Publications Citing SVM-Prot:
Zook, J.; Mo, G.; Sisco, N. J.; Craciunescu, F. M.; Hansen, D. T.; Baravati, B.; Cherry, B. R.; Sykes, K.; Wachter, R.; Van Horn, W. D.; Fromme, P., NMR Structure of Francisella tularensis Virulence Determinant Reveals Structural Homology to Bet v1 Allergen Proteins. Structure 2015, 23 (6), 1116-1122.
Yousef, A.; Charkari, N. M., A novel method based on physicochemical properties of amino acids and one class classification algorithm for disease gene identification. Journal of Biomedical Informatics 2015, 56, 300-306.
Wu, Q. Y.; Wang, Z. Y.; Li, C. S.; Ye, Y. M.; Li, Y. P.; Sun, N., Protein functional properties prediction in sparsely-label PPI networks through regularized non-negative matrix factorization. Bmc Systems Biology 2015, 9.
Sinha, A.; Ahmad, F.; Hassan, M. I., Structure Based Functional Annotation of Putative Conserved Proteins from Treponema pallidum: Search for a Potential Drug Target. Letters in Drug Design & Discovery 2015, 12 (1), 46-59.
Singh, B. P.; Jayaswal, P. K.; Singh, B.; Singh, P. K.; Kumar, V.; Mishra, S.; Singh, N.; Panda, K.; Singh, N. K., Natural allelic diversity in OsDREB1F gene in the Indian wild rice germplasm led to ascertain its association with drought tolerance. Plant Cell Reports 2015, 34 (6), 993-1004.
Shahbaaz, M.; Ahmad, F.; Hassan, M. I., Structure-based functional annotation of putative conserved proteins having lyase activity from Haemophilus influenzae. 3 Biotech 2015, 5 (3), 317-336.
Shahbaaz, M.; Ahmad, F.; Hassan, M. I., Structure-based function analysis of putative conserved proteins with isomerase activity from Haemophilus influenzae. 3 Biotech 2015, 5 (5), 741-763.
Naqvi, A. A. T.; Ahmad, F.; Hassan, M. I., Identification of functional candidates amongst hypothetical proteins of Mycobacterium leprae Br4923, a causative agent of leprosy. Genome 2015, 58 (1), 25-42.
Islam, S. M. A.; Sajed, T.; Kearney, C. M.; Baker, E. J., PredSTP: a highly accurate SVM based model to predict sequential cystine stabilized peptides. Bmc Bioinformatics 2015, 16.
Hu, Y. Y.; Guo, Y. Z.; Shi, Y. N.; Li, M. L.; Pu, X. M., A consensus subunit-specific model for annotation of substrate specificity for ABC transporters. Rsc Advances 2015, 5 (52), 42009-42019.
Foong, P. M.; Karjiban, R. A.; Normi, Y. M.; Salleha, A. B.; Rahman, M. B. A., Bioinformatics survey of the metal usage by psychrophilic yeast Glaciozyma antarctica PI12. Metallomics 2015, 7 (1), 156-164.
Farhadi, T.; Nezafat, N.; Ghasemi, Y.; Karimi, Z.; Hemmati, S.; Erfani, N., Designing of Complex Multi-epitope Peptide Vaccine Based on Omps of Klebsiella pneumoniae: An In Silico Approach. International Journal of Peptide Research and Therapeutics 2015, 21 (3), 325-341.
Dwivedi, U. N.; Tiwari, S.; Singh, P.; Singh, S.; Awasthi, M.; Pandey, V. P., Treponema pallidum Putative Novel Drug Target Identification and Validation: Rethinking Syphilis Therapeutics with Plant-Derived Terpenoids. Omics-a Journal of Integrative Biology 2015, 19 (2), 104-114.
Cheng, W. D.; Cai, C. Z.; Luo, Y.; Li, Y. H.; Zhao, C. J., Mechanical properties prediction for carbon nanotubes/epoxy composites by using support vector regression. Modern Physics Letters B 2015, 29 (5).
Chen, S. H.; Byrne, R. T.; Wood, E. A.; Cox, M. M., Escherichia coli radD (yejH) gene: a novel function involved in radiation resistance and double-strand break repair. Molecular Microbiology 2015, 95 (5), 754-768.
Zou, H. L., A Multi-label Classifier for Prediction Membrane Protein Functional Types in Animal. Journal of Membrane Biology 2014, 247 (11), 1141-1148.
Zheng, H. P.; Chordia, M. D.; Cooper, D. R.; Chruszcz, M.; Muller, P.; Sheldrick, G. M.; Minor, W., Validation of metal-binding sites in macromolecular structures with the CheckMyMetal web server. Nature Protocols 2014, 9 (1), 156-170.
Zhang, Y. P.; Xu, J.; Zheng, W.; Zhang, C.; Qiu, X. Y.; Chen, K.; Ruan, J. S., newDNA-Prot: Prediction of DNA-binding proteins by employing support vector machine and a comprehensive sequence representation. Computational Biology and Chemistry 2014, 52, 51-59.
Yang, L.; Wang, J. Z.; Wang, H. P.; Lv, Y. L.; Zuo, Y. C.; Jiang, W., Analysis and identification of toxin targets by topological properties in protein-protein interaction network. Journal of Theoretical Biology 2014, 349, 82-91.
Xu, R. F.; Zhou, J. Y.; Liu, B.; Yao, L.; He, Y. L.; Zou, Q.; Wang, X. L., enDNA-Prot: Identification of DNA-Binding Proteins by Applying Ensemble Learning. Biomed Research International 2014.
Wu, Q. Y.; Ye, Y. M.; Ho, S. S.; Zhou, S. G., Semi-supervised multi-label collective classification ensemble for functional genomics. Bmc Genomics 2014, 15.
Weirick, T.; Sahu, S. S.; Mahalingam, R.; Kaundal, R., LacSubPred: predicting subtypes of Laccases, an important lignin metabolism- related enzyme class, using in silico approaches. Bmc Bioinformatics 2014, 15.
Wang, Z.; Zou, Q.; Jiang, Y.; Ju, Y.; Zeng, X. X., Review of Protein Subcellular Localization Prediction. Current Bioinformatics 2014, 9 (3), 331-342.
Wang, Y. L.; Jing, R. Y.; Hua, Y. P.; Fu, Y. Y.; Dai, X.; Huang, L. Q.; Li, M. L., Classification of multi-family enzymes by multi-label machine learning and sequence-based descriptors. Analytical Methods 2014, 6 (17), 6832-6840.
Uddin, R.; Saeed, K., Identification and characterization of potential drug targets by subtractive genome analyses of methicillin resistant Staphylococcus aureus. Computational Biology and Chemistry 2014, 48, 55-63.
Song, L.; Li, D. P.; Zeng, X. X.; Wu, Y. F.; Guo, L.; Zou, Q., nDNA-prot: identification of DNA-binding proteins based on unbalanced classification. Bmc Bioinformatics 2014, 15.
Omer, A.; Singh, P.; Yadav, N. K.; Singh, R. K., An Overview of Data Mining Algorithms in Drug Induced Toxicity Prediction. Mini-Reviews in Medicinal Chemistry 2014, 14 (4), 345-354.
Niu, B.; Zhang, Y. C.; Ding, J.; Lu, Y.; Wang, M.; Lu, W. C.; Yuan, X. C.; Yin, J. Y., Predicting network of drug-enzyme interaction based on machine learning method. Biochimica Et Biophysica Acta-Proteins and Proteomics 2014, 1844 (1), 214-223.
Kucukkal, T. G.; Yang, Y.; Chapman, S. C.; Cao, W. G.; Alexov, E., Computational and Experimental Approaches to Reveal the Effects of Single Nucleotide Polymorphisms with Respect to Disease Diagnostics. International Journal of Molecular Sciences 2014, 15 (6), 9670-9717.
Klus, P.; Bolognesi, B.; Agostini, F.; Marchese, D.; Zanzoni, A.; Tartaglia, G. G., The cleverSuite approach for protein characterization: predictions of structural properties, solubility, chaperone requirements and RNA-binding abilities. Bioinformatics 2014, 30 (11), 1601-1608.
Hosen, M. I.; Tanmoy, A. M.; Mahbuba, D. A.; Salma, U.; Nazim, M.; Islam, M. T.; Akhteruzzaman, S., Application of a Subtractive Genomics Approach for in silico Identification and Characterization of Novel Drug Targets in Mycobacterium tuberculosis F11. Interdisciplinary Sciences-Computational Life Sciences 2014, 6 (1), 48-56.
De Ferrari, L.; Mitchell, J. B. O., From sequence to enzyme mechanism using multi-label machine learning. Bmc Bioinformatics 2014, 15.
Bakhtiarizadeh, M. R.; Moradi-Shahrbabak, M.; Ebrahimi, M.; Ebrahimie, E., Neural network and SVM classifiers accurately predict lipid binding proteins, irrespective of sequence homology. Journal of Theoretical Biology 2014, 356, 213-222.
Azam, S. S.; Shamim, A., An insight into the exploration of druggable genome of Streptococcus gordonii for the identification of novel therapeutic candidates. Genomics 2014, 104 (3), 203-214.
Zou, Q.; Wang, Z.; Guan, X. J.; Liu, B.; Wu, Y. F.; Lin, Z. Y., An Approach for Identifying Cytokines Based on a Novel Ensemble Classifier. Biomed Research International 2013.
Zou, Q.; Li, X. B.; Jiang, Y.; Zhao, Y. M.; Wang, G. H., BinMemPredict: a Web Server and Software for Predicting Membrane Protein Types. Current Proteomics 2013, 10 (1), 2-9.
Zou, Q.; Chen, W. C.; Huang, Y.; Liu, X. R.; Jiang, Y., Identifying Multi-Functional Enzyme by Hierarchical Multi-Label Classifier. Journal of Computational and Theoretical Nanoscience 2013, 10 (4), 1038-1043.
Zou, C. X.; Gong, J. Y.; Li, H. L., An improved sequence based prediction protocol for DNA-binding proteins using SVM and comprehensive feature analysis. Bmc Bioinformatics 2013, 14.
Xiao, T. T.; Cai, C. Z.; Tang, J. L.; Huang, S. J., MODELING OF TRANSITION TEMPERATURE FOR PULSED LASER DEPOSITION NdBa2Cu3O7-delta THIN FILMS VIA SUPPORT VECTOR REGRESSION. International Journal of Modern Physics B 2013, 27 (15).
Shahbaaz, M.; Hassan, M. I.; Ahmad, F., Functional Annotation of Conserved Hypothetical Proteins from Haemophilus influenzae Rd KW20. Plos One 2013, 8 (12).
Sarangi, A. N.; Lohani, M.; Aggarwal, R., Prediction of Essential Proteins in Prokaryotes by Incorporating Various Physico-chemical Features into the General form of Chou's Pseudo Amino Acid Composition. Protein and Peptide Letters 2013, 20 (7), 781-795.
Sahoo, G. C.; Dikhit, M. R.; Rani, M.; Ansari, M. Y.; Jha, C.; Rana, S.; Das, P., Analysis of sequence, structure of GAPDH of Leishmania donovani and its interactions. Journal of Biomolecular Structure & Dynamics 2013, 31 (3), 258-275.
Saha, S.; Chaki, R., A Brief Review of Data Mining Application Involving Protein Sequence Classification. In Advances in Computing and Information Technology, Vol 2, Meghanathan, N.; Nagamalai, D.; Chaki, N., Eds. 2013; Vol. 177, pp 469-477.
Rani, M.; Nischal, A.; Sahoo, G. C.; Khattri, S., Computational Analysis of the 3-D structure of Human GPR87 Protein: Implications for Structure-Based Drug Design. Asian Pacific Journal of Cancer Prevention 2013, 14 (12), 7473-7482.
Pei, J. F.; Cai, C. Z.; Zhu, Y. M.; Yan, B., Modeling and Predicting the Glass Transition Temperature of Polymethacrylates Based on Quantum Chemical Descriptors by Using Hybrid PSO-SVR. Macromolecular Theory and Simulations 2013, 22 (1), 52-60.
Pei, J. F.; Cai, C. Z.; Zhu, Y. M., MODELING AND PREDICTING THE GLASS TRANSITION TEMPERATURE OF VINYL POLYMERS BY USING HYBRID PSO-SVR METHOD. Journal of Theoretical & Computational Chemistry 2013, 12 (3).
Nicolaou, S. A.; Fast, A. G.; Nakamaru-Ogiso, E.; Papoutsakis, E. T., Overexpression of fetA (ybbL) and fetB (ybbM), Encoding an Iron Exporter, Enhances Resistance to Oxidative Stress in Escherichia coli. Applied and Environmental Microbiology 2013, 79 (23), 7210-7219.
Niarchou, A.; Alexandridou, A.; Athanasiadis, E.; Spyrou, G., C-PAmP: Large Scale Analysis and Database Construction Containing High Scoring Computationally Predicted Antimicrobial Peptides for All the Available Plant Species. Plos One 2013, 8 (11).
Malhotra, A.; Creer, S.; Harris, J. B.; Stocklin, R.; Favreau, P.; Thorpe, R. S., Predicting function from sequence in a large multifunctional toxin family. Toxicon 2013, 72, 113-125.
Ma, X.; Wu, J. S.; Xue, X. Y., Identification of DNA-Binding Proteins Using Support Vector Machine with Sequence Information. Computational and Mathematical Methods in Medicine 2013.
Lin, C.; Zou, Y.; Qin, J.; Liu, X. R.; Jiang, Y.; Ke, C. H.; Zou, Q., Hierarchical Classification of Protein Folds Using a Novel Ensemble Classifier. Plos One 2013, 8 (2).
Kuksa, P. P., Biological Sequence Classification with Multivariate String Kernels. Ieee-Acm Transactions on Computational Biology and Bioinformatics 2013, 10 (5), 1201-1210.
Jaramillo-Garzon, J. A.; Gallardo-Chacon, J. J.; Castellanos-Dominguez, C. G.; Perera-Lluna, A., Predictability of gene ontology slim-terms from primary structure information in Embryophyta plant proteins. Bmc Bioinformatics 2013, 14.
Hosseinzadeh, F.; KayvanJoo, A. H.; Ebrahimi, M.; Goliaei, B., Prediction of lung tumor types based on protein attributes by machine learning algorithms. Springerplus 2013, 2.
de la Iglesia, D.; Garcia-Remesal, M.; de la Calle, G.; Kulikowski, C.; Sanz, F.; Maojo, V., The Impact of Computer Science in Molecular Medicine: Enabling High-Throughput Research. Current Topics in Medicinal Chemistry 2013, 13 (5), 526-575.
Cao, D. S.; Liang, Y. Z.; Yan, J.; Tan, G. S.; Xu, Q. S.; Liu, S., PyDPI: Freely Available Python Package for Chemoinformatics, Bioinformatics, and Chemogenomics Studies. Journal of Chemical Information and Modeling 2013, 53 (11), 3086-3096.
Cai, C. Z.; Xiao, T. T.; Tang, J. L.; Huang, S. J., Analysis of process parameters in the laser deposition of YBa2Cu3O7 superconducting films by using SVR. Physica C-Superconductivity and Its Applications 2013, 493, 100-103.
Zhao, X. W.; Ma, Z. Q.; Yin, M. H., Predicting Protein-Protein Interactions by Combing Various Sequence-Derived Features into the General Form of Chou's Pseudo Amino Acid Composition. Protein and Peptide Letters 2012, 19 (5), 492-500.
Tang, J. L.; Cai, C. Z.; Zhu, X. J.; Wang, G. L.; Cao, D. F., Modeling and Predicting Tensile Strength of Tungsten Alloy by Using PSO-SVR. In Future Material Research and Industry Application, Pts 1 and 2, Thaung, K. S., Ed. 2012; Vol. 455-456, pp 1497-1503.
Tang, J. L.; Cai, C. Z.; Xiao, T. T.; Huang, S. J., MODELING AND PREDICTING THE ELECTRICAL CONDUCTIVITY OF COMPOSITE CATHODE FOR SOLID OXIDE FUEL CELL BY USING SUPPORT VECTOR REGRESSION. International Journal of Modern Physics B 2012, 26 (13).
Tang, J. L.; Cai, C. Z.; Xiao, T. T.; Huang, S. J., SUPPORT VECTOR REGRESSION MODEL FOR DIRECT METHANOL FUEL CELL. International Journal of Modern Physics C 2012, 23 (7).
Tang, J. L.; Cai, C. Z.; Xiao, T. T.; Huang, S. J., Modeling and Predicting the Central Magnetic Flux Density of the Superconducting Solenoid Surrounded with Iron Yoke via SVR. Journal of Superconductivity and Novel Magnetism 2012, 25 (6), 1747-1751.
Sugahara, J.; Fujishima, K.; Nunoura, T.; Takaki, Y.; Takami, H.; Takai, K.; Tomita, M.; Kanai, A., Genomic Heterogeneity in a Natural Archaeal Population Suggests a Model of tRNA Gene Disruption. Plos One 2012, 7 (3).
Rana, S.; Dikhit, M. R.; Rani, M.; Moharana, K. C.; Sahoo, G. C.; Das, P., CPDB: cysteine protease annotation database in Leishmania species. Integrative Biology 2012, 4 (11), 1351-1357.
Pei, J. F.; Cai, C. Z.; Zhu, X. J.; Wang, G. L.; Yan, B., Prediction of Glass Transition Temperature of Polymer by Support Vector Regression. In Future Material Research and Industry Application, Pts 1 and 2, Thaung, K. S., Ed. 2012; Vol. 455-456, pp 436-442.
Pei, J. F.; Cai, C. Z.; Zhu, X. J.; Wang, G. L.; Yan, B., Modeling the Glass Transition Temperature of Polymers via Multipole Moments Using Support Vector Regression. In Future Material Research and Industry Application, Pts 1 and 2, Thaung, K. S., Ed. 2012; Vol. 455-456, pp 430-435.
Pei, J. F.; Cai, C. Z.; Tang, J. L.; Zhao, S.; Yuan, F. Q., Prediction of the Glass Transition Temperatures of Styrenic Copolymers by Using Support Vector Regression Combined with Particle Swarm Optimization. Journal of Macromolecular Science Part B-Physics 2012, 51 (7), 1437-1448.
Miklos, I.; Zadori, Z., Positive Evolutionary Selection of an HD Motif on Alzheimer Precursor Protein Orthologues Suggests a Functional Role. Plos Computational Biology 2012, 8 (2).
Li, Z. C.; Lai, Y. H.; Chen, L. L.; Zhou, X.; Dai, Z.; Zou, X. Y., Identification of human protein complexes from local sub-graphs of protein-protein interaction network based on random forest with topological structure features. Analytica Chimica Acta 2012, 718, 32-41.
Huo, T.; Zhang, Y. J.; Lin, J. P., Functional annotation from the genome sequence of the giant panda. Protein & Cell 2012, 3 (8), 602-608.
Hu, L. L.; Feng, K. Y.; Gu, L.; Liu, X. J., Prediction of Protein Quaternary Structure with Feature Selection and Analysis Based on Protein Biological Features. Protein and Peptide Letters 2012, 19 (1), 23-28.
Hosseinzadeh, F.; Ebrahimi, M.; Goliaei, B.; Shamabadi, N., Classification of Lung Cancer Tumors Based on Structural and Physicochemical Properties of Proteins by Bioinformatics Models. Plos One 2012, 7 (7).
Hayat, M.; Khan, A., Mem-PHybrid: Hybrid features-based prediction system for classifying membrane protein types. Analytical Biochemistry 2012, 424 (1), 35-44.
Hallinan, J. S., Data mining for microbiologists. In Systems Biology of Bacteria, Harwood, C.; Wipat, A., Eds. 2012; Vol. 39, pp 27-79.
Cheng, X. Y.; Huang, W. J.; Hu, S. C.; Zhang, H. L.; Wang, H.; Zhang, J. X.; Lin, H. H.; Chen, Y. Z.; Zou, Q.; Ji, Z. L., A Global Characterization and Identification of Multifunctional Enzymes. Plos One 2012, 7 (6).
Chen, W.; Liu, X.; Huang, Y.; Jiang, Y.; Zou, Q.; Lin, C., Improved method for predicting protein fold patterns with ensemble classifiers. Genetics and Molecular Research 2012, 11 (1), 174-181.
Chen, J. G.; Xiong, J.; Cui, B. J.; Yang, J. F.; Li, W. C.; Mao, Z. J., Molecular characterization of eight segments of Scylla serrata reovirus (SsRV) provides the complete genome sequence. Archives of Virology 2012, 157 (8), 1551-1557.
Cao, D. S.; Liu, S.; Xu, Q. S.; Lu, H. M.; Huang, J. H.; Hu, Q. N.; Liang, Y. Z., Large-scale prediction of drug-target interactions using protein sequences and drug topological structures. Analytica Chimica Acta 2012, 752, 1-10.
Zeng, Q. G.; Yue, G. X.; Li, R. F., Prediction of Protein Functional Class from Pseudo-Amino Acid Composition. Journal of Computational and Theoretical Nanoscience 2011, 8 (7), 1247-1251.
Yu, X. Q.; Liu, T. G.; Zheng, X. Q.; Yang, Z. N.; Wang, J., Prediction of regulatory interactions in Arabidopsis using gene-expression data and support vector machines. Plant Physiology and Biochemistry 2011, 49 (3), 280-283.
Wang, G. L.; Cai, C. Z.; Pei, J. F.; Zhu, X. J., Prediction of thermal conductivity of polymer-based composites by using support vector regression. Science China-Physics Mechanics & Astronomy 2011, 54 (5), 878-883.
Umamaheswari, A.; Kumar, M. M.; Pradhan, D.; Marisetty, H., Docking Studies towards Exploring Antiviral Compounds against Envelope Protein of Yellow Fever Virus. Interdisciplinary Sciences-Computational Life Sciences 2011, 3 (1), 64-77.
Udatha, D.; Kouskoumvekaki, I.; Olsson, L.; Panagiotou, G., The interplay of descriptor-based computational analysis with pharmacophore modeling builds the basis for a novel classification scheme for feruloyl esterases. Biotechnology Advances 2011, 29 (1), 94-110.
Tang, J. L.; Cai, C. Z.; Zhu, X. J.; Wang, G. L., SVR-Based Predictive Model for Purity of the Mg-Al-Hydrotalcite. In Manufacturing Process Technology, Pts 1-5, Jiang, Z. Y.; Li, S. Q.; Zeng, J. M.; Liao, X. P.; Yang, D. G., Eds. 2011; Vol. 189-193, pp 1482-1485.
Song, X. F.; Zhou, T.; Jia, H.; Guo, X. J.; Zhang, X. B.; Han, P.; Sha, J. H., SProtP: A Web Server to Recognize Those Short-Lived Proteins Based on Sequence-Derived Features in Human Cells. Plos One 2011, 6 (11).
Rao, H. B.; Zhu, F.; Yang, G. B.; Li, Z. R.; Chen, Y. Z., Update of PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence. Nucleic Acids Research 2011, 39, W385-W390.
Pei, J. F.; Cai, C. Z.; Zhu, X. J.; Wang, G. L., Investigation on the Processing-Properties of Hot Deformed TA15 Titanium Alloy via Support Vector Regression. In Materials Modeling, Simulation, and Characterization, Han, E.; Lu, G. H.; Shu, X. L., Eds. 2011; Vol. 689, pp 134-143.
Libault, M.; Govindarajulu, M.; Berg, R. H.; Ong, Y. T.; Puricelli, K.; Taylor, C. G.; Xu, D.; Stacey, G., A Dual-Targeted Soybean Protein Is Involved in Bradyrhizobium japonicum Infection of Soybean Root Hair and Cortical Cells. Molecular Plant-Microbe Interactions 2011, 24 (9), 1051-1060.
Jiang, Z. R.; Yu, W. M.; Tao, R., Predicting important classes of chemokine family based on kernel method. Computer Physics Communications 2011, 182 (8), 1606-1609.
Jeong, J. C.; Lin, X. T.; Chen, X. W., On Position-Specific Scoring Matrix for Protein Function Prediction. Ieee-Acm Transactions on Computational Biology and Bioinformatics 2011, 8 (2), 308-315.
Janga, S. C.; Diaz-Mejia, J. J.; Moreno-Hagelsieb, G., Network-based function prediction and interactomics: The case for metabolic enzymes. Metabolic Engineering 2011, 13 (1), 1-10.
Hu, L. L.; Zheng, L. L.; Wang, Z. W.; Li, B.; Liu, L., Using Pseudo Amino Acid Composition to Predict Protease Families by Incorporating a Series of Protein Biological Features. Protein and Peptide Letters 2011, 18 (6), 552-558.
Fan, D.; Liu, Z. M.; Jin, H. W.; Zhang, L. R., Classification of Coenzyme-A Binding Proteins Based on Co-Factor Binding Modes. Acta Physico-Chimica Sinica 2011, 27 (5), 1223-1231.
du Plessis, L.; Skunca, N.; Dessimoz, C., The what, where, how and why of gene ontology-a primer for bioinformaticians. Briefings in Bioinformatics 2011, 12 (6), 723-735.
Ciavardelli, D.; Ammendola, S.; Ronci, M.; Consalvo, A.; Marzano, V.; Lipoma, M.; Sacchetta, P.; Federici, G.; Di Ilio, C.; Battistoni, A.; Urbani, A., Phenotypic profile linked to inhibition of the major Zn influx system in Salmonella enterica: proteomics and ionomics investigations. Molecular Biosystems 2011, 7 (3), 608-619.
Cai, C. Z.; Zhu, X. J.; Pei, J. F.; Wang, G. L., Study on the Process Optimization of Synthesizing Co3O4 Nanoparticles by Homogeneous Precipitation Based on Support Vector Regression. In Materials Modeling, Simulation, and Characterization, Han, E.; Lu, G. H.; Shu, X. L., Eds. 2011; Vol. 689, pp 211-219.
Bologna, G.; Veuthey, A. L.; Pagni, M.; Lane, L.; Bairoch, A., A Preliminary Study on the Prediction of Human Protein Functions. In Foundations on Natural and Artificial Computation: 4th International Work-Conference on the Interplay between Natural and Artificial Computation, Iwinac 2011, Part I, Ferrandez, J. M.; Sanchez, J. R. A.; DeLaPaz, F.; Toledo, F. J., Eds. 2011; Vol. 6686, pp 334-343.
Abdullah, F. M.; Othman, R. M.; Kasim, S.; Hashim, R., An Optimal Mesh Algorithm for Remote Protein Homology Detection. In Ubiquitous Computing and Multimedia Applications, Pt Ii, Kim, T. H.; Adeli, H.; Robles, R. J.; Balitanas, M., Eds. 2011; Vol. 151, pp 471-497.
Zhang, L. C.; Li, W.; Song, L. L.; Chen, L. N., A towards-multidimensional screening approach to predict candidate genes of rheumatoid arthritis based on SNP, structural and functional annotations. Bmc Medical Genomics 2010, 3.
Yang, X. Y.; Shi, X. H.; Meng, X.; Li, X. L.; Lin, K.; Qian, Z. L.; Feng, K. Y.; Kong, X. Y.; Cai, Y. D., Classification of Transcription Factors Using Protein Primary Structure. Protein and Peptide Letters 2010, 17 (7), 899-908.
Yang, X. G.; Cong, Y.; Xue, Y., Identification of vasodilators from molecular descriptors by machine learning methods. Chemometrics and Intelligent Laboratory Systems 2010, 101 (2), 95-101.
Xia, J. F.; Han, K.; Huang, D. S., Sequence-Based Prediction of Protein-Protein Interactions by Means of Rotation Forest and Autocorrelation Descriptor. Protein and Peptide Letters 2010, 17 (1), 137-145.
Wigoda, N.; Ma, X. H.; Moran, N., Phosphatidylinositol 4,5-bisphosphate regulates plant K+ channels. Biochemical Society Transactions 2010, 38, 705-709.
Sarac, O. S.; Atalay, V.; Cetin-Atalay, R., GOPred: GO Molecular Function Prediction by Combined Classifiers. Plos One 2010, 5 (8).
Rao, H. B.; Li, Z. R.; Li, X. Y.; Ma, X. H.; Ung, C. Y.; Li, H.; Liu, X. H.; Chen, Y. Z., Identification of Small Molecule Aggregators From Large Compound Libraries by Support Vector Machines. Journal of Computational Chemistry 2010, 31 (4), 752-763.
Pugalenthi, G.; Kandaswamy, K. K.; Suganthan, P. N.; Archunan, G.; Sowdhamini, R., Identification of functionally diverse lipocalin proteins from sequence information using support vector machine. Amino Acids 2010, 39 (3), 777-783.
Parra, M. C.; Shaffer, S. A.; Hajjar, A. M.; Gallis, B. M.; Hager, A.; Goodlett, D. R.; Guina, T.; Miller, S.; Collins, C. M., Identification, cloning, expression, and purification of Francisella lpp3: An immunogenic lipoprotein. Microbiological Research 2010, 165 (7), 531-545.
Panwar, B.; Raghava, G. P. S., Prediction and classification of aminoacyl tRNA synthetases using PROSITE domains. Bmc Genomics 2010, 11.
Mahadevan, P.; Seto, D., Taxonomic Parsing of Bacteriophages Using Core Genes and In Silico Proteome-Based CGUG and Applications to Small Bacterial Genomes. In Advances in Computational Biology, Arabnia, H. R., Ed. 2010; Vol. 680, pp 379-385.
Lu, L. Y.; Qian, Z. L.; Shi, X. H.; Li, H. P.; Cai, Y. D.; Li, Y. X., A knowledge-based method to predict the cooperative relationship between transcription factors. Molecular Diversity 2010, 14 (4), 815-819.
Liu, X. H.; Song, H. Y.; Ma, X. H.; Lear, M. J.; Chen, Y. Z., Virtual screening prediction of new potential organocatalysts for direct aldol reactions. Journal of Molecular Catalysis a-Chemical 2010, 319 (1-2), 114-118.
Lin, K.; Qian, Z. L.; Lu, L.; Lu, L. Y.; Lai, L. H.; Gu, J. Y.; Zeng, Z. B.; Li, H. P.; Cai, Y. D., Predicting miRNA's target from primary structure by the nearest neighbor algorithm. Molecular Diversity 2010, 14 (4), 719-729.
Kirberger, M.; Wang, X.; Zhao, K.; Tang, S.; Chen, G. T.; Yang, J. J., Integration of Diverse Research Methods to Analyze and Engineer Ca2+-Binding Proteins: From Prediction to Production. Current Bioinformatics 2010, 5 (1), 68-80.
Huang, C.; Zhang, R. J.; Chen, Z. Q.; Jiang, Y. S. A.; Shang, Z. W.; Sun, P.; Zhang, X. H.; Li, X., Predict potential drug targets from the ion channel proteins based on SVM. Journal of Theoretical Biology 2010, 262 (4), 750-756.
He, Z. S.; Zhang, J.; Shi, X. H.; Hu, L. L.; Kong, X. Y.; Cai, Y. D.; Chou, K. C., Predicting Drug-Target Interaction Networks Based on Functional Groups and Biological Features. Plos One 2010, 5 (3).
Fischer-Kilbienski, I.; Miao, Y.; Roitsch, T.; Zschiesche, W.; Humbeck, K.; Krupinska, K., Nuclear targeted AtS40 modulates senescence associated gene expression in Arabidopsis thaliana during natural development and in darkness. Plant Molecular Biology 2010, 73 (4-5), 379-390.
Dong, Q. W.; Zhou, S. G.; Deng, L.; Guan, J. H., Gene Ontology-Based Protein Function Prediction by Using Sequence Composition Information. Protein and Peptide Letters 2010, 17 (6), 789-795.
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