Blog

NSF REU Program in Medical Informatics (MedIX)

The NSF REU Program in Medical Informatics (MedIX) at DePaul University and University of Chicago enters its twelfth year. We are looking for bright undergraduate students to get involved in research in the area of Medical Informatics for the summer of 2016. Participants will receive a stipend of $5,000, travel support to/from the REU site, and subsistence allowance.

Important Dates:

  • March 4, 2016: Application submission deadline
  • March 30, 2016: Notification of decision
  • April 6, 2016: Confirmation of participation
  • June 13, 2016: MedIX Program Orientation
  • August 19, 2016: MedIX Program last day

Statistics on the previous REU MedIX Program (2005-2013):

  • 84% students had at least one research publication
  • 65 publications (9 journal papers, 46 conference papers, 10 extended abstracts)
  • 25% of students are in, or have finished, PhD programs; all received support
  • 18% of student participants have finished masters programs
  • 21% of student are in, or have finished, medical degrees; of those, six are active in research.

For the application form and additional information, please have your students visit the MedIX website at http://facweb.cdm.depaul.edu/research/vc/medix/index.htm or contact Dr. Daniela Raicu at draicu@cdm.depaul.edu.

Summer School for Computational Genomics, June 13-24, 2016.

The Icahn School of Medicine at Mount Sinai is currently accepting applications for the Summer School for Computational Genomics, June 13-24, 2016. The application deadline is March 1, 2016.

Description: Summer School for Computational Genomics – The Icahn School of Medicine at Mount Sinai (New York, NY). Our big data science summer program from June 13-24, 2016, is designed for graduate students and professionals who have a strong interest in the intersection of computer science, genetics, and medicine. There will be sessions on the human genome, genetic variation, and genome technologies, and there will be introductory sessions on Unix, Python, Galaxy Toolkit, and scripting. There will also be field trips to New York Genome Center, Columbia University, and Weill-Cornell Medical Center. This program is funded through NIH grant Big Data to Knowledge (BD2K) 1R25EB020393. Educational and housing costs are covered through this grant; travel stipends are also available. The selection process for participants is competitive, and those selected will be awarded the opportunity to be in the program. For more information and to apply: http://icahn.mssm.edu/creeds. Applicants must be U.S. citizens or permanent residents. This program strives to enhance the diversity of the biomedical big data workforce through recruitment of individuals from diverse racial, ethnic, cultural, and socioeconomic backgrounds.summerprogcompgenomics

Workshop: Big Data Causal Discovery

The Increasing Diversity in Interdisciplinary Big Data to Knowledge (IDI-BD2K) Program at the University of Puerto Rico is pleased to announce a workshop on Big Data causal discovery.

Wednesday Feb 17, 2016

A.   Introduction. Presentations from the Center for Causal Discovery of the University of Pittsburgh and from the UPR IDI-BD2K participant faculty. Dr. Gregory Cooper and Dr. Richard Scheines from the University of Pittsburgh and several faculty members from UPR (and other participating institutions) will briefly present their ongoing projects.  These presentations are aimed at introducing students and faculty to Big Data Projects in Causal Discovery as applied to biomedical problems and at establishing collaborations between the BD2K participants and U. Pittsburgh

Wed 17 Feb, 2016
8:30 – 11:00 am

NCN A-211
Natural Sciences
Rio Piedras Campus
University of Puerto Rico

B. STUDENT RECRUITMENT for Big Data Summer Research Experiences. Drs Joseph Ayoob and David Boone will be presenting information on opportunities for training of students in Big Data, particularly in summer programs for undergraduate students at the University of Pittsburgh.

Wed Feb 17, 2016
11:30 am – 12:30 pm

NCN-A-211
Natural Sciences
Rio Piedras Campus
University of Puerto Rico

C.  HANDS ON WORKSHOP

Causal Discovery from Biomedical Data
Dr. Richard Scheines
University of Pittsburgh

Limited to 30 participants.  Those interested must register by writing to: jegarcia@hpcf.upr.edu

Wed February 17,  2016
1:00-3:00 pm

Julio Garcia Diaz building, room 123
Rio Piedras Campus
University of Puerto Rico

Seminar: Bugs, Parasites and Cities: the complex ecology of Chagas disease in Southern Peru.

The Increasing Diversity in Interdisciplinary Big Data to Knowledge (IDI-BD2K) Program at the University of Puerto Rico is pleased to announce a seminar:

Bugs, Parasites and Cities: the complex ecology of Chagas disease in Southern Peru

Dr. Michael Levy
Assistant Professor
Biostatistics and Epidemiology
University of Pennsylvania

Tuesday, February 9, 2016

Noon

Julio Garcia Diaz building, room 123
Rio Piedras Campus
University of Puerto Rico

Seminar: Graphlet kernels for vertex classification

Graphlet kernels for vertex classification

Where:   College of Natural Sciences, Department of Computer Science
When:    Monday, November 23, 2015
Hour:      2:30 pm
Room:    C-356

Graph kernels for supervised learning and inference on graphs have been around for more than a decade. However, the problem of designing robust kernel functions that can effectively compare graph neighborhoods in a variety of practical scenarios (e.g. the presence of incomplete and/or noisy data, auxiliary information) remains much less explored. Here, I will present my methods for vertex classification in large, sparse, and labeled graphs. Then, I will present an application of these methods to predicting protein function as well as molecular mechanisms of disease.

José Lugo-Martinez is a PhD candidate in Computer Science at Indiana University. The focus of his doctoral research has been the development of graph-based classification algorithms in the supervised and semi‐supervised scenarios, as well as statistical inference from large, noisy, biased, and high‐dimensional data. In particular, he develops computational approaches towards understanding protein function and how disruption of protein function leads to disease. Mr. Lugo-Martinez received dual B.S. degrees in Computer Science and Mathematics at the University of Puerto Rico-Rio Piedras, and M.S. degree in Computer Science at the University of California-San Diego. His research interest include machine learning, data mining and structural bioinformatics.

Department of Computer Science

graphlet

Get ready for IDI-BD2K

We were awarded the IDI-BD2K grant, and are getting ready to select students for next year. If you want to participate, try to select the courses you need to get up to speed during pre-registration.

Here’s a copy of the table showing the courses we want you to have. If you have questions, speak to one of the participating researchers.

courses

Roche-Lima, Abiel – Machine Learning to Predict Biological Networks.

Dr. Roche-Lima has been working on machine learning methods, based on kernels, to predict biological networks. He proposed a new framework, called Pairwise Rational Kernel (PRK), to manipulate sequence data represented as finite-state transducers (FSTs). By combining PRKs with supervised learning methods, biological network interactions have been predicted. As kernel methods are used, disparate type of data can be combined to find general relations. Using finite-state transducers, large amount of sequence data can be efficiently represented, processed and analyzed, improving the performance of the algorithms. Dr. Roche-Lima has been working and collaborating with bioinformatics studies at University of Manitoba, Canada, to predict biological interactions in several bacteria species. He is currently working at Medical Science Campus, University of Puerto Rico, where  large volume of sequence data, from several projects, are being generated. Students in his lab will learn how to represent, manipulate and analyze these data using the existing frameworks and machine learning methods. As well, students will develop new computational tools using these techniques.

Due to his experience working with predicting models and biological sequence data, Dr. Roche-Lima brings to the project the ability to develop computational tools for analyzing and processing big sequence data. It can be used to predict biological network interactions, but also it can be extended to any other string data, such as text data in social network interactions.