, N. Reena, S. Eapen","doi":"10.1145/1722024.1722075","DOIUrl":"https://doi.org/10.1145/1722024.1722075","url":null,"abstract":"Identification of orthologous segments plays a very important role in comparative genomics studies. In the present study, we have identified orthologous segments shared between Radopholus similis and 15 other nematodes. Complete genomes of 16 nematodes were used for the study. OSfinder was used to find the orthologous segments shared between R. similis and other 15 nematodes. Orthologous segments were visualized with the help of GTK powered Murasaki Visualizer (GMV) programme. Extremely AT rich genome of the burrowing nematode R. similis, which has the largest mitochondrial genome, was found to have orthologous segments from start position, 4 to end position 16791 with 15 nematodes. Brugia malayi, Dirofilaria immitis, Onchocerca volvulus, and Xiphinema americanum share similar orthologous segment with that of R. similis. The mitochondrial genome analysis revealed the presence of conserved gene locations in mitochondrion and the close evolutionary relationship of nematodes belonging to different clades and different parasitic habitats. This study has many practical implications like reconstruction of ancestral genome of nematode and calculation of evolutionary time.","PeriodicalId":39379,"journal":{"name":"In Silico Biology","volume":"1 1","pages":"44"},"PeriodicalIF":0.0,"publicationDate":"2010-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/1722024.1722075","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64108625","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}

{"title":"Chronological order of reversal events on Rickettsia genus","authors":"Christian Baudet, Zanoni Dias","doi":"10.1145/1722024.1722026","DOIUrl":"https://doi.org/10.1145/1722024.1722026","url":null,"abstract":"Traditional algorithms for sorting permutations by signed reversals output one solution while the solution space can be huge. The enumeration of traces of solutions for this problem can be a powerful tool to help the study of rear-rangement scenarios which only include reversals. Through the analysis of the permutations of six members of the Rickettsia genus in relation with their common ancestral, we were able to produce all possible scenarios and infer some chronological order over the reversal events that occurred during the evolution of these species. Our results matched with the scenario proposed in the literature.","PeriodicalId":39379,"journal":{"name":"In Silico Biology","volume":"1 1","pages":"1"},"PeriodicalIF":0.0,"publicationDate":"2010-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/1722024.1722026","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64107908","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}

{"title":"In silico approach to discover multi-target-directed ligands for the treatment of Alzheimer's disease","authors":"A. Tyagi, Shikha Gupta, C. G. Mohan","doi":"10.1145/1722024.1722032","DOIUrl":"https://doi.org/10.1145/1722024.1722032","url":null,"abstract":"Multi-target directed (MTD) drugs have been found to be very effective in controlling neurodegenerative diseases. We have developed an in silico strategy to screen molecules for both AChE and BACE-1 enzyme dual inhibition. Pharmacophore model development of known AChE and BACE-1 inhibitors were used for sequential virtual screening (VS) of three different small molecule databases. Eight new MTD ligands were identified using these sequential VS techniques. Among these molecule 2 obtained from NCI database was found to be most promising hit on the basis of Gold docking score and Log-BB value, and which could be further explored for experimental analysis. Our present strategy for identification of the AChE and BACE-1 dual inhibitors might be one of the promising directions to discover better leads for the treatment of Alzheimer's disease.","PeriodicalId":39379,"journal":{"name":"In Silico Biology","volume":"1 1","pages":"6"},"PeriodicalIF":0.0,"publicationDate":"2010-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/1722024.1722032","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64108052","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}

{"title":"An integrated multistep prediction system based on wavelet filter analysis and improved instance based learning (IIBL)","authors":"M. Pushpalatha, N. Nalini","doi":"10.1145/1722024.1722078","DOIUrl":"https://doi.org/10.1145/1722024.1722078","url":null,"abstract":"In this paper we present a novel wavelet based forecast model integrating wavelet filters for denoising and Improved Instance based learning approach. The proposed model implements a novel technique that extends the nearest neighbor algorithm to include the concept of pattern matching so as to identify similar instances thus implementing a nonparametric regression approach. A hybrid distance measure combining correlation and euclidean distance to select similar instances has been proposed. To illustrate the performance and effectiveness of the proposed model simulations using Mackey-Glass benchmark series and a real time Nord pool time series used in day-ahead forecast of electricity prices have been carried out. We apply a comprehensive set of non redundant orthogonal wavelet transforms for individual wavelet subband to denoise the signal. The analysis of simulations demonstrate that the proposed wavelet based - IIBL model results in accurate predictions and encouraging results for both the series.","PeriodicalId":39379,"journal":{"name":"In Silico Biology","volume":"1 1","pages":"47"},"PeriodicalIF":0.0,"publicationDate":"2010-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/1722024.1722078","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64108179","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}

{"title":"Inference of gene regulatory network using modified genetic algorithm","authors":"S. Seema, K. Ramanatha","doi":"10.1145/1722024.1722049","DOIUrl":"https://doi.org/10.1145/1722024.1722049","url":null,"abstract":"The major challenge of inferring genetic network is mining the dependencies and regulating relationship among genes. The paper tries to address this problem using Genetic Algorithms to infer the transcription regulatory network. While Genetic Algorithms(GA) are able to infer smaller networks with good sensitivity and precision, several generations and much greater computation power are required to infer regulatory networks from realistic data. Here a modified GA that uses statistical techniques to narrow the search space is proposed. The system is tested on the publicly available datasets of the Hela cell cycle and Yeast cell cycle. The results have been compared with regulatory networks inferred by using second order differential equations. It is found that the sensitivity and specificity are at par with differential equation method and has a considerable improvement in comparison with the Basic GA method.","PeriodicalId":39379,"journal":{"name":"In Silico Biology","volume":"1 1","pages":"21"},"PeriodicalIF":0.0,"publicationDate":"2010-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/1722024.1722049","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64108255","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}

{"title":"Exhaustive analysis of the modular structure of the spliceosomal assembly network: a Petri net approach.","authors":"Ralf H Bortfeldt,&nbsp;Stefan Schuster,&nbsp;Ina Koch","doi":"10.3233/ISB-2010-0419","DOIUrl":"https://doi.org/10.3233/ISB-2010-0419","url":null,"abstract":"<p><p>Spliceosomes are macro-complexes involving hundreds of proteins with many functional interactions. Spliceosome assembly belongs to the key processes that enable splicing of mRNA and modulate alternative splicing. A detailed list of factors involved in spliceosomal reactions has been assorted over the past decade, but, their functional interplay is often unknown and most of the present biological models cover only parts of the complete assembly process. It is a challenging task to build a computational model that integrates dispersed knowledge and combines a multitude of reaction schemes proposed earlier.Because for most reactions involved in spliceosome assembly kinetic parameters are not available, we propose a discrete modeling using Petri nets, through which we are enabled to get insights into the system's behavior via computation of structural and dynamic properties. In this paper, we compile and examine reactions from experimental reports that contribute to a functional spliceosome. All these reactions form a network, which describes the inventory and conditions necessary to perform the splicing process. The analysis is mainly based on system invariants. Transition invariants (T-invariants) can be interpreted as signaling routes through the network. Due to the huge number of T-invariants that arise with increasing network size and complexity, maximal common transition sets (MCTS) and T-clusters were used for further analysis. Additionally, we introduce a false color map representation, which allows a quick survey of network modules and the visual detection of single reactions or reaction sequences, which participate in more than one signaling route. We designed a structured model of spliceosome assembly, which combines the demands on a platform that i) can display involved factors and concurrent processes, ii) offers the possibility to run computational methods for knowledge extraction, and iii) is successively extendable as new insights into spliceosome function are reported by experimental reports. The network consists of 161 transitions (reactions) and 140 places (reactants). All reactions are part of at least one of the 71 T-invariants. These T-invariants define pathways, which are in good agreement with the current knowledge and known hypotheses on reaction sequences during spliceosome assembly, hence contributing to a functional spliceosome. We demonstrate that present knowledge, in particular of the initial part of the assembly process, describes parallelism and interaction of signaling routes, which indicate functional redundancy and reflect the dependency of spliceosome assembly initiation on different cellular conditions. The complexity of the network is further increased by two switches, which introduce alternative routes during A-complex formation in early spliceosome assembly and upon transition from the B-complex to the C-complex. By compiling known reactions into a complete network, the combinatorial nature of invariant ","PeriodicalId":39379,"journal":{"name":"In Silico Biology","volume":"10 1","pages":"89-123"},"PeriodicalIF":0.0,"publicationDate":"2010-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/ISB-2010-0419","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30512780","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}

{"title":"Cell Illustrator 4.0: a computational platform for systems biology.","authors":"Masao Nagasaki,&nbsp;Ayumu Saito,&nbsp;Euna Jeong,&nbsp;Chen Li,&nbsp;Kaname Kojima,&nbsp;Emi Ikeda,&nbsp;Satoru Miyano","doi":"10.3233/ISB-2010-0415","DOIUrl":"https://doi.org/10.3233/ISB-2010-0415","url":null,"abstract":"<p><p>Cell Illustrator is a software platform for Systems Biology that uses the concept of Petri net for modeling and simulating biopathways. It is intended for biological scientists working at bench. The latest version of Cell Illustrator 4.0 uses Java Web Start technology and is enhanced with new capabilities, including: automatic graph grid layout algorithms using ontology information; tools using Cell System Markup Language (CSML) 3.0 and Cell System Ontology 3.0; parameter search module; high-performance simulation module; CSML database management system; conversion from CSML model to programming languages (FORTRAN, C, C++, Java, Python and Perl); import from SBML, CellML, and BioPAX; and, export to SVG and HTML. Cell Illustrator employs an extension of hybrid Petri net in an object-oriented style so that biopathway models can include objects such as DNA sequence, molecular density, 3D localization information, transcription with frame-shift, translation with codon table, as well as biochemical reactions.</p>","PeriodicalId":39379,"journal":{"name":"In Silico Biology","volume":"10 1","pages":"5-26"},"PeriodicalIF":0.0,"publicationDate":"2010-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/ISB-2010-0415","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30513843","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}