Pub Date : 2010-12-01DOI: 10.1109/BIBM.2010.5706624
Huiru Zheng, F. Azuaje, Haiying Wang
In recent years there has been a growing trend towards the adoption of ontologies to support comprehensive, large-scale functional genomics research. This paper introduces seGOsa, a user-friendly cross-platform system to support large-scale assessment of Gene Ontology (GO)-driven similarity among gene products. Using information-theoretic approaches, the system exploits both topological features of the GO (i.e., between-term relationships in the hierarchy) and statistical features of the model organism databases annotated to the GO (i.e., term frequency) to assess functional similarity among gene products. Based on the assumption that the more information two terms share in common, the more similar they are, three GO-driven similarity measures (Resnik's, Lin's and Jiang's metrics) have been implemented to measure between-term similarity within each of the GO hierarchies. Meanwhile, seGOsa offers two approaches (simple and highest average similarity) to assessing the similarity between gene products based on the aggregation of between-term similarities. The program is freely available for non-profit use on request from the authors.
{"title":"seGOsa: Software environment for gene ontology-driven similarity assessment","authors":"Huiru Zheng, F. Azuaje, Haiying Wang","doi":"10.1109/BIBM.2010.5706624","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706624","url":null,"abstract":"In recent years there has been a growing trend towards the adoption of ontologies to support comprehensive, large-scale functional genomics research. This paper introduces seGOsa, a user-friendly cross-platform system to support large-scale assessment of Gene Ontology (GO)-driven similarity among gene products. Using information-theoretic approaches, the system exploits both topological features of the GO (i.e., between-term relationships in the hierarchy) and statistical features of the model organism databases annotated to the GO (i.e., term frequency) to assess functional similarity among gene products. Based on the assumption that the more information two terms share in common, the more similar they are, three GO-driven similarity measures (Resnik's, Lin's and Jiang's metrics) have been implemented to measure between-term similarity within each of the GO hierarchies. Meanwhile, seGOsa offers two approaches (simple and highest average similarity) to assessing the similarity between gene products based on the aggregation of between-term similarities. The program is freely available for non-profit use on request from the authors.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129794000","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 : 2010-12-01DOI: 10.1109/BIBM.2010.5706567
Xuepo Ma, T. Hestilow, Jian Cui, Jianqiu Zhang
Analysis of peptide profiles from a Liquid Chromatography Fourier Transform Mass Spectrometry (LC/FTMS) measurement reveals a non-linear distortion in intensity. Investigation of the measured CVi /C12 ratios comparing with theoretical ones shows that the non-linearity can be attributed to low intensity signal suppression of low abundance peptide peaks. We find that the suppression is homogenous for different isotopes of identical peptides but non-homogenous for different peptides. We developed an iterative correction algorithm that corrects the intensity distortions for peptides with relatively high abundance. This algorithm can be applied in a wide range of applications using FTMS.
{"title":"Iterative correction of suppressed peptide profiles from FTMS measurements","authors":"Xuepo Ma, T. Hestilow, Jian Cui, Jianqiu Zhang","doi":"10.1109/BIBM.2010.5706567","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706567","url":null,"abstract":"Analysis of peptide profiles from a Liquid Chromatography Fourier Transform Mass Spectrometry (LC/FTMS) measurement reveals a non-linear distortion in intensity. Investigation of the measured CVi /C12 ratios comparing with theoretical ones shows that the non-linearity can be attributed to low intensity signal suppression of low abundance peptide peaks. We find that the suppression is homogenous for different isotopes of identical peptides but non-homogenous for different peptides. We developed an iterative correction algorithm that corrects the intensity distortions for peptides with relatively high abundance. This algorithm can be applied in a wide range of applications using FTMS.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128348902","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 : 2010-12-01DOI: 10.1109/BIBM.2010.5706544
Bo Liu, Theodore Gibbons, M. Ghodsi, Mihai Pop
A major goal of metagenomics is to characterize the microbial diversity of an environment. The most popular approach relies on 16S rRNA sequencing, however this approach can generate biased estimates due to differences in the copy number of the 16S rRNA gene between even closely related organisms, and due to PCR artifacts. The taxonomic composition can also be determined from whole-metagenome sequencing data by matching individual sequences against a database of reference genes. One major limitation of prior methods used for this purpose is the use of a universal classification threshold for all genes at all taxonomic levels. We propose that better classification results can be obtained by tuning the taxonomic classifier to each matching length, reference gene, and taxonomic level. We present a novel taxonomic profiler MetaPhyler, which uses marker genes as a taxonomic reference. Results on simulated datasets demonstrate that MetaPhyler outperforms other tools commonly used in this context (CARMA, Megan and PhymmBL). We also present interesting results obtained by applying MetaPhyler to a real metagenomic dataset.
{"title":"MetaPhyler: Taxonomic profiling for metagenomic sequences","authors":"Bo Liu, Theodore Gibbons, M. Ghodsi, Mihai Pop","doi":"10.1109/BIBM.2010.5706544","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706544","url":null,"abstract":"A major goal of metagenomics is to characterize the microbial diversity of an environment. The most popular approach relies on 16S rRNA sequencing, however this approach can generate biased estimates due to differences in the copy number of the 16S rRNA gene between even closely related organisms, and due to PCR artifacts. The taxonomic composition can also be determined from whole-metagenome sequencing data by matching individual sequences against a database of reference genes. One major limitation of prior methods used for this purpose is the use of a universal classification threshold for all genes at all taxonomic levels. We propose that better classification results can be obtained by tuning the taxonomic classifier to each matching length, reference gene, and taxonomic level. We present a novel taxonomic profiler MetaPhyler, which uses marker genes as a taxonomic reference. Results on simulated datasets demonstrate that MetaPhyler outperforms other tools commonly used in this context (CARMA, Megan and PhymmBL). We also present interesting results obtained by applying MetaPhyler to a real metagenomic dataset.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125439969","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 : 2010-12-01DOI: 10.1109/BIBM.2010.5706586
Li-Zhi Liu, Fang-Xiang Wu, Li-Li Han, W. Zhang
Reconstruction of a biological system from its experimental time series data is a challenging task in systems biology. The S-system which consists of a group of nonlinear ordinary differential equations is an effective model to characterize molecular biological systems and analyze the system dynamics. However, inference of S-systems without the knowledge of system structure is not a trivial task due to its nonlinearity and complexity. In this paper, a pruning separable parameter estimation algorithm is proposed for inferring S-systems. This novel algorithm combines the separable parameter estimation method and a pruning strategy, which includes adding an ℓ1 regularization term to the objective function and pruning the solution with a threshold value. The performance of the pruning strategy in the proposed algorithm is evaluated from two aspects: the parameter estimation error and structure identification accuracy. The proposed algorithm is applied to two S-systems with simulated data. The results show that the proposed algorithm has much lower estimation error and much higher identification accuracy than the existing method.
{"title":"Structure identification and parameter estimation of biological s-systems","authors":"Li-Zhi Liu, Fang-Xiang Wu, Li-Li Han, W. Zhang","doi":"10.1109/BIBM.2010.5706586","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706586","url":null,"abstract":"Reconstruction of a biological system from its experimental time series data is a challenging task in systems biology. The S-system which consists of a group of nonlinear ordinary differential equations is an effective model to characterize molecular biological systems and analyze the system dynamics. However, inference of S-systems without the knowledge of system structure is not a trivial task due to its nonlinearity and complexity. In this paper, a pruning separable parameter estimation algorithm is proposed for inferring S-systems. This novel algorithm combines the separable parameter estimation method and a pruning strategy, which includes adding an ℓ1 regularization term to the objective function and pruning the solution with a threshold value. The performance of the pruning strategy in the proposed algorithm is evaluated from two aspects: the parameter estimation error and structure identification accuracy. The proposed algorithm is applied to two S-systems with simulated data. The results show that the proposed algorithm has much lower estimation error and much higher identification accuracy than the existing method.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"157 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126645595","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 : 2010-12-01DOI: 10.1109/BIBM.2010.5706598
Jin Liu, T. Pham, W. Wen, P. Sachdev
Motivated by fuzzy clustering incorporating spatial information, we present a spatially constrained fuzzy hyper-prototype clustering algorithm in this paper. This approach uses hyperplanes as cluster centers and adds a spatial regularizer into the fuzzy objective function. Formulation of the new fuzzy objective function is presented; and its iterative numerical solution, which minimizes the objective function, derived. We applied the proposed algorithm for the segmentation of brain MRI data. Experimental results have demonstrated that the proposed clustering method outperforms other fuzzy clustering models.
{"title":"Spatially constrained fuzzy hyper-prototype clustering with application to brain tissue segmentation","authors":"Jin Liu, T. Pham, W. Wen, P. Sachdev","doi":"10.1109/BIBM.2010.5706598","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706598","url":null,"abstract":"Motivated by fuzzy clustering incorporating spatial information, we present a spatially constrained fuzzy hyper-prototype clustering algorithm in this paper. This approach uses hyperplanes as cluster centers and adds a spatial regularizer into the fuzzy objective function. Formulation of the new fuzzy objective function is presented; and its iterative numerical solution, which minimizes the objective function, derived. We applied the proposed algorithm for the segmentation of brain MRI data. Experimental results have demonstrated that the proposed clustering method outperforms other fuzzy clustering models.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123170755","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 : 2010-12-01DOI: 10.1109/BIBM.2010.5706571
Lei Shi, A. Zhang
New technological advances in large-scale proteinprotein interaction (PPI) detection provide researchers a valuable source for elucidating the bimolecular mechanism in the cell. In this paper, we investigate the problem of protein complex detection from noisy protein interaction data, i.e., finding the subsets of proteins that are closely coupled via protein interactions. Many people try to solve the problem by finding dense subgraphs in protein interaction networks with unsupervised methods. Here, we stand from a different point of view, redefining the properties and features for protein complexes and designing a “semi-supervised” method to analyze the problem. First we select some representative topological features and biological features to represent the protein complexes and then utilize the training data to build a multi-layer neural network model and finally detect hidden protein complexes in the protein-protein interaction networks with the obtained model. Experiments show the desirable properties of our proposed algorithm and the effectiveness of our approach.
{"title":"Semi-supervised learning protein complexes from protein interaction networks","authors":"Lei Shi, A. Zhang","doi":"10.1109/BIBM.2010.5706571","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706571","url":null,"abstract":"New technological advances in large-scale proteinprotein interaction (PPI) detection provide researchers a valuable source for elucidating the bimolecular mechanism in the cell. In this paper, we investigate the problem of protein complex detection from noisy protein interaction data, i.e., finding the subsets of proteins that are closely coupled via protein interactions. Many people try to solve the problem by finding dense subgraphs in protein interaction networks with unsupervised methods. Here, we stand from a different point of view, redefining the properties and features for protein complexes and designing a “semi-supervised” method to analyze the problem. First we select some representative topological features and biological features to represent the protein complexes and then utilize the training data to build a multi-layer neural network model and finally detect hidden protein complexes in the protein-protein interaction networks with the obtained model. Experiments show the desirable properties of our proposed algorithm and the effectiveness of our approach.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125152311","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 : 2010-12-01DOI: 10.1109/BIBM.2010.5706540
F. Cattonaro, A. Policriti, F. Vezzi
Next Generation Sequencing has totally changed genomics: we are able to produce huge amounts of data at an incredible low cost if compared to Sanger sequencing. Despite this some old problems have become even more difficult, denovo assembly being on top of this list. In this paper we propose a novel method that aims at improving de-novo assembly in presence of a closely related reference. The idea is to combine de-novo assembly and reference guided assembly in order to obtain an enhanced assembly.
{"title":"Enhanced reference guided assembly","authors":"F. Cattonaro, A. Policriti, F. Vezzi","doi":"10.1109/BIBM.2010.5706540","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706540","url":null,"abstract":"Next Generation Sequencing has totally changed genomics: we are able to produce huge amounts of data at an incredible low cost if compared to Sanger sequencing. Despite this some old problems have become even more difficult, denovo assembly being on top of this list. In this paper we propose a novel method that aims at improving de-novo assembly in presence of a closely related reference. The idea is to combine de-novo assembly and reference guided assembly in order to obtain an enhanced assembly.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"235 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122884772","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 : 2010-12-01DOI: 10.1109/BIBM.2010.5706563
T. Joshi, Q. Yao, D. F. Levi, L. Brechenmacher, B. Valliyodan, G. Stacey, H. Nguyen, Dong Xu
SoyMetDB is a metabolomic database for soybean, developed to target the growing needs of the soybean community. The goal is to provide a one-stop web resource for integrating, mining and visualizing soybean metabolomic data, including identification and expression of various metabolites across different experiments and time courses. It incorporates GC-MS and LC-MS based metabolite-profiling data dynamically linked to metabolite information from other public metabolomic databases, including HMDB and Knapsack. SoyMetDB includes Arabidopsis metabolomic data for cross-species comparisons and can retrieve information including the expression patterns of various experiments for complete or partial metabolite name queries. It also incorporates a pathway viewer tool integrating the data from various experimental conditions and presenting them on the pathways to highlight the expressed metabolite, and identifies the most highly represented pathways for multiple metabolite queries. SoyMetDB can be accessed at http://soymetdb.org.
{"title":"SoyMetDB: The soybean metabolome database","authors":"T. Joshi, Q. Yao, D. F. Levi, L. Brechenmacher, B. Valliyodan, G. Stacey, H. Nguyen, Dong Xu","doi":"10.1109/BIBM.2010.5706563","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706563","url":null,"abstract":"SoyMetDB is a metabolomic database for soybean, developed to target the growing needs of the soybean community. The goal is to provide a one-stop web resource for integrating, mining and visualizing soybean metabolomic data, including identification and expression of various metabolites across different experiments and time courses. It incorporates GC-MS and LC-MS based metabolite-profiling data dynamically linked to metabolite information from other public metabolomic databases, including HMDB and Knapsack. SoyMetDB includes Arabidopsis metabolomic data for cross-species comparisons and can retrieve information including the expression patterns of various experiments for complete or partial metabolite name queries. It also incorporates a pathway viewer tool integrating the data from various experimental conditions and presenting them on the pathways to highlight the expressed metabolite, and identifies the most highly represented pathways for multiple metabolite queries. SoyMetDB can be accessed at http://soymetdb.org.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131292626","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 : 2010-12-01DOI: 10.1109/BIBM.2010.5706651
Songfeng Zheng, Weixiang Liu
Automatically selecting a subset of genes with strong discriminative power is a very important step in classification problems based on gene expression data. Lasso and Dantzig selector are known to have automatic variable selection ability in linear regression analysis. This paper employs Lasso and Dantzig selector to select most informative genes for representing the class label as a linear function of gene expression data. The selected genes are further used to fit linear classifiers for cancer classification. On 3 publicly available cancer datasets, the experimental results show that in general, Lasso is more capable than Dantzig selector in selecting informative genes for classification.
{"title":"Selecting informative genes by Lasso and Dantzig selector for linear classifiers","authors":"Songfeng Zheng, Weixiang Liu","doi":"10.1109/BIBM.2010.5706651","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706651","url":null,"abstract":"Automatically selecting a subset of genes with strong discriminative power is a very important step in classification problems based on gene expression data. Lasso and Dantzig selector are known to have automatic variable selection ability in linear regression analysis. This paper employs Lasso and Dantzig selector to select most informative genes for representing the class label as a linear function of gene expression data. The selected genes are further used to fit linear classifiers for cancer classification. On 3 publicly available cancer datasets, the experimental results show that in general, Lasso is more capable than Dantzig selector in selecting informative genes for classification.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134366733","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 : 2010-12-01DOI: 10.1109/BIBM.2010.5706537
Qian Xu, E. Xiang, Qiang Yang
Protein-protein interactions (PPI) play an important role in cellular processes and metabolic processes within a cell. An important task is to determine the existence of interactions among proteins. Unfortunately, existing biological experimental techniques are expensive, time-consuming and labor-intensive. The network structures of many such networks are sparse, incomplete and noisy, containing many false positive and false negatives. Thus, state-of-the-art methods for link prediction in these networks often cannot give satisfactory prediction results, especially when some networks are extremely sparse. Noticing that we typically have more than one PPI network available, we naturally wonder whether it is possible to 'transfer' the linkage knowledge from some existing, relatively dense networks to a sparse network, to improve the prediction performance. Noticing that a network structure can be modeled using a matrix model, in this paper, we introduce the well-known Collective Matrix Factorization (CMF) technique to 'transfer' usable linkage knowledge from relatively dense interaction network to a sparse target network. Our approach is to establish the correspondence between a source and a target network via network similarities. We test this method on two real protein-protein interaction networks, Helicobacter pylori (as a target network) and Human (as a source network). Our experimental results show that our method can achieve higher and more robust performance as compared to some baseline methods.
{"title":"Protein-protein interaction prediction via Collective Matrix Factorization","authors":"Qian Xu, E. Xiang, Qiang Yang","doi":"10.1109/BIBM.2010.5706537","DOIUrl":"https://doi.org/10.1109/BIBM.2010.5706537","url":null,"abstract":"Protein-protein interactions (PPI) play an important role in cellular processes and metabolic processes within a cell. An important task is to determine the existence of interactions among proteins. Unfortunately, existing biological experimental techniques are expensive, time-consuming and labor-intensive. The network structures of many such networks are sparse, incomplete and noisy, containing many false positive and false negatives. Thus, state-of-the-art methods for link prediction in these networks often cannot give satisfactory prediction results, especially when some networks are extremely sparse. Noticing that we typically have more than one PPI network available, we naturally wonder whether it is possible to 'transfer' the linkage knowledge from some existing, relatively dense networks to a sparse network, to improve the prediction performance. Noticing that a network structure can be modeled using a matrix model, in this paper, we introduce the well-known Collective Matrix Factorization (CMF) technique to 'transfer' usable linkage knowledge from relatively dense interaction network to a sparse target network. Our approach is to establish the correspondence between a source and a target network via network similarities. We test this method on two real protein-protein interaction networks, Helicobacter pylori (as a target network) and Human (as a source network). Our experimental results show that our method can achieve higher and more robust performance as compared to some baseline methods.","PeriodicalId":275098,"journal":{"name":"2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117090235","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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