Vasant Honavar

Summary

Affiliation: Iowa State University
Location: Ames, USA
URL: http://www.cs.iastate.edu/~honavar
Publications:

1. PRIDB: a Protein-RNA Interface Database.
Lewis BA, Walia RR, Terribilini M, Ferguson J, Zheng C, Honavar V, Dobbs D.
Nucleic Acids Res. 2011 Jan;39(Database issue):D277-82. Epub 2010 Nov 11.
PMID: [PubMed - indexed for MEDLINE] Free PMC Article
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2. Recent advances in B-cell epitope prediction methods.
El-Manzalawy Y, Honavar V.
Immunome Res. 2010 Nov 3;6 Suppl 2:S2.
PMID: [PubMed - in process] Free PMC Article
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3. Semi-supervised prediction of protein subcellular localization using abstraction augmented Markov models.
Caragea C, Caragea D, Silvescu A, Honavar V.
BMC Bioinformatics. 2010 Oct 26;11 Suppl 8:S6.
PMID: [PubMed - indexed for MEDLINE] Free PMC Article
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4. Methods for transcriptomic analyses of the porcine host immune response: application to Salmonella infection using microarrays.
Tuggle CK, Bearson SM, Uthe JJ, Huang TH, Couture OP, Wang YF, Kuhar D, Lunney JK, Honavar V.
Vet Immunol Immunopathol. 2010 Dec 15;138(4):280-91. Epub 2010 Oct 14. Review.
PMID: [PubMed - indexed for MEDLINE]
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5. Predicting MHC-II Binding Affinity Using Multiple Instance Regression.
El-Manzalawy Y, Dobbs D, Honavar V.
IEEE/ACM Trans Comput Biol Bioinform. 2010 Sep 10. [Epub ahead of print]
PMID: [PubMed - as supplied by publisher]
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6. Detection of gene orthology from gene co-expression and protein interaction networks.
Towfic F, VanderPlas S, Oliver CA, Couture O, Tuggle CK, West Greenlee MH, Honavar V.
BMC Bioinformatics. 2010 Apr 29;11 Suppl 3:S7.
PMID: [PubMed - indexed for MEDLINE] Free PMC Article
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7. Struct-NB: predicting protein-RNA binding sites using structural features.
Towfic F, Caragea C, Gemperline DC, Dobbs D, Honavar V.
Int J Data Min Bioinform. 2010;4(1):21-43.
PMID: [PubMed - indexed for MEDLINE] Free PMC Article
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8. Characterization of the retinal proteome during rod photoreceptor genesis.
Barnhill AE, Hecker LA, Kohutyuk O, Buss JE, Honavar VG, Greenlee HW.
BMC Res Notes. 2010 Jan 27;3:25.
PMID: [PubMed - in process] Free PMC Article
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9. ANEXdb: an integrated animal ANnotation and microarray EXpression database.
Couture O, Callenberg K, Koul N, Pandit S, Younes R, Hu ZL, Dekkers J, Reecy J, Honavar V, Tuggle C.
Mamm Genome. 2009 Nov-Dec;20(11-12):768-77. Epub 2009 Nov 20.
PMID: [PubMed - indexed for MEDLINE]
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10. Mixture of experts models to exploit global sequence similarity on biomolecular sequence labeling.
Caragea C, Sinapov J, Dobbs D, Honavar V.
BMC Bioinformatics. 2009 Apr 29;10 Suppl 4:S4.
PMID: [PubMed - indexed for MEDLINE] Free PMC Article
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11. Use of machine learning algorithms to classify binary protein sequences as highly-designable or poorly-designable.
Peto M, Kloczkowski A, Honavar V, Jernigan RL.
BMC Bioinformatics. 2008 Nov 18;9:487.
PMID: [PubMed - indexed for MEDLINE] Free PMC Article
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12. On evaluating MHC-II binding peptide prediction methods.
El-Manzalawy Y, Dobbs D, Honavar V.
PLoS One. 2008 Sep 24;3(9):e3268.
PMID: [PubMed - indexed for MEDLINE] Free PMC Article
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13. Predicting linear B-cell epitopes using string kernels.
El-Manzalawy Y, Dobbs D, Honavar V.
J Mol Recognit. 2008 Jul-Aug;21(4):243-55.
PMID: [PubMed - indexed for MEDLINE] Free PMC Article
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14. Animal trait ontology: The importance and usefulness of a unified trait vocabulary for animal species.
Hughes LM, Bao J, Hu ZL, Honavar V, Reecy JM.
J Anim Sci. 2008 Jun;86(6):1485-91. Epub 2008 Feb 13.
PMID: [PubMed - indexed for MEDLINE] Free PMC Article
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15. A neural-network architecture for syntax analysis.
Chen CH, Honavar V.
IEEE Trans Neural Netw. 1999;10(1):94-114.
PMID: [PubMed]
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16. Constructive neural-network learning algorithms for pattern classification.
Parekh R, Yang J, Honavar V.
IEEE Trans Neural Netw. 2000;11(2):436-51.
PMID: [PubMed]
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17. Striking similarities in diverse telomerase proteins revealed by combining structure prediction and machine learning approaches.
Lee JH, Hamilton M, Gleeson C, Caragea C, Zaback P, Sander JD, Li X, Wu F, Terribilini M, Honavar V, Dobbs D.
Pac Symp Biocomput. 2008:501-12.
PMID: [PubMed - indexed for MEDLINE] Free PMC Article
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18. Using a seed-network to query multiple large-scale gene expression datasets from the developing retina in order to identify and prioritize experimental targets.
Hecker LA, Alcon TC, Honavar VG, Greenlee MH.
Bioinform Biol Insights. 2008 Feb 1;2:401-12.
PMID: [PubMed] Free PMC Article
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19. Predicting flexible length linear B-cell epitopes.
El-Manzalawy Y, Dobbs D, Honavar V.
Comput Syst Bioinformatics Conf. 2008;7:121-32.
PMID: [PubMed - indexed for MEDLINE] Free Article
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20. Glycosylation site prediction using ensembles of Support Vector Machine classifiers.
Caragea C, Sinapov J, Silvescu A, Dobbs D, Honavar V.
BMC Bioinformatics. 2007 Nov 9;8:438.
PMID: [PubMed - indexed for MEDLINE] Free PMC Article
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21. Characterization of protein-protein interfaces.
Yan C, Wu F, Jernigan RL, Dobbs D, Honavar V.
Protein J. 2008 Jan;27(1):59-70.
PMID: [PubMed - indexed for MEDLINE] Free PMC Article
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22. Exploring inconsistencies in genome-wide protein function annotations: a machine learning approach.
Andorf C, Dobbs D, Honavar V.
BMC Bioinformatics. 2007 Aug 3;8:284.
PMID: [PubMed - indexed for MEDLINE] Free PMC Article
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23. RNABindR: a server for analyzing and predicting RNA-binding sites in proteins.
Terribilini M, Sander JD, Lee JH, Zaback P, Jernigan RL, Honavar V, Dobbs D.
Nucleic Acids Res. 2007 Jul;35(Web Server issue):W578-84. Epub 2007 May 5.
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24. Identifying interaction sites in "recalcitrant" proteins: predicted protein and RNA binding sites in rev proteins of HIV-1 and EIAV agree with experimental data.
Terribilini M, Lee JH, Yan C, Jernigan RL, Carpenter S, Honavar V, Dobbs D.
Pac Symp Biocomput. 2006:415-26.
PMID: [PubMed - indexed for MEDLINE] Free PMC Article
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25. On the Semantics of Linking and Importing in Modular Ontologies.
Bao J, Caragea D, Honavar VG.
Lect Notes Comput Sci. 2006 Nov 6;4273:72-86.
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26. Learning Classifiers from Distributed, Ontology-Extended Data Sources.
Caragea D, Zhang J, Pathak J, Honavar V.
Lect Notes Comput Sci. 2006 Sep 4;4081:363-373.
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27. Prediction of RNA binding sites in proteins from amino acid sequence.
Terribilini M, Lee JH, Yan C, Jernigan RL, Honavar V, Dobbs D.
RNA. 2006 Aug;12(8):1450-62. Epub 2006 Jun 21.
PMID: [PubMed - indexed for MEDLINE] Free PMC Article
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28. Predicting DNA-binding sites of proteins from amino acid sequence.
Yan C, Terribilini M, Wu F, Jernigan RL, Dobbs D, Honavar V.
BMC Bioinformatics. 2006 May 19;7:262.
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29. Learning accurate and concise naïve Bayes classifiers from attribute value taxonomies and data.
Zhang J, Kang DK, Silvescu A, Honavar V.
Knowl Inf Syst. 2006 Feb 1;9(2):157-179.
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30. RNBL-MN: A Recursive Naive Bayes Learner for Sequence Classification.
Kang DK, Silvescu A, Honavar V.
Lect Notes Comput Sci. 2006;3918 LNAI:45-54.
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31. Information Integration from Semantically Heterogeneous Biological Data Sources.
Caragea D, Bao J, Pathak J, Silvescu A, Andorf C, Dobbs D, Honavar V.
Int Workshop databases Expert Syst Appl. 2005 Aug 26;2005:580-584.
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32. Multinomial Event Model Based Abstraction for Sequence and Text Classification.
Kang DK, Zhang J, Silvescu A, Honavar V.
Lect Notes Comput Sci. 2005 Jul 26;3607:134-148.
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33. Information Integration and Knowledge Acquisition from Semantically Heterogeneous Biological Data Sources.
Caragea D, Pathak J, Bao J, Silvescu A, Andorf C, Dobbs D, Honavar V.
Lect Notes Comput Sci. 2005;3615:175-190.
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34. Algorithms and Software for Collaborative Discovery from Autonomous, Semantically Heterogeneous, Distributed Information Sources.
Caragea D, Zhang J, Bao J, Pathak J, Honavar V.
Lect Notes Comput Sci. 2005 Jan 1;3734:13-44.
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35. Learning Classifiers Using Hierarchically Structured Class Taxonomies.
Wu F, Zhang J, Honavar V.
Lect Notes Comput Sci. 2005;3607:313-320.
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36. Predicting binding sites of hydrolase-inhibitor complexes by combining several methods.
Sen TZ, Kloczkowski A, Jernigan RL, Yan C, Honavar V, Ho KM, Wang CZ, Ihm Y, Cao H, Gu X, Dobbs D.
BMC Bioinformatics. 2004 Dec 17;5:205.
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37. An incremental learning algorithm with confidence estimation for automated identification of NDE signals.
Polikar R, Udpa L, Udpa S, Honavar V.
IEEE Trans Ultrason Ferroelectr Freq Control. 2004 Aug;51(8):990-1001.
PMID: [PubMed]
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38. A two-stage classifier for identification of protein-protein interface residues.
Yan C, Dobbs D, Honavar V.
Bioinformatics. 2004 Aug 4;20 Suppl 1:i371-8.
PMID: [PubMed - indexed for MEDLINE] Free Article
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39. Identification of interface residues in protease-inhibitor and antigen-antibody complexes: a support vector machine approach.
Yan C, Honavar V, Dobbs D.
Neural Comput Appl. 2004 Jun 1;13(2):123-129.
PMID: [PubMed] Free PMC Article
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40. A Framework for Learning from Distributed Data Using Sufficient Statistics and its Application to Learning Decision Trees.
Caragea D, Silvescu A, Honavar V.
Int J Hybrid Intell Syst. 2004 Apr 1;1(1-2):80-89.
PMID: [PubMed] Free PMC Article
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41. A proteomic analysis of maize chloroplast biogenesis.
Lonosky PM, Zhang X, Honavar VG, Dobbs DL, Fu A, Rodermel SR.
Plant Physiol. 2004 Feb;134(2):560-74.
PMID: [PubMed - indexed for MEDLINE] Free PMC Article
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42. Learning Classifiers from Semantically Heterogeneous Data.
Caragea D, Pathak J, Honavar VG.
Lect Notes Comput Sci. 2004;3291:963-980.
PMID: [PubMed] Free PMC Article
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43. A Multi-relational Decision Tree Learning Algorithm - Implementation and Experiments.
Atramentov A, Leiva H, Honavar V.
Lect Notes Comput Sci. 2003;2835:38-56.
PMID: [PubMed] Free PMC Article
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44. Is the hippocampus a Kalman filter?
Bousquet O, Balakrishnan K, Honavar V.
Pac Symp Biocomput. 1998:657-68.
PMID: 9697220 [PubMed - indexed for MEDLINE] Free Article
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Publications

  1. ncbi PRIDB: a Protein-RNA Interface Database
    Benjamin A Lewis
    Bioinformatics and Computational Biology Program, Iowa State University, Department of Genetics, Development and Cell Biology, Iowa State University, Department of Computer Science, Iowa State University, Ames, IA 50011, Department of Biology, Elon University, Elon, NC 27244 and Computational Systems Biology Summer Institute, Iowa State University, Ames, IA 50011, USA
    Nucleic Acids Res 39:D277-82. 2011
  2. ncbi ANEXdb: an integrated animal ANnotation and microarray EXpression database
    Oliver Couture
    Interdepartmental Genetics, Iowa State University, Ames, IA 50011, USA
    Mamm Genome 20:768-77. 2009
  3. ncbi Using a seed-network to query multiple large-scale gene expression datasets from the developing retina in order to identify and prioritize experimental targets
    Laura A Hecker
    Interdepartmental Neuroscience Program, Iowa State University, Ames, IA 50011, USA
    Bioinform Biol Insights 2:401-12. 2008
  4. ncbi Predicting flexible length linear B-cell epitopes
    Yasser El-Manzalawy
    Artificial Intelligence Laboratory, Iowa State University, Ames, IA 50010, USA
    Comput Syst Bioinformatics Conf 7:121-32. 2008
  5. ncbi Characterization of the retinal proteome during rod photoreceptor genesis
    Alison E Barnhill
    Interdepartmental Neuroscience Program, Iowa State University, Ames, IA USA
    BMC Res Notes 3:25. 2010
  6. ncbi Struct-NB: predicting protein-RNA binding sites using structural features
    Fadi Towfic
    Bioinformatics and Computational Biology Graduate Program, Iowa State University, Ames, IA 50011 1040, USA
    Int J Data Min Bioinform 4:21-43. 2010
  7. ncbi Recent advances in B-cell epitope prediction methods
    Yasser El-Manzalawy
    Department of Systems and Computer Engineering, Al Azhar University, Egypt
    Immunome Res 6:S2. 2010
  8. ncbi Methods for transcriptomic analyses of the porcine host immune response: application to Salmonella infection using microarrays
    C K Tuggle
    Department of Animal Science, and Center for Integrated Animal Genomics, 2255 Kildee Hall, Iowa State University, Ames, IA 50010, United States
    Vet Immunol Immunopathol 138:280-91. 2010
  9. ncbi Predicting MHC-II binding affinity using multiple instance regression
    Yasser El-Manzalawy
    Department of Systems and Computers Engineering, Al Azhar University, Cairo, Egypt
    IEEE/ACM Trans Comput Biol Bioinform 8:1067-79. 2011
  10. ncbi Identifying interaction sites in "recalcitrant" proteins: predicted protein and RNA binding sites in rev proteins of HIV-1 and EIAV agree with experimental data
    Michael Terribilini
    Bioinformatics and Computational Biology Graduate Program and L H Baker Center for Bioinformatics and Biological Statistics, Iowa State University, Ames, IA 50010, USA
    Pac Symp Biocomput . 2006

Collaborators

Detail Information

Publications45

  1. ncbi PRIDB: a Protein-RNA Interface Database
    Benjamin A Lewis
    Bioinformatics and Computational Biology Program, Iowa State University, Department of Genetics, Development and Cell Biology, Iowa State University, Department of Computer Science, Iowa State University, Ames, IA 50011, Department of Biology, Elon University, Elon, NC 27244 and Computational Systems Biology Summer Institute, Iowa State University, Ames, IA 50011, USA
    Nucleic Acids Res 39:D277-82. 2011
    ..Also, several non-redundant benchmark data sets of protein-RNA complexes are provided. The PRIDB database is freely available online at http://bindr.gdcb.iastate.edu/PRIDB...
  2. ncbi ANEXdb: an integrated animal ANnotation and microarray EXpression database
    Oliver Couture
    Interdepartmental Genetics, Iowa State University, Ames, IA 50011, USA
    Mamm Genome 20:768-77. 2009
    ..The ANEXdb application is open source and available from SourceForge.net...
  3. ncbi Using a seed-network to query multiple large-scale gene expression datasets from the developing retina in order to identify and prioritize experimental targets
    Laura A Hecker
    Interdepartmental Neuroscience Program, Iowa State University, Ames, IA 50011, USA
    Bioinform Biol Insights 2:401-12. 2008
    ....
  4. ncbi Predicting flexible length linear B-cell epitopes
    Yasser El-Manzalawy
    Artificial Intelligence Laboratory, Iowa State University, Ames, IA 50010, USA
    Comput Syst Bioinformatics Conf 7:121-32. 2008
    ..An implementation of FBCPred and the datasets used in this study are publicly available through our linear B-cell epitope prediction server, BCPREDS, at: http://ailab.cs.iastate.edu/bcpreds/...
  5. ncbi Characterization of the retinal proteome during rod photoreceptor genesis
    Alison E Barnhill
    Interdepartmental Neuroscience Program, Iowa State University, Ames, IA USA
    BMC Res Notes 3:25. 2010
    ..We have used a proteomics approach to identify proteins that are dynamically expressed in the mouse retina during rod genesis and differentiation...
  6. ncbi Struct-NB: predicting protein-RNA binding sites using structural features
    Fadi Towfic
    Bioinformatics and Computational Biology Graduate Program, Iowa State University, Ames, IA 50011 1040, USA
    Int J Data Min Bioinform 4:21-43. 2010
    ..Our experiments show that Struct-NB, a Naive Bayes classifier that exploits structural features, outperforms its counterparts that use only sequence features to predict protein-RNA binding residues...
  7. ncbi Recent advances in B-cell epitope prediction methods
    Yasser El-Manzalawy
    Department of Systems and Computer Engineering, Al Azhar University, Egypt
    Immunome Res 6:S2. 2010
    ..We review recent advances in computational methods for B-cell epitope prediction, identify some gaps in the current state of the art, and outline some promising directions for improving the reliability of such methods...
  8. ncbi Methods for transcriptomic analyses of the porcine host immune response: application to Salmonella infection using microarrays
    C K Tuggle
    Department of Animal Science, and Center for Integrated Animal Genomics, 2255 Kildee Hall, Iowa State University, Ames, IA 50010, United States
    Vet Immunol Immunopathol 138:280-91. 2010
    ..Through this review, we expect readers will gain an appreciation for the necessary steps to plan, conduct, analyze and interpret the data from transcriptomic analyses directly applicable to their research interests...
  9. ncbi Predicting MHC-II binding affinity using multiple instance regression
    Yasser El-Manzalawy
    Department of Systems and Computers Engineering, Al Azhar University, Cairo, Egypt
    IEEE/ACM Trans Comput Biol Bioinform 8:1067-79. 2011
    ..An online web server that implements the MHCMIR method for MHC-II binding affinity prediction is freely accessible at http://ailab.cs.iastate.edu/mhcmir...
  10. ncbi Identifying interaction sites in "recalcitrant" proteins: predicted protein and RNA binding sites in rev proteins of HIV-1 and EIAV agree with experimental data
    Michael Terribilini
    Bioinformatics and Computational Biology Graduate Program and L H Baker Center for Bioinformatics and Biological Statistics, Iowa State University, Ames, IA 50010, USA
    Pac Symp Biocomput . 2006
    ....
  11. ncbi Prediction of RNA binding sites in proteins from amino acid sequence
    Michael Terribilini
    Bioinformatics and Computationa Biology, Graduate Program, Iowa State University, Ames, Iowa 50010, USA
    RNA 12:1450-62. 2006
    ..RNABindR is available as a Web tool from http://bindr.gdcb.iastate.edu.)...
  12. ncbi Characterization of protein-protein interfaces
    Changhui Yan
    Department of Computer Science, Utah State University, 4205 Old Main Hill, Logan, UT 84341, USA
    Protein J 27:59-70. 2008
    ..Consistent results are obtained across these datasets. We have also investigated separately the characteristics of heteromeric interfaces and homomeric interfaces...
  13. ncbi Predicting binding sites of hydrolase-inhibitor complexes by combining several methods
    Taner Z Sen
    L H Baker Center for Bioinformatics and Biological Statistics, Iowa State University, Ames, IA 50011, USA
    BMC Bioinformatics 5:205. 2004
    ....
  14. ncbi An incremental learning algorithm with confidence estimation for automated identification of NDE signals
    Robi Polikar
    Department of Electrical and Computer Engineering, Rowan University, Glassboro, NJ 08028, USA
    IEEE Trans Ultrason Ferroelectr Freq Control 51:990-1001. 2004
    ..The voting procedure also allows Learn++ to estimate the confidence in its own decision. We present the algorithm and its promising results on two separate ultrasonic weld inspection applications...
  15. ncbi A two-stage classifier for identification of protein-protein interface residues
    Changhui Yan
    Artificial Intelligence Research Laboratory, Iowa State University, Ames, IA, 50010, USA
    Bioinformatics 20:i371-8. 2004
    ..The success of the predictions is validated by examining the predictions in the context of the three-dimensional structures of protein complexes...
  16. ncbi A proteomic analysis of maize chloroplast biogenesis
    Patricia M Lonosky
    Department of Genetics, Iowa State University, Ames, Iowa 50011, USA
    Plant Physiol 134:560-74. 2004
    ..Our experiments provide a foundation for the use of proteomics in the design of experiments to address fundamental questions in plant physiology and molecular biology...
  17. ncbi Is the hippocampus a Kalman filter?
    O Bousquet
    Dept of Computer Science, Iowa State University, Ames 50011, USA
    Pac Symp Biocomput . 1998
    ..This parallel allows us to derive statistically optimal update expressions for the localization performed by our computational model...
  18. ncbi RNABindR: a server for analyzing and predicting RNA-binding sites in proteins
    Michael Terribilini
    Department of Genetics, Development and Cell Biology, Bioinformatics and Computational Biology Program, Iowa State University, Ames, Iowa 50011, USA
    Nucleic Acids Res 35:W578-84. 2007
    ..RNABindR automatically displays 'high specificity' and 'high sensitivity' predictions of RNA-binding residues. RNABindR is freely available at http://bindr.gdcb.iastate.edu/RNABindR...
  19. ncbi On evaluating MHC-II binding peptide prediction methods
    Yasser El-Manzalawy
    Department of Computer Science, Center for Computational Intelligence, Learning, and Discovery, Iowa State University, Ames, Iowa, USA
    PLoS ONE 3:e3268. 2008
    ..These results underscore the importance of using similarity-reduced datasets in rigorously comparing the performance of alternative MHC-II peptide prediction methods...
  20. ncbi Predicting linear B-cell epitopes using string kernels
    Yasser El-Manzalawy
    Artificial Intelligence Laboratory, Iowa State University, Ames, IA 50010, USA
    J Mol Recognit 21:243-55. 2008
    ..Our homology-reduced data set and implementations of BCPred as well as the APP method are publicly available through our web-based server, BCPREDS, at: http://ailab.cs.iastate.edu/bcpreds/...
  21. ncbi Striking similarities in diverse telomerase proteins revealed by combining structure prediction and machine learning approaches
    Jae Hyung Lee
    Bioinformatics and Computational Biology Program, L H Baker Center for Bioinformatics and Biological Statistics, Iowa State University, Ames, IA 50010, USA
    Pac Symp Biocomput . 2008
    ..In addition, the combined evidence from machine learning and structural modeling identified several specific amino acids that are likely to play a role in binding DNA or RNA, but for which no experimental evidence is currently available...
  22. ncbi Constructive neural-network learning algorithms for pattern classification
    R Parekh
    Allstate Research and Planning Center, Menlo Park, CA 94025, USA
    IEEE Trans Neural Netw 11:436-51. 2000
    ..Additionally, we show how the incorporation of a local pruning step can eliminate several redundant neurons from MTiling-real networks...
  23. ncbi A neural-network architecture for syntax analysis
    C H Chen
    Advanced Technology Center, Computer and Communication Laboratories, Industrial Technology Research Institute, Chutung, Hsinchu, Taiwan, R O C
    IEEE Trans Neural Netw 10:94-114. 1999
    ....
  24. ncbi Animal trait ontology: The importance and usefulness of a unified trait vocabulary for animal species
    L M Hughes
    Department of Animal Science, Center for Integrated Animal Genomics, Iowa State University, 2255 Kildee Hall, Ames 50011, USA
    J Anim Sci 86:1485-91. 2008
    ..The ATO will eventually be linked to other species (e.g., human, rat, mouse) so that comparative analysis can be efficiently performed between species...
  25. ncbi HomPPI: a class of sequence homology based protein-protein interface prediction methods
    Li C Xue
    Department of Computer Science, Iowa State University, Ames, IA 50011, USA
    BMC Bioinformatics 12:244. 2011
    ....
  26. ncbi Semi-supervised prediction of protein subcellular localization using abstraction augmented Markov models
    Cornelia Caragea
    Artificial Intelligence Research Laboratory, Department of Computer Science, Iowa State University, Ames, IA 50010, USA
    BMC Bioinformatics 11:S6. 2010
    ..Hence, there is a growing interest in developing semi-supervised methods for predicting protein subcellular localization from large amounts of unlabeled data together with small amounts of labeled data...
  27. ncbi Mixture of experts models to exploit global sequence similarity on biomolecular sequence labeling
    Cornelia Caragea
    Artificial Intelligence Research Laboratory, Computer Science Department, Iowa State University, Ames, IA 50010, USA
    BMC Bioinformatics 10:S4. 2009
    ..Hence, there is a need to develop reliable computational methods for identifying functionally important sites from biomolecular sequences...
  28. ncbi Exploring inconsistencies in genome-wide protein function annotations: a machine learning approach
    Carson Andorf
    BMC Bioinformatics 8:284. 2007
    ..Editors Note: Authors from the original publication (Okazaki et al.: Nature 2002, 420:563-73) have provided their response to Andorf et al, directly following the correspondence...
  29. ncbi Predicting DNA-binding sites of proteins from amino acid sequence
    Changhui Yan
    Department of Computer Science, Utah State University, Logan, Utah 84341, USA
    BMC Bioinformatics 7:262. 2006
    ..We present a machine learning approach for the identification of amino acid residues involved in protein-DNA interactions...
  30. ncbi Detection of gene orthology from gene co-expression and protein interaction networks
    Fadi Towfic
    Bioinformatics and Computational Biology Graduate Program Iowa State University, Ames, USA
    BMC Bioinformatics 11:S7. 2010
    ....
  31. ncbi Glycosylation site prediction using ensembles of Support Vector Machine classifiers
    Cornelia Caragea
    Artificial Intelligence Research Laboratory, Computer Science Department, Iowa State University, USA
    BMC Bioinformatics 8:438. 2007
    ..Experimental identification of glycosylation sites is expensive and laborious. Hence, there is significant interest in the development of computational methods for reliable prediction of glycosylation sites from amino acid sequences...
  32. ncbi Use of machine learning algorithms to classify binary protein sequences as highly-designable or poorly-designable
    Myron Peto
    Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, IA 50011 3020, USA
    BMC Bioinformatics 9:487. 2008
    ....