Ensemble attribute profile clustering: Discovering and characterizing of genes with similar patterns of biological features

BMC Bioinformatics

Volumn 7, Issue , 2006, Pages

  Semeiks, J R ^a   Riski, A ^a   Bissell, M J ^a   Mian, I S ^a  

^a LAWRENCE BERKELEY NATIONAL LABORATORY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BINARY RANDOM VARIABLES; BIOLOGICAL FEATURES; BIOLOGICAL PROPERTIES; CHROMOSOMAL LOCATION; FINITE MIXTURE MODELS; FUNCTIONAL PROPERTIES; HYDROLASE ACTIVITIES; STATISTICAL INFORMATION;

CELLS; GENES; NUCLEIC ACIDS; PROTEINS; SET THEORY; VECTOR SPACES;

TRANSCRIPTION;

CELL MARKER; HYDROLASE; MEMBRANE RECEPTOR; OXIDOREDUCTASE;

ARTICLE; BREAST; CELL METABOLISM; CHROMOSOMAL LOCALIZATION; CONSENSUS SEQUENCE; CONTROLLED STUDY; DATA ANALYSIS SOFTWARE; DATA COLLECTION METHOD; ENZYME ACTIVITY; EPITHELIUM CELL; GENE ASSIGNMENT; GENE CLUSTER; GENE EXPRESSION PROFILING; GENE FUNCTION; GENE LOCUS; GENETIC ANALYSIS; GENETIC DATABASE; GENETIC MODEL; HUMAN; MARKER GENE; MYOEPITHELIUM CELL; PROCESS MODEL; PROTEIN DOMAIN; SIGNAL TRANSDUCTION; SYNTHESIS; ALGORITHM; AUTOMATED PATTERN RECOGNITION; CLUSTER ANALYSIS; DNA MICROARRAY; DNA SEQUENCE; PROCEDURES; SEQUENCE ALIGNMENT;

ALGORITHMS; CLUSTER ANALYSIS; GENE EXPRESSION PROFILING; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; PATTERN RECOGNITION, AUTOMATED; SEQUENCE ALIGNMENT; SEQUENCE ANALYSIS, DNA;

EID: 33645772945     PISSN: 14712105     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-7-147     Document Type: Article

References (46)

1. PubMed [http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed]
2. LocusLink [http://www.ncbi.nlm.nih.gov/LocusLink]
3. Entrez Gene [http://www.ncb.nlm.nih.gov/entrez/query.fcgi?db=gene]
4. SGD [http://www.yeastgenome.org]
5. Wormbase [http://www.wormbase.org]
6. Flybase [http://www.flybase.org]
7. MacCallum R, Kelley R, Steinberg M: SAWTED: Structure Assignment With Text Description - Enhanced detection of remote homologues with automated SWISS-PROT annotation comparisons. Bioinformatics 2000, 16:125-129.
8. Jenssen T, Laegreid A, Komorowski J, Hovig E: A literature network of human genes for high-throughput analysis of gene expression. Nature Genetics 2001, 28:21-28.
9. Raychaudhuri S, Chang J, Imam F, Altman R: The computational analysis of scientific literature to define and recognize gene expression clusters. Nucleic Acids Research 2003, 31:4553-4560.
10. Korbel J, Doerks T, Jensen L, Perez-Iratxeta C, Kaczanowski S, Hooper S, Andrade M, Bork P: Systematic association of genes to phenotypes by genome and literature mining. PLoS Biology 2005, 3:e134.
11. Blei D, Franks K, Jordan M, Mian I: Statistical modeling of biomedical corpora: mining the Caenorhabditis Genetic Center Bibliography for genes related to aging. BMC Bioinformatics 2006 in press.
12. Raychaudhuri S, Schütze H, Altman R: Using text analysis to identify functionally coherent gene groups. Genome Research 2002, 12:1582-1590.
13. Harris M, Clark J, Ireland A, Lomax J, Ashburner M, Foulger R, Eilbeck K, Lewis S, Marshall B, Mungall C, Richter J, Rubin G, Blake J, Bult C, Dolan M, Drabkin H, Eppig J, Hill D, Ni L, Ringwald M, Balakrishnan R, Cherry J, Christie K, Costanzo M, Dwight S, Engel S, Fisk D, Hirschman J, Hong E, Nash R, Sethuraman A, Theesfeld C, Botstein D, Dolinski K, Feierbach B, Berardini T, Mundodi S, Rhee S, Apweiler R, Barrell D, Camon E, Dimmer E, Lee V, Chisholm R, Gaudet P, Kibbe W, Kishore R, Schwarz E, Sternberg P, Gwinn M, Hannick L, Wortman J, Berriman M, Wood V, de la Cruz N, Tonellato P, Jaiswal P, Seigfried T, White R: The Gene Ontology (GO) database and informatics resource. Nucleic Acids Research 2004, 32:D258-D261.
14. Zeeberg B, Feng W, Wang G, Wang M, Fojo A, Sunshine M, Narasimhan S, Kane D, Reinhold W, Lababidi S, Bussey K, Riss J, Barrett J, Weinstein J: GoMiner: a resource for biological interpretation of genomic and proteomic data. Genome Biology 2003, 4:.
15. GOTermFinder [http://www.Yeastgenome.org]
16. Robinson M, Grigull J, Mohammad N, Hughes T: FunSpec: a web-based cluster interpreter for yeast. BMC Bioinformatics 2002, 3:35-40.
17. Beißbarth T, Speed T. GOstat: Find statistically overrepresented Gene Ontologies within a group of genes. Bioinformatics 2004, 20:1464-1465.
18. Berriz G, King O, Bryant B, Sander C, Roth F: Characterizing gene sets with FuncAssociate. Bioinformatics 2003, 19:2502-2504.
19. Manning C, Schütze H: Foundations of Statistical Natural Language Processing Cambridge, MA: MIT Press; 1999.
20. Salton G: Automatic Text Processing: The Transformation Analysis and Retrieval of Information by Computer Addison-Wesley; 1988.
21. Moler E, Chow M, Mian I: Analysis of molecular profile data using generative and discriminative methods. Physiological Genomics 2000, 4:109-126.
22. Moler E, Radisky D, Mian I: Integrating naïve Bayes models and external knowledge to examine copper and iron homeostasis in Saccharomyces cerevisiae. Physiological Genomics 2000, 4:127-135.
23. Bhattacharjee A, Richards W, Staunton J, Li C, Monti S, Vasa P, Ladd C, Beheshti J, Bueno R, Gillette M, Loda M, Weber G, Mark E, Lander E, Wong W, Johnson B, Golub T, Sugarbaker D, Meyerson M: Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proc Natl Acad Sci 2001, 98:13790-13795.
24. Monti S, Tamayo P, Mesirov J, Golub T: Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data. Machine Learning 2003, 52:91-118.
25. Kellam P, Liu X, Martin N, Orengo C, Swift S, Tucker A: Comparing, contrasting and combining clusters in viral gene expression data. Proceedings of the IDAMAP2001 Workshop 2001 2001:56-62.
26. Jones C, Mackay A, Grigoriadis A, Cossu A, Reis-Filho J, Fulford L, Dexter T, Davies S, Bulmer K, Ford E, Parry S, Budroni M, Palmieri G, Neville A, O'Hare M, Lakhani S: Expression profiling of purified normal human luminal and myoepithelial breast cells: identification of novel prognostic markers for breast cancer. Cancer Research 2004, 64:3037-3045.
27. Hofmann T, Puzicha J, Jordan M: Learning from dyadic data. In Advances in Neural Information Processing Systems Volume II. MIT Press, Cambridge MA; 1999.
28. Hoff P: Model-based subspace clustering. Bayesian Analysis 2005 in press.
29. Hoff P: Subset clustering of binary sequences, with an application to genomic abnormality data. Biometrics 2005 in press.
30. Glenisson P, Coessens B, van Vooren S, Mathys J, Moreau Y, de Moor B: TXTGate: profiling gene groups with text-based information. Genome Biology 2004, 5:R43.
31. Hurst L, Pál C, Lercher M: The evolutionary dynamics of eukaryotic gene order. Nature Review Genetics 2004, 5:299-310.
32. Allinen M, Beroukhim R, Cai L, Brennan C, Lahti-Domenici J, Huang H, Porter D, Hu M, Chin L, Richardson A, Schnitt S, Sellers W, Polyak K: Molecular characterization of the tumor microenvironment in breast cancer. Cancer Cell 2004, 6:17-32.
33. Jones C, Ford E, Gillett C, Ryder K, Merrett S, Reis-Filho J, Fulford L, Hanby A, Lakhani S: Molecular cytogenetic identification of subgroups of grade III invasive ductal breast carcinomas with different clinical outcomes. Clinical Cancer Research 2004, 10:5988-5997.
34. Pruitt K, Maglott D: RefSeq and LocusLink: NCBI gene-centered resources. Nucleic Acids Research 2001, 29:137-140.
35. RefSeq [http://www.ncbi.nlm.nih.gov/RefSeq]
36. Bioperl [http://www.bioperl.org]
37. GO [http://www.geneontology.org]
38. CDD [http://www.ncbi.nlm.nih.gov/Structure/cdd/cdd.shtml]
39. Cheeseman P, Stutz J: Bayesian Classification (AutoClass): Theory and Results. In Advances in Knowledge Discovery and Data Mining Edited by: Fayyad U, Piatetsky-Shapiro G, Smyth P, Uthurusamy R. AAAI Press/MIT Press; 1996:153-180.
40. Kuncheva L: Combining Pattern Classifiers: Methods and Algorithms London: John Wiley & Sons; 2004.
41. Fred A: Finding consistent clusters in data partitions. In Multiple Classifier Systems Volume LNCS 2364. Springer 2002:309-318.
42. Strehl A, Ghosh J: Cluster Ensembles - A Knowledge Reuse Framework for Combining Multiple Partitions. Journal of Machine Learning Research 2002, 3:583-617.
43. Topchy A, Jain A, Punch W: A mixture model of clustering ensembles. Proceedings SIAM Conf on Data Mining 2004.
44. C++ Boost Graph library [http://www.boost.org/libs/graph/doc/index-html]
45. KEGG [http://www.genome.jp/kegg]
46. UCSC Genome Browser [http://genome.ucsc.edu]