Congratulations to Drs. Bahar, Jernigan, and Dill who published a book with Garland Science titled, Protein Actions. The book:
- Introduces a quantitative approach to the basic principles of protein structure and function
- Emphasizes concepts and theory of proteins
- Gives the full breath of protein principles, bioinformatics, molecular simulations, and mechanism
- Illuminates how proteins act and interact
- Ends with a synopsis on the roles of computational biology in advancing protein science
There will be a celebration held at the Biophysical Society Meeting in New Orleans on Tuesday, February 14, 2017.
TECBio (http://www.tecbioreu.pitt.edu/) is an NSF-funded, 10-week, summer, REU program hosted by the Department of Computational and Systems Biology that provides a challenging graduate-level research experience in computational biology.
Last year, we completed our 7th summer of TECBio and applied for a renewal. We’re very happy to announce that we were awarded 4 more years of the program! We will be funded until 2021.
Thanks to Joseph Ayoob (PI), Chakra Chennubhotla (CO-PI), the rest of Team TECBio, our mentors, and our students for all of their efforts in creating and maintaining a successful undergraduate program. We look forward to the next 4 years of research and training with the future generation of scientists!
Dr. Jianhua Xing and his collaborator Dr. Youhua Liu (Department of Pathology) received a CTSI Biomedical Modeling Pilot Award. The project is to use modeling and experimental studies to investigate development and regulation of acute kidney injury and chronic kidney diseases.
Scalable Machine Learning for Big Data Biology
Number of units (credits): 3
Grading Basis: Letter Grade
Day/Time/Location: Biomedical Science Tower 3, Room 3081, Wed–Fri 1:30–3
Enrollment Capacity: 25
Machine learning (ML) has become an integral part of computational thinking in the era of big data biology. This course will focus on understanding the statistical structure of large-scale biological datasets using ML algorithms. We will cover the basics of ML and study their scalable versions for implementation on a distributed computing framework. We will pursue distributed ML algorithms for: matrix factorization, convex optimization, dimensionality reduction, clustering, classification, graph analytics and deep learning, among others.
The course will be project driven (3 to 4 mini projects) with source material from genomic sciences, structural biology, drug discovery, systems modeling and biological imaging. There will be one final project, along with a presentation.
Students will be expected to design, implement and test their ML solutions in Apache Spark.
No biological background is expected. The assignments will cover the necessary biology. Experience in programming and some software engineering is preferred. Knowledge of probability, statistics, linear algebra and algorithms is a bonus.
The class is open to senior-year undergraduates and graduate students.
Prof. Chakra Chennubhotla
Prof. David Koes
(Official White House Photo by Pete Souza)
The Frontiers Conference, taking place on October 13, will be cohosted by the University of Pittsburgh and Carnegie Mellon University to explore the future of innovation here and around the world
The Conference will include programming featuring five “Frontiers” of innovation:
- Personal frontiers in health care innovation and precision medicine;
- Local frontiers in building smart, inclusive communities, including through investments in open data and the Internet of things;
- National frontiers in harnessing the potential of artificial intelligence, including data science, machine learning, automation, and robotics to engage and benefit all Americans;
- Global frontiers in accelerating the clean energy revolution and developing advanced climate information, tools, services, and collaborations; and
- Interplanetary frontiers in space exploration, including our journey to Mars.
To learn more about the conference or to nominate an innovator, please visit: FrontiersConference.org.