Pub Date : 2007-12-01DOI: 10.1142/9781848161092_0016
Bin Ma, Hongyi Yao
Spaced seed is a lter method invented to eciently identify the regions of interest in similarity searches. It is now well known that certain spaced seeds hit (detect) a randomly sampled similarity region with higher probabilities than the others. Assume each position of the similarity region is identity with probability p independently. The seed optimization problem seeks for the optimal seed achieving the highest hit probability with given length and weight. Despite that the problem was previously shown not to be NP-hard, in practice it seems dicult to solve. The only algorithm known to compute the optimal seed is still exhaustive search in exponential time. In this article we put some insight into the hardness of the seed design problem by demonstrating the relation between the seed optimization problem and the optimal Golomb ruler design problem, which is a well known dicult problem in combinatorial design.
{"title":"Seed Optimization Is No Easier than Optimal Golomb Ruler Design","authors":"Bin Ma, Hongyi Yao","doi":"10.1142/9781848161092_0016","DOIUrl":"https://doi.org/10.1142/9781848161092_0016","url":null,"abstract":"Spaced seed is a lter method invented to eciently identify the regions of interest in similarity searches. It is now well known that certain spaced seeds hit (detect) a randomly sampled similarity region with higher probabilities than the others. Assume each position of the similarity region is identity with probability p independently. The seed optimization problem seeks for the optimal seed achieving the highest hit probability with given length and weight. Despite that the problem was previously shown not to be NP-hard, in practice it seems dicult to solve. The only algorithm known to compute the optimal seed is still exhaustive search in exponential time. In this article we put some insight into the hardness of the seed design problem by demonstrating the relation between the seed optimization problem and the optimal Golomb ruler design problem, which is a well known dicult problem in combinatorial design.","PeriodicalId":74513,"journal":{"name":"Proceedings of the ... Asia-Pacific bioinformatics conference","volume":"454 1","pages":"133-144"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81009294","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 : 2007-12-01DOI: 10.1142/9781848161092_0037
T. Alioto, R. Guigó, E. Picardi, G. Pesole
We have developed a generic framework for combining introns from genomicly aligned expressed–sequence–tag clusters with a set of exon predictions to produce alternative transcript predictions. Our current implementation uses ASPIC to generate alternative transcripts from EST mappings. Introns from ASPIC and a set of gene predictions from many diverse gene prediction programs are given to the gene prediction combiner GenePC which then generates alternative consensus splice forms. We evaluated our method on the ENCODE regions of the human genome. In general we see a marked improvement in transcript-level sensitivity due to the fact that more than one transcript per gene may now be predicted. GenePC, which alone is highly specific at the transcript level, balances the lower specificity of ASPIC.
{"title":"GenePC and ASPIC Integrate Gene Predictions with Expressed Sequence Alignments To Predict Alternative Transcripts","authors":"T. Alioto, R. Guigó, E. Picardi, G. Pesole","doi":"10.1142/9781848161092_0037","DOIUrl":"https://doi.org/10.1142/9781848161092_0037","url":null,"abstract":"We have developed a generic framework for combining introns from genomicly aligned expressed–sequence–tag clusters with a set of exon predictions to produce alternative transcript predictions. Our current implementation uses ASPIC to generate alternative transcripts from EST mappings. Introns from ASPIC and a set of gene predictions from many diverse gene prediction programs are given to the gene prediction combiner GenePC which then generates alternative consensus splice forms. We evaluated our method on the ENCODE regions of the human genome. In general we see a marked improvement in transcript-level sensitivity due to the fact that more than one transcript per gene may now be predicted. GenePC, which alone is highly specific at the transcript level, balances the lower specificity of ASPIC.","PeriodicalId":74513,"journal":{"name":"Proceedings of the ... Asia-Pacific bioinformatics conference","volume":"29 1","pages":"363-372"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89273437","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 : 2007-12-01DOI: 10.1142/9781848161092_0038
Yannick Haudry, C. Ong, L. Ettwiller, Hugo Bérubé, Ivica Letunic, M. Kapushesky, Paul-Daniel Weeber, Xi Wang, J. Gagneur, Charles Girardot, D. Arendt, P. Bork, A. Brazma, E. Furlong, J. Wittbrodt, T. Henrich
High-resolution spatial information on gene expression over time can be acquired through whole mount in-situ hybridisation experiments in animal model species such as fish, fly or mouse. Expression patterns of many genes have been studied and data has been integrated into dedicated model organism databases like ZFIN for zebrafish, MEPD for medaka, BDGP for drosophila or MGI for mouse. Nevertheless, a central repository that allows users to query and compare gene expression patterns across different species has not yet been established. For this purpose we have integrated gene expression data for zebrafish, medaka, drosophila and mouse into a central public repository named 4DXpress (http://ani.embl.de/4DXpress). 4DXpress allows to query anatomy ontology based expression annotations across species and quickly jump from one gene to the orthologs in other species based on ensembl-compara relationships. We have set up a linked resource for microarray data at ArrayExpress. In addition we have mapped developmental stages between the species to be able to compare corresponding developmental time phases. We have used clustering algorithms to classify genes based on their expression pattern annotations. To illustrate the use of 4DXpress we systematically analysed the relationships between conserved regulatory inputs and spatio-temporal gene expression derived from 4DXpress and found significant correlation between expression patterns of genes predicted to have similar regulatory elements in their promoters.
{"title":"Comparing and Analysing Gene Expression Patterns Across Animal Species Using 4DXpress","authors":"Yannick Haudry, C. Ong, L. Ettwiller, Hugo Bérubé, Ivica Letunic, M. Kapushesky, Paul-Daniel Weeber, Xi Wang, J. Gagneur, Charles Girardot, D. Arendt, P. Bork, A. Brazma, E. Furlong, J. Wittbrodt, T. Henrich","doi":"10.1142/9781848161092_0038","DOIUrl":"https://doi.org/10.1142/9781848161092_0038","url":null,"abstract":"High-resolution spatial information on gene expression over time can be acquired through whole mount in-situ hybridisation experiments in animal model species such as fish, fly or mouse. Expression patterns of many genes have been studied and data has been integrated into dedicated model organism databases like ZFIN for zebrafish, MEPD for medaka, BDGP for drosophila or MGI for mouse. Nevertheless, a central repository that allows users to query and compare gene expression patterns across different species has not yet been established. For this purpose we have integrated gene expression data for zebrafish, medaka, drosophila and mouse into a central public repository named 4DXpress (http://ani.embl.de/4DXpress). 4DXpress allows to query anatomy ontology based expression annotations across species and quickly jump from one gene to the orthologs in other species based on ensembl-compara relationships. We have set up a linked resource for microarray data at ArrayExpress. In addition we have mapped developmental stages between the species to be able to compare corresponding developmental time phases. We have used clustering algorithms to classify genes based on their expression pattern annotations. To illustrate the use of 4DXpress we systematically analysed the relationships between conserved regulatory inputs and spatio-temporal gene expression derived from 4DXpress and found significant correlation between expression patterns of genes predicted to have similar regulatory elements in their promoters.","PeriodicalId":74513,"journal":{"name":"Proceedings of the ... Asia-Pacific bioinformatics conference","volume":"62 1","pages":"373-382"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82347901","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 : 2007-12-01DOI: 10.1142/9781848161092_0001
A. Dress
of D is an R-tree. (ii) There exists a tree (V,E) whose vertex set V contains X, and an edge weighting ` : E → R that assigns a positive length `(e) to each edge e in E, such that D is the restriction of X to the shortest-path metric induced on V. (iii) There exists a map w : S(X) → R≥0 from the set S(X) of all bi-partitions or splits of X into the set R≥0 of non-negative real numbers such that, given any two splits S = {A,B} and S′ = {A′, B′} in S(X) with w(S), w(S′) 6= 0, at least one of the four intersections A ∩A′, B ∩A′, A ∩B′, and B ∩B′ is empty and D(x, y) = ∑ S∈S(X:x↔y) w(S) holds where S(X : x↔y) denotes the set of splits S = {A,B} ∈ S(X) that separate x and y. (iv) D(x, y)+D(u, v) ≤ max ( D(x, u)+D(y, v), D(x, v)+D(y, u) ) holds for all x, y, u, v ∈ X
(D)是一个r树。(ii)存在一棵树(V,E)，其顶点集V包含X，并且存在一个边权':E→R，该边权':E→R赋予E中的每条边E一个正长度' (E)，使得D是X对V诱导的最短路径度量的约束。(iii)存在一个映射w:S (X)→R≥0集合S (X)的所有bi-partitions或分裂X的非负实数集R≥0的,给定的任意两个分裂S = {A、B}和S ' ={一个“B”}S (X)和w (S), w(年代”)6 = 0,至少有一个的四个十字路口∩“,B∩,∩B, B和B∩”是空的和D (X, y) =∑∈年代(X, X↔y) w (S)认为,S (X): X↔y)表示的集合分裂S = {A、B}∈(X),独立的X和y。(iv) D (X, y) + D (u, v)≤马克斯(D (X, u) + D (y, v)、D (X, v) + D (y, u))拥有对所有的X, y, u, v∈X
{"title":"Recent Progress in Phylogenetic Combinatorics","authors":"A. Dress","doi":"10.1142/9781848161092_0001","DOIUrl":"https://doi.org/10.1142/9781848161092_0001","url":null,"abstract":"of D is an R-tree. (ii) There exists a tree (V,E) whose vertex set V contains X, and an edge weighting ` : E → R that assigns a positive length `(e) to each edge e in E, such that D is the restriction of X to the shortest-path metric induced on V. (iii) There exists a map w : S(X) → R≥0 from the set S(X) of all bi-partitions or splits of X into the set R≥0 of non-negative real numbers such that, given any two splits S = {A,B} and S′ = {A′, B′} in S(X) with w(S), w(S′) 6= 0, at least one of the four intersections A ∩A′, B ∩A′, A ∩B′, and B ∩B′ is empty and D(x, y) = ∑ S∈S(X:x↔y) w(S) holds where S(X : x↔y) denotes the set of splits S = {A,B} ∈ S(X) that separate x and y. (iv) D(x, y)+D(u, v) ≤ max ( D(x, u)+D(y, v), D(x, v)+D(y, u) ) holds for all x, y, u, v ∈ X","PeriodicalId":74513,"journal":{"name":"Proceedings of the ... Asia-Pacific bioinformatics conference","volume":"6 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82986766","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 : 2007-12-01DOI: 10.1142/9781848161092_0020
Yang Yang, Bao-Liang Lu, Wen-Yun Yang
Protein sequences contain great potential revealing protein function, structure families and evolution information. Classifying protein sequences into different functional groups or families based on their sequence patterns has attracted lots of research efforts in the last decade. A key issue of these classification systems is how to interpret and represent protein sequences, which largely determines the performance of classifiers. Inspired by text classification and Chinese word segmentation techniques, we propose a segmentation-based feature extraction method. The extracted features include selected words, i.e., substrings of the sequences, and also motifs specified in public database. They are segmented out and their occurrence frequencies are recorded as the feature vector values. We conducted experiments on two protein data sets. One is a set of SCOP families, and the other is GPCR family. Experiments in classification of SCOP protein families show that the proposed method not only results in an extremely condensed feature set but also achieves higher accuracy than the methods based on whole k-spectrum feature space. And it also performs comparably to the most powerful classifiers for GPCR level I and level II subfamily recognition with 92.6 and 88.8% accuracy, respectively.
{"title":"Classification of Protein Sequences Based on Word Segmentation Methods","authors":"Yang Yang, Bao-Liang Lu, Wen-Yun Yang","doi":"10.1142/9781848161092_0020","DOIUrl":"https://doi.org/10.1142/9781848161092_0020","url":null,"abstract":"Protein sequences contain great potential revealing protein function, structure families and evolution information. Classifying protein sequences into different functional groups or families based on their sequence patterns has attracted lots of research efforts in the last decade. A key issue of these classification systems is how to interpret and represent protein sequences, which largely determines the performance of classifiers. Inspired by text classification and Chinese word segmentation techniques, we propose a segmentation-based feature extraction method. The extracted features include selected words, i.e., substrings of the sequences, and also motifs specified in public database. They are segmented out and their occurrence frequencies are recorded as the feature vector values. We conducted experiments on two protein data sets. One is a set of SCOP families, and the other is GPCR family. Experiments in classification of SCOP protein families show that the proposed method not only results in an extremely condensed feature set but also achieves higher accuracy than the methods based on whole k-spectrum feature space. And it also performs comparably to the most powerful classifiers for GPCR level I and level II subfamily recognition with 92.6 and 88.8% accuracy, respectively.","PeriodicalId":74513,"journal":{"name":"Proceedings of the ... Asia-Pacific bioinformatics conference","volume":"26 1","pages":"177-186"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72818721","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 : 2007-12-01DOI: 10.1142/9781848161092_0018
Yuan-Peng Li, Bao-Liang Lu
The similarity of two gene products can be used to solve many problems in information biology. Since one gene product corresponds to several GO (Gene Ontology) terms, one way to calculate the gene product similarity is to use the similarity of their GO terms. This GO term similarity can be defined as the semantic similarity on the GO graph. There are many kinds of similarity definitions of two GO terms, but the information of the GO graph is not used efficiently. This paper presents a new way to mine more information of the GO graph by regarding edge as information content and using the information of negation on the semantic graph. A simple experiment is conducted and, as a result, the accuracy increased by 8.3 percent in average, compared with the traditional method which uses node as information source.
{"title":"Semantic Similarity Definition over Gene Ontology by Further Mining of the Information Content","authors":"Yuan-Peng Li, Bao-Liang Lu","doi":"10.1142/9781848161092_0018","DOIUrl":"https://doi.org/10.1142/9781848161092_0018","url":null,"abstract":"The similarity of two gene products can be used to solve many problems in information biology. Since one gene product corresponds to several GO (Gene Ontology) terms, one way to calculate the gene product similarity is to use the similarity of their GO terms. This GO term similarity can be defined as the semantic similarity on the GO graph. There are many kinds of similarity definitions of two GO terms, but the information of the GO graph is not used efficiently. This paper presents a new way to mine more information of the GO graph by regarding edge as information content and using the information of negation on the semantic graph. A simple experiment is conducted and, as a result, the accuracy increased by 8.3 percent in average, compared with the traditional method which uses node as information source.","PeriodicalId":74513,"journal":{"name":"Proceedings of the ... Asia-Pacific bioinformatics conference","volume":"30 1","pages":"155-164"},"PeriodicalIF":0.0,"publicationDate":"2007-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87979725","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 : 2007-01-01DOI: 10.1142/9781860947995_0008
Hongya Zhao, Alan Wee-Chung Liew, Hong Yan
In this paper, we present a new biclustering algorithm to provide the geometrical interpretation of similar microarray gene expression profiles. Different from standard clustering analyses, biclustering methodology can perform simultaneous classification on the row and column dimensions of a data matrix. The main object of the strategy is to reveal the submatrix, in which a subset of genes exhibits a consistent pattern over a subset of conditions. However, the search for such subsets is a computationally complex task. We propose a new algorithm, based on the Hough transform in the column-pair space to perform pattern identification. The algorithm is especially suitable for the biclustering analysis of large-scale microarray data. Our simulation studies show that the method is robust to noise and computationally efficient. Furthermore, we have applied it to a large database of gene expression profiles of multiple human organs and the resulting biclusters show clear biological meanings.
{"title":"A New Strategy of Geometrical Biclustering for Microarray Data Analysis","authors":"Hongya Zhao, Alan Wee-Chung Liew, Hong Yan","doi":"10.1142/9781860947995_0008","DOIUrl":"https://doi.org/10.1142/9781860947995_0008","url":null,"abstract":"In this paper, we present a new biclustering algorithm to provide the geometrical interpretation of similar microarray gene expression profiles. Different from standard clustering analyses, biclustering methodology can perform simultaneous classification on the row and column dimensions of a data matrix. The main object of the strategy is to reveal the submatrix, in which a subset of genes exhibits a consistent pattern over a subset of conditions. However, the search for such subsets is a computationally complex task. We propose a new algorithm, based on the Hough transform in the column-pair space to perform pattern identification. The algorithm is especially suitable for the biclustering analysis of large-scale microarray data. Our simulation studies show that the method is robust to noise and computationally efficient. Furthermore, we have applied it to a large database of gene expression profiles of multiple human organs and the resulting biclusters show clear biological meanings.","PeriodicalId":74513,"journal":{"name":"Proceedings of the ... Asia-Pacific bioinformatics conference","volume":"39 1","pages":"47-56"},"PeriodicalIF":0.0,"publicationDate":"2007-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74905469","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 : 2007-01-01DOI: 10.1142/9781860947995_0032
B. Shan, B. Ma, Kaizhong Zhang, G. Lajoie
Determining glycan structures is vital to comprehend cell-matrix, cell-cell, and even intracellular biological events. Glycan structure sequencing, which is to determine the primary structure of a glycan using MS/MS spectrometry, remains one of the most important tasks in proteomics. Analogous to the peptide de novo sequencing, the glycan de novo sequencing is to determine the structure without the aid of a known glycan database. We show in this paper that glycan de novo sequencing is NP-hard. We then provide a heuristic algorithm and develop a software program to solve the problem in practical cases. Experiments on real MS/MS data of glycopeptides demonstrate that our heuristic algorithm gives satisfactory results on practical data.
{"title":"Complexities and Algorithms for Glycan Structure Sequencing using Tandem Mass Spectrometry","authors":"B. Shan, B. Ma, Kaizhong Zhang, G. Lajoie","doi":"10.1142/9781860947995_0032","DOIUrl":"https://doi.org/10.1142/9781860947995_0032","url":null,"abstract":"Determining glycan structures is vital to comprehend cell-matrix, cell-cell, and even intracellular biological events. Glycan structure sequencing, which is to determine the primary structure of a glycan using MS/MS spectrometry, remains one of the most important tasks in proteomics. Analogous to the peptide de novo sequencing, the glycan de novo sequencing is to determine the structure without the aid of a known glycan database. We show in this paper that glycan de novo sequencing is NP-hard. We then provide a heuristic algorithm and develop a software program to solve the problem in practical cases. Experiments on real MS/MS data of glycopeptides demonstrate that our heuristic algorithm gives satisfactory results on practical data.","PeriodicalId":74513,"journal":{"name":"Proceedings of the ... Asia-Pacific bioinformatics conference","volume":"50 1","pages":"297-306"},"PeriodicalIF":0.0,"publicationDate":"2007-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75004251","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 : 2007-01-01DOI: 10.1142/9781860947995_0020
Gaofeng Huang, P. Jeavons, D. Kwiatkowski
A Single Nucleotide Polymorphism (SNP) is a small DNA variation which occurs naturally between dierent individuals of the same species. Some combinations of SNPs in the human genome are known to increase the risk of certain complex genetic diseases. This paper formulates the problem of identifying such disease-associated SNP motifs as a combinatorial optimization problem and shows it to be NP-hard. Both exact and heuristic approaches for this problem are developed and tested on simulated data and real clinical data. Computational results are given to demonstrate that these approaches are suciently eective to support ongoing biological research.
{"title":"Exact and Heuristic Approaches for Identifying Disease-Associated SNP Motifs","authors":"Gaofeng Huang, P. Jeavons, D. Kwiatkowski","doi":"10.1142/9781860947995_0020","DOIUrl":"https://doi.org/10.1142/9781860947995_0020","url":null,"abstract":"A Single Nucleotide Polymorphism (SNP) is a small DNA variation which occurs naturally between dierent individuals of the same species. Some combinations of SNPs in the human genome are known to increase the risk of certain complex genetic diseases. This paper formulates the problem of identifying such disease-associated SNP motifs as a combinatorial optimization problem and shows it to be NP-hard. Both exact and heuristic approaches for this problem are developed and tested on simulated data and real clinical data. Computational results are given to demonstrate that these approaches are suciently eective to support ongoing biological research.","PeriodicalId":74513,"journal":{"name":"Proceedings of the ... Asia-Pacific bioinformatics conference","volume":"30 1","pages":"175-184"},"PeriodicalIF":0.0,"publicationDate":"2007-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77213011","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 : 2007-01-01DOI: 10.1142/9781860947995_0007
Xudong Xie, Shuanhu Wu, K. Lam, Hong Yan
In this paper, an effective promoter detection algorithm, which is called PromoterExplorer, is proposed. In our approach, various features, i.e. local distribution of pentamers, positional CpG island features and digitized DNA sequence, are combined to build a high-dimensional input vector. A cascade AdaBoost based learning procedure is adopted to select the most “informative” or “discriminating” features to build a sequence of weak classifiers. A number of weak classifiers construct a strong classifier, which can achieve a better performance. In order to reduce the false positive, a cascade structure is used for detection. PromoterExplorer is tested based on large-scale DNA sequences from different databases, including EPD, Genbank and human chromosome 22. The proposed method consistently outperforms PromoterInspector and Dragon Promoter Finder.
{"title":"An Effective Promoter Detection Method using the Adaboost Algorithm","authors":"Xudong Xie, Shuanhu Wu, K. Lam, Hong Yan","doi":"10.1142/9781860947995_0007","DOIUrl":"https://doi.org/10.1142/9781860947995_0007","url":null,"abstract":"In this paper, an effective promoter detection algorithm, which is called PromoterExplorer, is proposed. In our approach, various features, i.e. local distribution of pentamers, positional CpG island features and digitized DNA sequence, are combined to build a high-dimensional input vector. A cascade AdaBoost based learning procedure is adopted to select the most “informative” or “discriminating” features to build a sequence of weak classifiers. A number of weak classifiers construct a strong classifier, which can achieve a better performance. In order to reduce the false positive, a cascade structure is used for detection. PromoterExplorer is tested based on large-scale DNA sequences from different databases, including EPD, Genbank and human chromosome 22. The proposed method consistently outperforms PromoterInspector and Dragon Promoter Finder.","PeriodicalId":74513,"journal":{"name":"Proceedings of the ... Asia-Pacific bioinformatics conference","volume":"64 1","pages":"37-46"},"PeriodicalIF":0.0,"publicationDate":"2007-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83453504","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：