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Great Lakes Bioinformatics Conference 2013

Jobs Board

Updated May 13, 2013


Computational Biologist (Job ID: 00GZS)
Monsanto Company
St. Louis, Missouri

Monsanto seeks a creative, mathematically-minded Computational Biologist to help us transform big data into higher yielding crops. In this role you role will support research across Monsanto Technology by integrating, modeling, and interpreting unprecedented genomic, transcriptomic, metabolic, phenotypic, population genetic, and geospatial data, all with the goal of driving interpretation into practical use. We aim to help feed the mid-21st century world using less water, less fertilizer, and less pesticide.

In this role you will design biological and computational experiments, generate biologically meaningful and testable hypotheses, and deliver robust analyses to projects across Monsanto Technology. You will also develop, optimize, and share computational practice with the larger Monsanto scientific community. Because we are a problem-driven team with high impact, we prize creativity, unceasing skills acquisition, and world-class collaboration skills.

  • A computationally-oriented Ph.D. in Genetics, Microbiology, Chemical Engineering, or Biophysics, with a strong publication record.
  • Successful development of novel, statistically-motivated algorithms to extract meaning from diverse biological data.
  • Successful experience working closely with experimental collaborators to test computationally derived hypotheses.
  • Ability to work on cross-functional teams to accomplish milestones.
  • Excellent verbal and written communication skills.

Required Skills/Experience:

  • Past laboratory experience, along with an appreciation for when to use a computational approach and when to do a physical experiment. A background in metabolic engineering is a plus.
  • Facility with the programmatic use of at least one broadly accepted statistical package (e.g. R, SAS, S+, Minitab).
  • Scientific programming skills in at least one high performance language (e.g. C, C++, Java, Scala) and one scripting language (e.g. Perl, Python).
  • Expert operation in high performance computing environments, including parallel/concurrent programming ability.
  • Experience guiding experimentalists on the best use of computational approaches for their projects and guiding informaticists on how to best meet the requirements of experimentalists.
  • Demonstrated ability to learn methods and technologies as necessary to meet objectives.
  • A highly collaborative approach, with the drive and ability to actively create new connections across the Monsanto Technology organization.
  • 2 years industry or postdoc experience in computational biology.

Job ID: 00GZS
http://jobs.monsanto.com/st-louis/research-and-development/jobid3395316-computational-biologist-jobs


Computational Biologist (Job ID: 00C86)
Monsanto Company
St. Louis, Missouri

Monsanto is seeking an experienced, highly-motivated scientist with a comprehensive background in Computational Biology and Comparative Genomics. In this newly-created position on a small team in Chemistry Technology, you will have the opportunity to apply your knowledge in DNA and RNA sequence analysis, bioinformatics, and pathogen genomics to the discovery of novel biological and chemical methods of crop protection.

As an individual contributor working on multiple project teams, you must have the ability to prioritize multiple tasks while thinking critically about rapidly-evolving scientific problems. Although this is not currently a direct supervisory position, you will play a key role in driving projects forward, and you must have a demonstrated ability to motivate others to work towards common goals. Project teams include scientists from a variety of scientific backgrounds, so the ability to communicate clearly across scientific boundaries will be important for success.

In addition to your role on project teams, as a member of Chemistry’s new Computational Biology platform you will have a voice in shaping the team’s research and direction. You will be working at the forefront of challenging cutting-edge technology, driving new experiments through evidence-based hypotheses, and developing new computational methods for sequence analysis and comparative genomics.

Responsibilities:

  • Work with insect control teams to discover, prioritize, and analyze new gene targets for crop protection.
  • Where existing sequence data is insufficient, work with Monsanto’s sequencing center to assemble and analyze new genomes, transcriptomes, and expression profiles.
  • In cooperation with experimental teams, use computational and statistical analysis to develop and communicate a detailed understanding of RNAi mechanisms in both plants and pathogens.
  • Work with bioinformatic and IT-related teams across Monsanto to systematize and centralize disparate data for use by the broader community.
  • On a regular basis, produce written and oral presentations of methods, results, conclusions, and recommendations to other members of the team.

Required Skills/Experience:

  • Master’s Degree in Computational Biology, Statistical Genetics, Bioinformatics, or related discipline with at least seven years of postgraduate experience
  • Experience applying statistical techniques to phylogenetics, multigene family analysis, and expression analysis
  • Demonstrated ability in using computational techniques to develop and test experimental hypotheses
  • Strong familiarity with current bioinformatic software on both Unix/Linux and Windows platforms
  • Strong computational skills in software scripting using Python (preferred), Perl, R, or equivalent
  • Experience with analyzing large, complex, heterogeneous data sets
  • Experience with next-generation sequencing technologies and next-generation sequence data analysis
  • Proven record of successful problem-solving and clear communication with individuals from varied technical backgrounds
  • Flexibility to adapt and incorporate new sources of data on an continuous basis

Desired Skills/Experience:

  • PhD in Computational Biology, Statistical Genetics, Bioinformatics, or related discipline with at least three years of postgraduate experience
  • Direct experience with RNA silencing (siRNA and miRNA) mechanisms, pathways, and experiments
  • Direct experience in plant or insect genomics
  • Experience with software development (Python, C/C++, Java, or equivalent) and relational databases
  • Experience with visualization of complex data

Job ID: 00C86
http://jobs.monsanto.com/st-louis/research-and-development/jobid2904477-computational-biologist-jobs


Mathematical Modeler
Immunetrics, a bio-simulation company located in Pittsburgh, Pennsylvania, and on the cutting edge of in silico modeling, seeks a Mathematical Modeler.

The Company's modeling technology provides critical information to accelerate and improve drug discovery and development. Working with pharmaceutical clients and global thought leaders, Immunetrics brings the power of computational modeling to biomarker development, preclinical experiment design, lead selection, and clinical trial design. This reduces risk while speeding the path to approval.

Biosimulation technology from Immunetrics Inc. translates data between model systems and between development stages. Put simply, we build predictive computer models based on biology. The inputs are customer data and the outputs are real endpoints, whether the goal is power calculation, dosage selection, exclusion criteria, PK/PD modeling, biomarker identification, or any other factor in clinical study design.

The Company is seeking a full-time Mathematical Modeler as described below.

Mathematical Modeler

Job Description:

  • Function as part of a team of mathematical modelers to design and develop mathematical models of complex biological systems
  • Research biological literature and understand biological pathways and physiological processes
  • Analyze clinical data and work with statisticians to perform data analysis
  • Develop and deploy models using Immunetrics proprietary modeling platform. Responsibilities span every stage of modeling cycle: design, implementation, debugging, testing, analysis, and maintenance
  • Perform large-scale parameter estimation for models using experimental/clinical data
  • Communicate research and findings to technical and non-technical audiences inside and outside the company

Qualifications:

  • MS/PhD in Bioengineering, Chemical, Control Engineering, Mathematics, Physics or related field
  • Strong background in computation, particularly differential equations and parameter estimation
  • Prior modeling experience or background in one or more of the following domains: inflammation, immunology, cell signaling, or physiology
  • Prior industrial experience or history of producing concrete project deliverables is strongly preferred
  • Experience with the development of mathematical models of biological systems. Experience with large-scale models is strongly preferred
  • Familiarity with searching biological literature using available tools
  • Some knowledge of statistics is preferred, but not essential
  • Strong communication skills


Postdoctoral Scientist, Bioinformatics Analyst
Agilent Technologies
Santa Clara, CA
Apply at www.agilent.com, requisition #2049853

Job Description:

Working as a part of research consortium with leading academic institutions, you will help design experiments, manage and analyze large microarray, mass spectrometry, and NGS datasets collected as part of a collaborative toxicology initiative.

Key responsibilities include:

  • Conduct statistical analysis of the transcriptomics, metabolomics, NGS and pathway data.
  • Work with Agilent scientists and consortium partners to coordinate interpretation of the analysis results.
  • Develop bioinformatics pipelines for analyzing transcriptomics, NGS and metabolomics data, both independently and in conjunction with each other. Assist with implementation in production environment.
  • Implement the pipelines in a production environment.
  • Apply analytical techniques to identify pathways which are involved in organism’s response to known toxicants
  • Collaborate with computational and system biologists to develop novel network analysis algorithms for toxicology
  • Present analysis results to members of the consortium via in-person and written communications.

Qualifications:

  • PhD in computer science, statistics, bioinformatics, computer science or equivalent discipline
  • Experience with application of common statistical methods to either microarray, mass spectrometry, or NGS data, including knowledge of common bioinformatics databases and tools
  • In-depth knowledge of biological pathway analyses and content source
  • Extensive experience with analysis and algorithm development on large data sets; 3+ years hands-on bioinformatics experience
  • Knowledge of R/Bioconductor, Python, or Java required
  • Excellent communication skills
  • Strong analytical skills
  • Toxicology background preferred but not required


Postdoctoral Scientist, Bioinformatics Analyst
Agilent Technologies
Santa Clara, CA
Apply at www.agilent.com, requisition #2049854

Job Description:

Working as a part of research consortium with leading academic institutions, you will help create, manage and maintain a cloud-based data repository and bioinformatics tools to collect and analyze microarray, mass spectrometry and NGS datasets collected as part of a collaborative toxicology initiative.

Key responsibilities include:

  • Collaborate with biologists to understand and document analytical and data management requirements; contribute to development of bioinformatics best practices.
  • Develop bioinformatics pipelines for analyzing transcriptomics, NGS and metabolomics data. Implement the pipelines in a production environment.
  • Architect a cloud-based infrastructure enabling data monitoring, management, QA, analysis and reporting.
  • Develop a web interface to the repository, train end users.
  • Manage and maintain consortium data repository, making changes and adding features as necessary.
  • Conduct QA and validation of deposited data and metadata.
  • Present analysis results to members of the consortium via verbal and written communications.

Qualifications:

  • PhD in computer science, statistics, bioinformatics, computer science or equivalent discipline
  • Demonstrated experience as scientific programmer
  • Familiarity with cloud-based technologies and services preferred
  • 3+ years expertise coding in Python, Java and JavaScript required
  • Experience working with relational database systems, web-based development
  • Experience in best-practices for coding, revision control, bug tracking, and release management
  • Excellent communication skills
  • Strong analytical skills
  • Familiarity with toxicology or pharmacology is a plus


Postdoctoral Positions Available
Joint project between researchers at Carnegie Mellon University and University of Maryland, College Park

Several NIH-funded computational biology postdoctoral positions are available on a collaborative project between Dr. Kingsford (http://cs.cmu.edu/~ckingsf) at Carnegie Mellon University, and Dr. Hannenhalli (http://cbcb.umd.edu/~sridhar) and Dr. Girvan (http://networks.umd.edu) at the University of Maryland, College Park. The research project broadly aims to understand the connection between spatial chromatin structure and transcriptional regulation through the analyses of large-scale genome/epigenomic data. The specific project ideas will be developed collaboratively with the postdoc. Depending on the interests and qualifications of the applicant, positions are available in both Pittsburgh, PA and College Park, MD.

Requirements for the position include: a proven record of self-motivated research and a PhD in computer science, computational biology, statistics, applied physics, or quantitative biology. The candidate should have programming experience, experience with analyzing biological data pertaining to gene transcription, and a good understanding of transcriptional regulation and molecular evolution.

The position provides a competitive annual stipend plus benefits and health insurance. The position is available immediately. The initial appointment will be for 1 year and will be extended depending on performance and availability of funds. Highly motivated applicants are encouraged to email a statement of research interests, CV, and contact details for three references to This email address is being protected from spambots. You need JavaScript enabled to view it..

The Lane Center for Computational Biology at Carnegie Mellon University seeks to realize the potential of machine learning for expanding our understanding of complex biological systems. A primary goal of the center is to develop computational tools that will enable automated creation of detailed, predictive models of biological processes, including automated experiment design and data acquisition. The Center for Bioinformatics and Computational Biology (CBCB) at University of Maryland, College Park, is worldwide a leader in developing software and methods for computational genomics, metagenomics, genome assembly, and other analyses of large-scale biological data.


Predictive Analytics Scientist Biotechnology (Job ID: 00IOI)
Monsanto Company
St. Louis, Missouri

Monsanto is seeking a highly motivated individual to join our Biotechnology Trait Field Solutions (TFS) Global Analytics team. The Predictive Analytics Scientist will join a team of statisticians and collaborate with a diverse group of researchers to develop and implement predictive data mining models to improve data-driven decision making processes across multiple business platforms within Biotech. This involves leading initiatives to identify and develop complex predictive models and providing directions and strategies to stakeholders for integration of the models into critical business decisions in product development. The successful candidate must possess good communications skills, work well in a team setting, must be proficient in working on complex projects with evolving objectives, and is expected to be a proactive, independent problem solver and innovator.

Required Skills/Experience:

  • Master’s/PhD degree in Computer Science, Statistics, Operations Research, Mathematics, an engineering discipline, or a related field
  • Experience creating predictive models and strategies with machine learning and data mining techniques
  • Thorough knowledge in computational modeling, simulation modeling and data analysis
  • 3+ years of experience in statistical programming languages (R, MATLAB, or SAS)

Desired Skills/Experience:

  • Advanced knowledge in statistical modeling and optimization techniques
  • Experience working with agricultural/biological scientific data
  • Experience in PMML or other scientific programming skills (e.g., Perl)
  • Experience working with “unstructured data” (e.g., image)

Job ID:  00IOI
http://jobs.monsanto.com/st-louis/research-and-development/jobid3634450-predictive-analytics-scientist-biotechnology-jobs


Research Scientist Computational Protein Design (Job ID: 00HAV)
Monsanto Company
St. Louis, Missouri

Responsibilities:
We are seeking a highly talented and motivated scientist to join our Protein Science group in the area of computational protein design. The job description includes the design of novel proteins for both rational and combinatorial design approaches and the computational analysis of biophysical/structural properties of proteins to 1) aid in the design of protein variant libraries, and 2) to build structural models of target proteins. These efforts will increase our understanding of protein design/structure/function relationships with the goal of increasing the efficiency of our overall protein design program. The successful candidate will be expected to collaborate across projects and geographical locations.

Requirements:
A Ph.D. or equivalent in biochemistry, computational biology, molecular biology or a related discipline with experience in protein design is required. The candidate should have comprehensive working knowledge in experimental and computational aspects of protein structure/function and design. The candidate should be fluent in computer programming, ideally in C++ or Python, and be familiar with tools/algorithms used for computational design/analysis of novel proteins. Postdoctoral experience is preferred. Excellent communication and interpersonal skills and the ability to work on multiple project teams is a must.

Job ID: 00HAV
http://jobs.monsanto.com/st-louis/research-and-development/jobid3528059-research-scientist-computational-protein-design-jobs


Scientist (Job ID: 00HLD)
Monsanto Company
Bangalore, Karnataka, India

Job Description for Computational Biology Scientist

Monsanto seeks a creative Computational Biologist to help transform unprecedented *omics data into higher yielding crops. You will participate in the design of biological and computational experiments across several projects, generate biologically meaningful and testable hypotheses, and deliver robust analyses to other team members and internal customers. You will also develop, optimize, and share bioinformatics tools with the larger Monsanto scientific community.

Ideal candidate will have PhD degree in a quantitative biology discipline (e.g. computational biology, bioinformatics, biophysics, biostatistics) or a degree in biology or in a quantitative/computational discipline with significant experience in the complementary domain. Demonstrated success in working with multi-disciplinary research team requiring scientific creativity, innovative thinking, organization and collaborative skills are necessary for this role.

Role and responsibility:

  • Generate hypotheses using computational methods to integrate and interpret data from various sources such as transcriptomic, genomic, metabolomic, field and molecular assays
  • Generate intellectual property from large amount of genomic and field data using sound statistical methods
  • Develop systems biology approaches to identify trait-gene or trait-marker associations
  • Work with other research teams to continually improve the bioinformatics capabilities and workflows associated with next generation sequencing to support population and comparative genomics projects.

Qualifications:

  • Ph.D. in a Computational, Statistical, Biophysics, or Bioinformatics related field.
  • Strong publication record in the field of Genetics, Bioinformatics, or Statistics.
  • Demonstrated successful development of novel, statistically-motivated algorithms to extract meaning from diverse biological data.
  • Successful experience working closely with experimental collaborators to test computationally derived hypotheses.
  • Ability to work on cross-functional teams to meet milestones and deadlines.
  • Excellent verbal and written communication skills.
  • Facility with the programmatic use of at least one broadly accepted statistical package (e.g. R, SAS, S+, Minitab).
  • Scientific programming skills in at least one high performance (e.g. C, C++, Java, Scala) and one scripting language (e.g. Perl, Python).
  • Ability to write SQL queries and design simple relational database schemas.
  • Demonstrated ability to learn methods and technologies as necessary to meet objectives.

Desired Skills/Experience:

  • 2 years industry or postdoc experience in computational biology.
  • Past laboratory experience a plus, along with an appreciation for when to use a computational approach and when to do an experiment.
  • Ability to use high performance computing environments. Parallel/concurrent programming ability is a plus.
  • Experience guiding experimentalists on the best use of computational approaches for their projects and guiding informaticists on how to best meet the requirements of experimentalists.
  • A highly collaborative approach, with the drive and ability to actively create new connections across the Monsanto Technology organization.

Job ID: 00HLD
http://jobs.monsanto.com/in/india/research-and-development/jobid3583215-scientist-jobs


Scientific Programmer
Immunetrics, Inc. is a U.S.-based bio-simulation company that creates mechanistic mathematical models of biological systems to accelerate the development of drugs, clinical diagnostics and medical devices.
Immunetrics is seeking a Scientific Programmer as described below.

Scientific Programmer

Responsibilities include:

  • Designing, developing, and maintaining software for high-performance solution of mathematical problems related to simulation, analysis, and application of biological models
  • Developing software to support analysis and visualization of data sets of both real and simulated patient data
  • Working with scientific user-base and architects to solicit new features and improve users' workflow
  • Rapid prototyping of next-generation experimental features
  • Analyzing algorithm performance and optimizing code for computational efficiency
  • Responsible for all aspects of software process: design, prototyping, debugging, testing, documentation, etc.
  • Helping end-users troubleshoot problems

Qualifications:

  • Minimum of BS in Computer Science with 2-3 years of industry experience required. Strong mathematics background beyond standard CS curriculum is required (math minor, double major, or comparable coursework/experience).
  • Experience in numerical computing, scientific simulation, machine learning, etc. is required.
  • Strong background in algorithms, data structures, and software engineering principles is essential.
  • Commitment to writing elegant, reliable, robust software is essential.
  • Solid object-oriented design skills, testing and debugging skills are required.
  • Strong working knowledge of C++ required; Java desirable.
  • Experience developing with a scripting language such as Perl, Python, etc. is strongly desired.
  • Eagerness to work in a team-oriented, small company environment


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Great Lakes Bioinformatics Conference 2013

Education Session - Wednesday, May 15, 2013

3:45pm – 5:30pm
Location:  Connan

Educating Biologists and Bioinformatics Professionals for the Future - Are We Getting It Right?


Panel Co-Chairs: Guenter Tusch, Grand Valley State U, and Sarah Elgin,Washington U in St. Louis

Panelists:

  • Lonnie Welch, Ohio University, Vice Chair ISCB Education Committee
  • Sarah Elgin, Washington University in St. Louis, and Genomics Education Partnershipnformatics Core, CCMB
  • Panayiotis (Takis) Benos, University of Pittsburgh, Joint Carnegie Mellon – U Pittsburgh Computational Biology Graduate Program
  • Regina Lamendella, Juniata College
  • Shawn Stricklin, Monsanto


Panel description:  The panel will explore the state of the art of bioinformatics curriculum efforts in the Great Lakes Region and on a national and international level by looking at different content areas from an undergraduate and graduate perspective, as well as current skill requirements in research and industry.

While bioinformatics education has not received much attention at the GLBIO and OCCBIO conferences during the last few years, the RECOMB Satellite Conference on Bioinformatics Education has intensified the discussion about the importance of bioinformatics education for undergraduate biology students. Russ Altman asked in his 1998 editorial an obvious question: ‘What is the proper curriculum for bioinformatics professionals?’ He warns that we must be careful not to define ‘curriculum’ narrowly as a list of required courses, but also consider career counseling, exposure to the culture of the discipline, and access to quality research projects to build enthusiasm and skills. The Curriculum Task Force of the ISCB Education Committee organized a ‘Birds of a Feather ‘(BoF) session at ISMB 2012 to discuss bioinformatics curriculum guidelines. In this open forum the participants considered curricular implications of the task force’s surveys of career opportunities, hiring practices of bioinformatics core facility directors, and existing curricula. We need to continue this dialogue.

The purpose of this panel is to extend the ongoing discussion started by the Curriculum Task Force of the ISCB Education Committee and to explore the state of the art of bioinformatics curriculum efforts by considering different approaches at the undergraduate and graduate level. In particular, we will discuss the   skills and understandings needed for a student to progress from an undergraduate to a graduate program, and to prepare for employment in research and/or industry. At the undergraduate level the need to develop specific bioinformatics courses, and to encourage biology majors to take course work in statistics and computer science, will be addressed. Other questions to consider are the use software in classroom and lab, the balance of methodological and applied elements in a graduate/undergraduate curriculum, how to incorporate soft skills (such as working in teams) and the culture of bioinformatics, and the need for continuing education.


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Great Lakes Bioinformatics Conference 2013

Tutorial Sessions - Tuesday May 14, 2013

Updated May 09, 2013

TUTORIAL 1
9:00 am - 11:00 am
Location: McConomy

Spatial Rule-based Modeling of Cellular Biochemistry with MCell/BioNetGen/CellBlender


James Faeder
University of Pittsburgh School of Medicine

Markus Dittrich
Pittsburgh Supercomputing Center

 Dr. Faeder is Associate Professor of Computational and Systems Biology at the University of Pittsburgh School of Medicine. His research focuses on the development of novel computational methods and tools for the modeling and simulation of cell regulatory networks, as well as their application to specific systems of basic and biomedical relevance including immune regulation, cancer, and chemo-sensing in bacteria. Dr. Faeder's group also leads the development of the BioNetGen software for rule-based modeling of biochemical systems and is currently working on the integration of rule-based and spatial modeling.

Dr. Dittrich is Director of the National Resource for Biomedical Supercomputing at the Pittsburgh Supercomputing Center. His group uses computer models and simulation to study the structure and function of synapses. He is also one of the main developers of the MCell simulation package.

Dr. Faeder and Dr. Dittrich are both members of the Cell Modeling component of the NIH P41 Center for Multiscale Modeling of Biological Systems (MMBioS) at the University of Pittsburgh, which supports development of the MCell simulation platform for simulation of spatially realistic 3-D cellular models
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TUTORIAL 2
12:00 pm - 2:00 pm
Location: McConomy

Exploring and Enabling Biomedical Data Analysis with Galaxy

Chair:    Anton Nekrutenko, Penn State University

Are you a biomedical researcher who needs to do complex analysis on high throughput NGS datasets?  Galaxy (http://galaxyproject.org/) is an open, web-based platform for data intensive biological research that enables non-bioinformaticians to create, run, tune, and share their own bioinformatic analyses. This workshop will show participants how to integrate data and perform simple and complex analysis within Galaxy. It will also demonstrate how Galaxy enables reproducibility in bioinformatics, and how to use visualization to refine and drive analysis, all within the Galaxy Framework. Finally, the workshop will highlight how researchers can install Galaxy on local computational resources, or using computational cloud providers such as Amazon Web Services.
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TUTORIAL 3
2:30 pm - 4:30 pm
Location: McConomy

Machine Learning in High Dimension for Genomic Data Analysis


Chair:   Seyoung Kim, Machine Learning in Biology

Abstract:  The recent advances in the high-throughput technology such as next-generation sequencing technology have allowed the researchers to collect a large amount of data for genomes and various other aspects of a cell system.  Such datasets hold the key to understanding the detailed mechanisms of the genetic control of a biological system and further deepening our knowledge of cell biology with a potential application to medicine. Genomic data often lie in a very high-dimensional space in which many entities interact with each other in a complex manner, as in expression measurements for tens of thousands of genes and genotypes of millions of genetic loci. This tutorial will introduce statistical machine learning techniques for learning statistical models of various kinds in very high-dimensional space with fast and highly scalable learning methods. In particular, the tutorial will discuss sparse learning methods for regression and graphical models, structured-sparse learning, multi-task learning, and optimization methods. Then, it will show how such techniques can be applied to various types of genomic datasets to extract complex epistatic and pleiotropic interactions among various entities in a biological system.

Bio:   Dr. Seyoung Kim is an assistant professor in the Lane Center for Computational Biology in the School of Computer Science at Carnegie Mellon University. Her research focuses on developing and applying statistical machine learning methods for understanding how the information encoded in genomes shapes the biological system in terms of gene regulations, phenotypes, diseases, and developments. Dr. Kim received her B.S. in computer engineering from Seoul National University, Korea, and Ph.D. in computer science from the University of California, Irvine. She was a postdoctoral fellow in Machine Learning Department at Carnegie Mellon University. She is a recipient of the NSF Career Award and the Alfred P. Sloan Fellowship.


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Great Lakes Bioinformatics Conference 2013

Program Committee

Updated December 15, 2012

• Raj Bhatnagar, University of Cincinnati
• Laura Brown, Michigan Technological University
• C. Titus Brown, Michigan State University
• Michael Brudno, University of Toronto
• Chakra Chennubhotla, University of Pittsburgh
• Alan Dombkowski, Wayne State University
• Zhong-Hui Duan, University of Akron
• Madhavi Ganapathiraju, University of Pittsburgh
• Elodie Ghedin, University of Pittsburgh
• Xiaoxu Han, Eastern Michigan University
• Paul Harrison, McGill University
• H. V. Jagadish, University of Michigan
• Anil Jegga, Cincinnati Children's Research Foundation
• Hyun Min Kang,University of Michigan
• Seyoung Kim, Carnegie Mellon University
• Brian King, Bucknell University
• Mehmet Koyuturk, Case Western Reserve University
• Steve Krawetz, Wayne State University
• H.K. Kwan, University of Windsor
• Jae Won Kyoung, University of Pennsylvania
• Jing Li, Case Western Reserve University
• Chun Liang, Miami University
• David Liebovitz, Northwestern University
• Nina Lin, University of Michigan
• Long (Jason) Lu, Cincinnati Children's Research Foundation
• Richard Maclin, University of Minnesota, Duluth
• Jarek Meller, University of Cincinnati
• Folker Meyer, Argonne National Laboratory
• Helen Piontkivska, Kent State University
• Sridhar Ramachandran, Indiana University Southeast
• Gustavo Rohde, Carnegie Mellon University
• Shin-Han Shiu, Michigan State University
• David Sept, University of Michigan
• Jinshan Tang, Michigan Technological University
• Guenter Tusch, Grand Valley State University
• Matt Weirauch, Cincinnati Children's Research Foundation
• Matt Wortman, University of Cincinnati

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GLBIO CONFERENCE PAST SPONSORS

  • Agilent Technologies
  • Appistry
  • BioOhio
  • Bioteam
  • Bowling Green State University
  • Cambridge Computer
  • Carnegie Mellon University
  • Case Comprehensive Cancer Center
  • Case Western - Case Center for Proteomics & Bioinformatics
  • Cincinnati Children’s Hospital Medical Center
  • CLC Bio
  • Compendia Bioscience
  • Diagnostic Hybrids
  • GeneCode
  • Genomatix Software
  • Great Lakes Bioinformatics Consortium
  • IBM
  • International Society for Computational Biology
  • Kent State University
  • Mathworks
  • Miami University - Dept. of Botany
  • Michigan Economic Development Corporation
  • Michigan Technology University
  • Ohio State University - Center for Applied Plant Sciences (CAPS)
  • Ohio Supercomputer Center
  • Ohio University (Russ College; Vice President Research; College of Arts and Science)
  • Ohio University Edison Biotechnology Institute
  • Procter & Gamble
  • Springer
  • The Ohio State University
  • University of Akron
  • University of Cincinnati Children's Hospital
  • University of Michigan - DCM&B and OVPR
  • University of Michigan - Biomedical Research Core Facilities
  • University of Michigan - Metabolomics Core
  • University of Pittsburgh
  • Wittenberg University
  • Wright State University



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Great Lakes Bioinformatics Conference 2013

Submissions


FOR QUESTIONS, PLEASE CONTACT

Meeting Coordinator:
Joyce Perhac
Ph: (412) 372-1899
Email:  This email address is being protected from spambots. You need JavaScript enabled to view it.