Shann-Ching Chen

Shann-Ching Chen, Ph.D.

Laboratory for Computational Cell Biology, The Scripps Research Institute Email: shanncc AT scripps DOT edu

*EDUCATION & TRAINING*

	The Scripps Research Institute, La Jolla, CA Oct. 2007 - present Postdoctoral Research Associate in Department of Cell Biology

	Carnegie Mellon University, Pittsburgh, PA Jan. 2004 - Sept. 2007 Ph.D. in Biomedical Engineering, with a dissertation entitled “Graphical Model Approaches to Segmentation and Classification for Analysis of Protein Subcellular Location Patterns”

	Carnegie Mellon University, Pittsburgh, PA Aug. 2001 - Dec. 2002 M.S. in Electrical and Computer Engineering

	National Taiwan University, Taipei, Taiwan Sept. 1995 - Jun. 1999 B.S. in Computer Science and Information Engineering


*RESEARCH INTERESTS*

	Bioimage informatics and computational biology.
	Computational modeling on cell migration in live cell images.
	Automated image analysis for protein subcellular location patterns in static multi-cell images.


*RESEARCH EXPERIENCE*

	Postdoctoral Research Associate, The Scripps Research Institute Oct. 2007 - present (Advisor: Gaudenz Danuser) - Developed image analysis software to map complicated cell migration patterns into a spatiotemporal space, called morphodynamic profiles, where the coordination between protrusion and retraction events can be quantitatively characterized. - Proposed a machine learning cascade for quantification of morphodynamic profiles, thus defining one of the first attempts to employ machine learning for the analysis of live cell image data. It sensitively captured experimental heterogeneity within the same genotype, identified similar movement patterns among different genotypes, and allowed grouping of genes into signaling pathways. - Studied Drosophila border cell migration, a model system for the collective migration of multiple cells. Collective cell migration occurs in most developmental and metastasis processes. - Facilitated visualization of cancer cell migration in 3D space.

	Graduate Research Assistant, Carnegie Mellon University Jan. 2004 - Sept. 2007 (Advisor: Robert F. Murphy) - Proposed a novel approximate inference algorithm with expressive power and computational efficiency. - Designed machine learning methodologies to classify protein subcellular location patterns in multi-cell images. These automated methods provided an objective, quantitative and repeatable assignment of protein locations, and can help in designing high-throughput screening systems for drug discovery. - Performed automated classification and re-annotated localization categories of yeast proteins in the yeast GFP fusion localization database. - Programmed major utilities on the Protein Subcellular Localization Image Database, one of the most important repositories for microscopy images documenting the subcellular location of proteins.

	Pre-Doctoral Fellow, School of Medicine, University of Pittsburgh Jan. 2003 - Dec. 2003 (Advisor: Ivet Bahar) - Utilized data mining algorithms to discover frequent patterns in protein structural families. - Incorporated proteins’ dynamic, biochemical, and geometrical features for pattern analysis.

	Graduate Research Assistant, Carnegie Mellon University Aug. 2001 - Dec. 2002 (Advisor: Tsuhan Chen) - Devised efficient computational methods to retrieve similar global structures and local active sites in the protein structure database.

*PUBLICATIONS*

	Journal Papers - Chen S.-C., Gordon G.J., and Murphy R.F. Graphical Models for Structured Classification, with an Application to Interpreting Images of Protein Subcellular Location Patterns. Journal of Machine Learning Research, 9, 651-682 2008 - Chen S.-C., Zhao T, Gordon G.J., and Murphy R.F. Automated Image Analysis of Protein Localization in Budding Yeast. Bioinformatics 23:i66-i71 2007 - Chen S.-C., and Murphy R.F. A graphical model approach to automated classification of protein subcellular location patterns in multi-cell images. BMC Bioinformatics 7:90 2006 - Yang L.-W., Rader A.J., Liu X., Jursa C.J., Chen S.-C., Karimi H., and Bahar I. oGNM: A protein dynamics online calculation engine using the Gaussian Network Model. Nucleic Acids Research, 34, W24-31 2006 - Chen S.-C., and Bahar I. Mining frequent patterns in proteins: A study of serine proteinases. Bioinformatics 20, i77-i85 2004

	Submitted Manuscripts - Chen S.-C., Prasad M., Montell D., and Danuser G. Machine Learning of Morphodynamic Profiles for the Mapping of Chemotactic Pathways of Border Cell Migration. - Chen S.-C., and Danuser G.. Quantitative Analysis of Border Cell Migration.

	Peer-Reviewed Conference Proceedings - Chen S.-C., Zhao T., Gordon G.J., and Murphy R.F. A Novel Graphical Model Approach to Segmenting Cell Images. Proceedings of the 2006 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, pp.1079-1086 2006 - Chen S.-C., Gordon G.J., and Murphy R.F. A Novel Approximation Inference Approach to Automated Classification of Protein Subcellular Location Pattern in Multi-cell Image. Proceedings of the 2006 IEEE International Symposium on Biomedical Imaging, pp.558-561 2006 - Chen S.-C., and Chen T. Retrieval of 3D Protein Structure. Proceedings of the 2002 International Conference on Image Processing, pp.933-936 2002 - Chen S.-C., and Chen T. Protein Retrieval by Matching 3D Surfaces. Proceedings of the 2002 Workshop on Genomic Signal Processing and Statistics, pp. CP2-09: 1-4 2002

	Book Chapter - Zhao T., Chen S.-C., and Murphy R.F. Location Proteomics. In: Introduction to Systems Biology. (S. Choi, ed.) 2006


*HONORS & PATENT*

	Outstanding Engineering Student Prize, Chinese Institute of Engineers 1999
	Presidential Award of National Taiwan University 1995, 1996
	Tip-Free Pad for Paper Cups, Patent No. 146940, Taiwan 1999 - 2009


*TECHNICAL SKILLS*

	Programming Skills - Matlab, Java, SQL, R, C, C++, MFC, Embedded VC, UNIX, LINUX, 80X86 Assembly Image Analysis - Pattern Recognition of Protein Subcellular Localization in Microscopy Images - Computational Modeling of Cell Migration in Live Cell Images - Image Segmentation Probabilistic Graphical Model Approaches - Hidden Markov Model - Approximate Inference Algorithms - Belief Propagations Data Mining - Frequent Pattern Discovery Protein Structure Dynamics - Gaussian Network Model

*OTHER PROFESSIONAL ACTIVITIES*

	Selected Presentations and Invited Talks - Quantitative Analysis of Border Cell Migration. In: Asilomar Conference on Signal, Systems, and Computers, Pacific Grove, CA, 2008 - Graphical Model Approaches to Segmentation and Classification for Analysis of Protein Subcellular Location Patterns. In: Department of Computer Science and Information Engineering, National Taiwan University, Taiwan, 2007 - Graphical Model Approaches to Segmentation and Classification for Analysis of Protein Subcellular Location Patterns. In: Institute of Information Science, Academia Sinica, Taiwan, 2007 - Graphical Model Approaches to Automated Classification of Protein Subcellular Location Patterns in Multicell Images. In: Howard Hughes Medical Institute’s 2007 Predoctoral Fellows Meeting - Automated Image Analysis of Protein Localization in Budding Yeast. In: National Technology Centers for Networks and Pathways 2007 All-Hands Meeting, National Institutes of Health, Bethesda, MD - A Novel Graphical Model Approach to Segmenting Cell Images. In: 2006 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology - A Novel Approximation Inference Approach to Automated Classification of Protein Subcellular Location Pattern in Multi-cell Image. In: 2006 IEEE International Symposium on Biomedical Imaging. - A Novel Graphical Model Method to Automated Classification of Protein Subcellular Location Patterns. In: Biomedical Engineering Society (BMES) Annual Meeting 2005 - Mining frequent patterns in protein structures: A study of protease families. In: Institute of Information Science, Academia Sinica, Taiwan, 2005

	Manuscript Reviewer - Nature Methods - Journal of Proteome Research - Cell Motility and the Cytoskeleton

*TEACHING EXPERIENCE*

	Undergraduate Research Mentor - Ashley Gonzalez, Image Acquisition for Heterogeneous Protein Location Pattern Analysis (2006) - Lori Dansie, Computer Analysis of Butons and Spines in the Neural Network (2005) - Amber Reed, Analysis of Subcellular Location Patterns on effects of Ras Oncogene on in NIH 3T3 Cells (2005) - Yenixsa Rivera-Sierra, Skeletonization by Morphological Thinning for Protein Subcellular Location Patterns (2004)

	Teaching Assistant - Computational Biology, Carnegie Mellon University (2005, 2006)

	Grader - Physiology, Carnegie Mellon University (2005)

*EXTRACURRICULAR ACTIVITIES*

	Second Runner-Up, Men’s Single, National Taiwan University Table-Tennis Tournament, April 1999

	President of Student Council, Dept. of Comp. Sci. and Info. Eng., National Taiwan Univ., June 1997 - May 1998

	Vice-President of Creative Intelligent Club, National Taiwan University, Sept. 1996 - June 1997

Postdoctoral Research Associate in Department of Cell Biology

Ph.D. in Biomedical Engineering, with a dissertation entitled “Graphical Model Approaches to Segmentation and Classification for Analysis of Protein Subcellular Location Patterns”

- Developed image analysis software to map complicated cell migration patterns into a spatiotemporal space, called morphodynamic profiles, where the coordination between protrusion and retraction events can be quantitatively characterized.

- Studied Drosophila border cell migration, a model system for the collective migration of multiple cells. Collective cell migration occurs in most developmental and metastasis processes.

- Facilitated visualization of cancer cell migration in 3D space.

- Proposed a novel approximate inference algorithm with expressive power and computational efficiency.

- Performed automated classification and re-annotated localization categories of yeast proteins in the yeast GFP fusion localization database.

- Programmed major utilities on the Protein Subcellular Localization Image Database, one of the most important repositories for microscopy images documenting the subcellular location of proteins.

- Utilized data mining algorithms to discover frequent patterns in protein structural families.

- Incorporated proteins’ dynamic, biochemical, and geometrical features for pattern analysis.

- Devised efficient computational methods to retrieve similar global structures and local active sites in the protein structure database.

- Matlab, Java, SQL, R, C, C++, MFC, Embedded VC, UNIX, LINUX, 80X86 Assembly

- Pattern Recognition of Protein Subcellular Localization in Microscopy Images

- Computational Modeling of Cell Migration in Live Cell Images

- Image Segmentation

- Hidden Markov Model

- Approximate Inference Algorithms

- Belief Propagations

- Frequent Pattern Discovery

- Gaussian Network Model

- Quantitative Analysis of Border Cell Migration. In: Asilomar Conference on Signal, Systems, and Computers, Pacific Grove, CA, 2008

- Graphical Model Approaches to Segmentation and Classification for Analysis of Protein Subcellular Location Patterns. In: Department of Computer Science and Information Engineering, National Taiwan University, Taiwan, 2007

- Graphical Model Approaches to Segmentation and Classification for Analysis of Protein Subcellular Location Patterns. In: Institute of Information Science, Academia Sinica, Taiwan, 2007

- Graphical Model Approaches to Automated Classification of Protein Subcellular Location Patterns in Multicell Images. In: Howard Hughes Medical Institute’s 2007 Predoctoral Fellows Meeting

- Automated Image Analysis of Protein Localization in Budding Yeast. In: National Technology Centers for Networks and Pathways 2007 All-Hands Meeting, National Institutes of Health, Bethesda, MD

- A Novel Graphical Model Approach to Segmenting Cell Images. In: 2006 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology

- A Novel Approximation Inference Approach to Automated Classification of Protein Subcellular Location Pattern in Multi-cell Image. In: 2006 IEEE International Symposium on Biomedical Imaging.

- A Novel Graphical Model Method to Automated Classification of Protein Subcellular Location Patterns. In: Biomedical Engineering Society (BMES) Annual Meeting 2005

- Mining frequent patterns in protein structures: A study of protease families. In: Institute of Information Science, Academia Sinica, Taiwan, 2005

- Nature Methods

- Journal of Proteome Research

- Cell Motility and the Cytoskeleton

- Ashley Gonzalez, Image Acquisition for Heterogeneous Protein Location Pattern Analysis (2006)

- Lori Dansie, Computer Analysis of Butons and Spines in the Neural Network (2005)

- Amber Reed, Analysis of Subcellular Location Patterns on effects of Ras Oncogene on in NIH 3T3 Cells (2005)

- Yenixsa Rivera-Sierra, Skeletonization by Morphological Thinning for Protein Subcellular Location Patterns (2004)

- Computational Biology, Carnegie Mellon University (2005, 2006)

- Physiology, Carnegie Mellon University (2005)