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labeling, called Multi-Q. This tool is designed as a   ously established fragment library and by further               Entropy (ME) model and Conditional Random        will construct the eukaryotic protein-protein interac-
                 generic platform that can accommodate various in-  developing new algorithms to dissect the sequence-                  Fields (CRF) as the underlying machine learning   tion network from recent high though-put interac-
                 put data formats from different mass spectrometers   structure relationship. We have successfully dem-                 methods, and incorporate dictionary-based and rule-  tome studies for various species. All the interactions
                 and search engines. This work is in collaboration   onstrated that our fragment library provides a good                based methods as post-processing of ME to enhance   will be converted into domain-domain interactions
                 with the Institute of Chemistry. According to the   basis set of building blocks for reconstructing and                the performance. Once named entities can be recog-  and then the conserved network motifs will be ex-
                 chemists, our software is the most advanced tool   predicting whole protein structures. However, the                   nized, we then aim to recognize relations between   tracted to infer protein interactome related to human
                 available in the world. In addition, we are develop-  exact nature of the relationship between a protein’              named entities. We collaborate with biologists to   diseases. Using this model, we will build a powerful
                 ing a tool for ICAT labeling quantitation. In the   s sequence and its structure remains one of the open               work on the problems of recognizing protein-protein   tool to discover unknown interacting protein pairs
                 future, we will adapt our tools to two-plex or mul-  challenges in computational biology. To discover                  interaction relations and gene-disease relations. Re-  with a probability score. According to the conserved   Research Groups
                 tiplexed quantitative analysis using other isotopic   the relationship of protein’s sequence and its struc-            lated paper has appeared in BMC Bioinformatics   network model with spatio-temporal information,
                 labeling strategies. Moreover, tools for visualization   ture is quite important and worth our effort.                 2006.                                            the interactions between pathogens and human, and
                 to assist in the biological interpretation of the data                                                                                                                  the procession of carcinogenesis will be deciphered.
                                                                  NMR backbone resonance assignment and NOE                             Genomic information retrieval
                 will also be developed.                                                                                                                                                 The critical target proteins in those networks will
                                                                  experiment
                                                                                                                                            The Intelligent Agent Systems Lab (IASL)     be unrevealed by the topological analysis of protein
                 3. Structural bioinformatics
                                                                                                                                                                                                                                           Research Groups
                                                                      NMR spectroscopy is one of the popular ex-                        participated in the TREC 2005 Genomics Track     network. The interaction network will provide po-
                                                                                                                                                                         th
                 Protein structure prediction                     periments to determine protein structure.  An im-                     Ad-hoc Retrieval Contest and won the 6  place out   tential candidates for developing new therapeutic
                                                                  portant stage of protein structure determination by                   of 32 teams. The Genomic information retrieval   strategies for human cancer and infectious diseases.
                     We have developed a hybrid knowledge-based
                                                                  using NMR is protein backbone resonance assign-                       contest combines natural language queries and table   Objectives of this study are to improve our un-
                 protein secondary structure prediction algorithm,
                                                                  ment. This is a tedious and time-consuming manual                     search. Due to the variations of biological terms and   derstanding of the puzzle during the development
                 called HYPROSP II, which combines an existing
                                                                  work.  We have developed an iterative relaxation                      the large amount of unknown medical words, the   stage, carcinogenesis and infectious mechanism,
                 machine learning approach, PSIPRED, and a new

                                                                  technique for automatic backbone assignment that                      retrieval task is particularly difficult. The lab has ac-  and furthermore to introduce a new paradigm for the
                 peptide knowledge based approach for prediction.
                                                                  can tolerate a huge amount of noise in the data. Our                  cumulated many years of experiences in developing   diagnosis and treatment of human disease to revolu-
                 The average prediction accuracy of HYPROSP is
                                                                  paper was accepted in RECOMB 2005 and was in-                         information extraction, retrieval, natural language   tionize current medical services delivered.
                 around 82%, which is better than both of PSIPRED
                                                                  vited to be published in Journal of Computational                     processing and question answering systems, and
                 and the knowledge based approach. As more protein
                                                                  Biology. It is the very first paper from Taiwan ac-                   obtained an accuracy of 24.53% (The best team
                 structures are determined, the knowledge base is
                                                                  cepted by RECOMB since its inception nine years                       has 28%). The performance is very close to the top
                 expected to grow and the prediction accuracy is also
                                                                  ago. A related result based on genetic algorithm                      five teams: York Univeristy, IBM、University of
                 expected to increase. Related papers appeared in
                                                                  has appeared in Nucleic Acids Research 2005. To                       Waterloo、UIUC及National Library of Medicine
                 Nucleic Acids Research 2004 and Bioinformatics
                                                                  extract geometric constraints for the structure calcu-                (NLM). In the fi rst year’s work, IASL has only
                 2005. We have also adopted more biological domain
                                                                  lations from the NMR spectra, we need to consider                     employed keyword expansion. In the future they
                 knowledge and machine learning techniques to pre-
                                                                  NOEs and coupling constants that are transformed                      will adopt more biological knowledge to enhance
                 dict related structure problems, such as local struc-
                                                                  into distance and dihedral angle constraints. We                      system performance.
                 ture, b-turn, transmembrane helix prediction, etc.
                                                                  shall develop an efficient algorithm for NOE data
                 Once protein secondary structures can be predicted                                                                     5. Systems biology
                                                                  analysis and use this data analysis result to improve
                 with improved accuracy, we then target to predict
                                                                  backbone assignment. This research is in collabora-                   Network analysis of human protein interactions
                 tertiary structures with emphasis on the protein fold
                                                                  tion with IBMS.                                                       for Tumorigenesis and infectious diseases using
                 recognition problem.
                                                                                                                                        systems biology
                                                                  4. Biomedical literature mining
                 Protein 3D structure prediction by fragment
                                                                                                                                            Advances in molecular biology, analytical
                 assembly                                         Biological term and relation extraction
                                                                                                                                        and computational technologies are enabling us to
                     We propose to predict the protein backbone       The Intelligent Agent System Lab has devel-                       investigate systematically on complicated molecu-
                 conformation based solely on the sequence informa-  oped a system for biological named entities recogni-               lar processes through protein interaction networks
                 tion. The objective will be achieved using our previ-  tion from biomedical literature. We use Maximum                 underlying biological phenotypes. In this study, we





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