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Tools & Databases

DCMinerDOMACHMMEditorMDockMULTICOMNNConPreDisorderProteinDBSSSpro 4.1SVMConVPhenoDBS


DCMiner

DCMiner
DCMiner is a web interface that offers abilities to effectively navigate the descriptive data mining findings from domain-concept partitions, where a domain-concept is a characteristic of the data. Users can compare, contrast, and aggregate findings across multiple domain-concepts, and view the findings using a variety of visualization techniques. DCMiner's target users are public health informaticians and professionals.

NIS: Mining Result Creation date: August, 2007.

Latest revision date: March, 2008.

BRFSS: Mining Result Creation date: November, 2003.

Latest revision date: May, 2008.

Link: http://medbio-ext.rnet.missouri.edu/kddm
http://medbio-ext.rnet.missouri.edu/dev_brfss


Publications:
  1. Wannapa Kay Mahamaneerat, Tetsuya Kobayashi, and Jason Green. Advisor: Chi-Ren Shyu. Domain-Concept Mining on the 2005 Nationwide Inpatient Sample Data, in American Medical Informatics Association (AMIA) 2007 Annual Symposium 2007
  2. Wannapa Kay Mahamaneerat, Laverne Alves Snow, and Chi-Ren Shyu. Mining Associations from the CDC’s Behavioral Risk Factor Surveillance System 2007
  3. Database to Assist Policy Making, in Enhancing Healthcare Education, Research & Practice Symposium, Hong Kong, China, July 2007
PI: Chi-Ren Shyu

DOMAC

DOMAC
Prediction of protein domain boundaries

Creation date: 2007

Latest revision date: 3/2008

Link: http://www.bioinfotool.org/domac.html

Publications:
  1. Jianlin Cheng, DOMAC: An Accurate, Hybrid Protein Domain Prediction Server, in Nucleic Acids Research, Vol. 35 2007; w354-356
PI: Jianlin Cheng

HMMEditor

HMMEditor
A visual editor for protein profile hidden Markov models

Creation date: 1/2007

Latest revision date: 1/2007

Link: http://casp.rnet.missouri.edu/hmmeditor/

Publications:
  1. J. Dai, and Jianlin Cheng. HMMEditor: A Visual Editing Tool for Profile Hidden Markov Model, in BMC Genomics, Vol. 9(S1):S8 2008
PI: Jianlin Cheng

MDock

MDock
MDock is an automated molecular docking software which can simultaneously docking ligands against multiple protein structures/conformations by using the ensemble docking algorithm (Huang and Zou, 2007a,b). It supports docking/optimizing ligand(s) against either a single protein structure or an ensemble of multiple protein structures and score calculations for given protein-ligand complexes. The energy function used in MDock is the knowledge-based scoring function, ITScore (Huang and Zou, 2006a,b).

MDock uses the sphere-ligand matching algorithm of UCSF DOCK to generate possible ligand conformations. Therefore, a UCSF DOCK license is required to generate the sphere points that represent a negative image of the protein to be matched with ligand atomic centers, for the use of MDock. UCSF DOCK is free for academic users. Files prepared for UCSF DOCK 4.0 can be applied to MDock without modification. In general, docking preparation would be easier for MDock than for UCSF DOCK, because MDock does not require adding hydrogens and charges for proteins and ligands.

In parallel to the method development for ligand-protein docking, studies on protein-protein docking are also under way (Huang and Zou, 2008).

Link: http://zoulab.dalton.missouri.edu/software.htm

Publications:
  1. Sheng-You Huang, and Xiaoqin Zou. An iterative knowledge-based scoring function for protein-protein recognition, in press 2008
  2. Sheng-You Huang, and Xiaoqin Zou. Ensemble docking of multiple protein structures: Considering protein structure variations in molecular docking, in Proteins: Structure, Function and Bioinformatics, Vol. 66 2007; 399-421
  3. Sheng-You Huang, and Xiaoqin Zou. Efficient molecular docking of NMR structures: Application to HIV-1 protease, in Protein Science, Vol. 16 2007; 43-51
  4. Sheng-You Huang, and Xiaoqin Zou. An iterative knowledge-based scoring function to predict protein-ligand interactions: I. Derivation of the interaction potentials, in Journal of Computational Chemistry, Vol. 27 2006; 1866-75
  5. Sheng-You Huang, and Xiaoqin Zou. An iterative knowledge-based scoring function to predict protein-ligand interactions: II. Validation of the scoring function, in Journal of Computational Chemistry, Vol. 27 2006; 1876-82
PI: Xiaoqin Zou

MULTICOM

MULTICOM
High-accuracy prediction of protein 3D structure using multi-template comparative modeling

Creation date: 2007

Latest revision date: 2/2008

Link: http://casp.rnet.missouri.edu/multicom/multicom.ht

Publications:
  1. Jianlin Cheng, A Multi-Template Combination Algorithm for Protein Comparative Modeling, in BMC Structural Biology 2008
PI: Jianlin Cheng

NNCon

NNCon
Fast prediction of protein residue-residue contacts using neural networks

Creation date: 1/2008

Latest revision date: 1/2008

Link: http://casp.rnet.missouri.edu/nncon.html

Publications:
  1. Jianlin Cheng, and P. Baldi. Three-Stage Prediction of Protein Beta-Sheets by Neural Networks, Alignments, and Graph Algorithms, in Proceedings of the 2005 Conference on Intelligent Systems for Molecular Biology (ISMB 2005), Bioinformatics, Vol. 21(suppl 1) 2005; i75-84
PI: Jianlin Cheng

PreDisorder

PreDisorder
Accurate prediction of protein disorder regions

Creation date: 9/2007

Latest revision date: 1/2008

Link: http://casp.rnet.missouri.edu/predisorder.html

Publications:
  1. J. Hecker, J. Yang, and Jianlin Cheng. Protein Disorder Prediction at Multiple Levels of Sensitivity and Specificity, in BMC Genomics, Vol. 9(S1):S9 2008
PI: Jianlin Cheng

ProteinDBS

ProteinDBS
Protein fold is known to be an important clue of detecting possible biological functions. The study of the structure-to-function relationships usually relies on an effective protein structure retrieval and classification method. The task of protein structure retrieval compares a query structure and each known proteins from a database and returns the ones with high similarities. The classification of protein structures categorizes and annotates a newly-discovered protein to possible folds, which could be relevant to the functional properties. With efforts of Structural Genomics (SG) projects, a large amount of protein structures has been identified in recent years via the high-throughput structural determination techniques such as X-ray crystallography and nuclear magnetic resonance (NMR). In the future, more new structures could be solved. To meet the needs of retrieving and classifying these high-throughput protein data, the research activities of this project are designed to face four central challenges.

  1. To compare globally similar 3D tertiary structures using content-based information retrieval (CBIR) and high-dimensional indexing techniques in real time.
  2. To efficiently classify newly-discovered proteins into the fold hiereachy of the Structural Classification of Protein (SCOP) database based on the structural similarity.
  3. To fast retrieve locally similar protein substructures with the non-contiguous structural core identifications in a large-scale protein database.
  4. To fuse the retrieval and classification results from different structure cores and provide suggestions to assist the functional predictions.


Link: http://proteindbs.rnet.missouri.edu/

Publications:
  1. Pin-Hao Chi, Chi-Ren Shyu, and Dong Xu. A fast SCOP fold classification system using content-based E-Predict algorithm, in BMC Bioinformatics, Vol. 7 2006; 362
  2. Pin-Hao Chi, and Chi-Ren Shyu. Predicting Ranked SCOP Domains by Mining Associations of Visual Contents in Distance Matrices, in Proc. of The Fourth Asia Pacific Bioinformatics Conference, Taipei, Taiwan 2006; 49-58
  3. Pin-Hao Chi, Grant Scott, and Chi-Ren Shyu. A fast protein structure retrieval system using image-based distance matrices and multidimensional index, in International Journal of Software Engineering and Knowledge Engineering, Special Issue on Software and Knowledge Engineering Support in Bioinformatics, Vol. 15, No. 3 2005; 527-545
  4. Chi-Ren Shyu, Pin-Hao Chi, Grant Scott, and Dong Xu. ProteinDBS - A content-based retrieval system for protein structure database, in Nucleic Acids Research, Vol. 32, July 2004; W572-W575
  5. Pin-Hao Chi, Grant Scott, and Chi-Ren Shyu. A fast protein structure retrieval system using image-based distance matrices and multidimensional index, in Proc. of IEEE Fourth Symposium on Bioinformatics and Bioengineering, Taichung, Taiwan 2004
PI: Chi-Ren Shyu

SSpro 4.1

SSpro 4.1
Accurate prediction of protein secondary structure using neural networks and homology modeling

Creation date: 2/2008

Latest revision date: 2/2008

Link: http://casp.rnet.missouri.edu/sspro4.html

Publications:
  1. Jianlin Cheng, A. Randall, M. Sweredoski, and P. Baldi. SCRATCH: a Protein Structure and Structural Feature Prediction Server, in Nucleic Acids Research, Vol. 33 2005; w72-76
PI: Jianlin Cheng

SVMCon

SVMCon
Prediction of protein residue-residue contacts using support vector machine

Creation date: 2007

Latest revision date: 2/2008

Link: http://casp.rnet.missouri.edu/svmcon.html

Publications:
  1. Jianlin Cheng, and P. Baldi. Improved Residue Contact Prediction Using Support Vector Machines and a Large Feature Set, in BMC Bioinformatics, Vol. 8, No. 113 2007
PI: Jianlin Cheng

VPhenoDBS

VPhenoDBS
Discoveries in biology often require extensive knowledge of the genetics of an organism, a keen eye for phenotypes, a deep understanding of related species, and efficient strategies for collecting, combining, analyzing, and comparing data. Currently, public database tools that retrieve phenotypic and genomic information allow only relatively simplistic queries, and viable software tools to capture, parse, and return information from digital images are lacking. We hope to enable biologists to simultaneously query phenotype data by image example, sequence, ontology, genetic and physical map information, and text annotations by developing the first web-based visual phenotypic information management system to allow such complex queries.

The database framework will consist of five modules:

  1. A system to extract and quantify low-level features from phenotypic images
  2. A high-dimensional database indexing system to manage and cluster images for real-time retrievals
  3. A linking hub to correlate visual features already attributed to a given locus with relevant genetic and physical maps
  4. A text mining and ontology utilization system for parsing annotations
  5. A results visualization system.


Link: http://phenomicsworld.org/search

Publications:
  1. Grant Scott, and Chi-Ren Shyu. Knowledge Driven Multidimensional Indexing Structure for Biomedical Media Database Retrieval, in IEEE Transactions on Information Technology in Biomedicine, Vol. 11, No. 3 , May 2007; 320-331
  2. Chi-Ren Shyu, Jason Green, D. P. K. Lun, Toni Kazic, M. Schaeffer, and Ed Coe. Image Analysis for Mapping Immeasurable Phenotypes in Maize, in IEEE Signal Processing Magazine, Vol. 24, No. 3 , May 2007; 116-119
  3. Chi-Ren Shyu, Jaturon Harnsomburana, Jason Green, Adrian Barb, Toni Kazic, M. Schaeffer, and Ed Coe. Searching and mining visually-observed phenotypes of maize mutants, in Journal of Bioinformatics and Computational Biology, Special Issue on Making Sense of Mutations Requires Knowledge Management, Vol. 5, No. 6 , December 2007; 1193-1213
  4. Jason Green, and Chi-Ren Shyu. A Computational Approach for Scoring Visually Observed Phenotypic Expression in Maize, in The 49th Maize Genetics Conference, March 2007
  5. Toni Kazic, Jason Green, Jaturon Harnsomburana, and Chi-Ren Shyu. Quantification of Lesion Phenotypes as a Function of Inbred Background, in The 49th Maize Genetics Conference, March 2007
  6. Chi-Ren Shyu, Jason Green, Cynthia Farmer, Toni Kazic, Ed Coe, M. Schaeffer, M. Millard, P. Cyr, and C. Gardner. A Computational Approach for Characterizing Standardized Phenotypic Images for Maize, in The 48th Maize Genetics Conference 2006
  7. Chi-Ren Shyu, Jason Green, Cynthia Farmer, Toni Kazic, Ed Coe, M. Schaeffer, C. Lawerence, M. Millard, P. Cyr, and C. Gardner. A Framework of Complex Queries For Plant Phenotype Image Databases, in The International Conference on Plant, Animal, and Microbe Genomes XIV 2006
PI: Chi-Ren Shyu
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Last modified: September 05, 2008
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