Calendar

Jun
10
Mon
2019
Computational Medicine in the Cloud
Jun 10 @ 9:00 am – Jun 11 @ 6:00 pm

We are pleased to announce the first ever Computational Medicine in the Cloud Hackathon! NCBI will help run a bioinformatics hackathon in Baltimore, Maryland hosted by the Johns Hopkins University.

We’re specifically looking for folks who have experience in working with complex haplotypes, complex disease, precision medicine, and similar genomic analysis. If this describes you, please apply! This event is for researchers, including students and postdocs, who are already engaged in the use of bioinformatics data or in the development of pipelines for large scale genomic analyses from high-throughput experiments (please note that the event itself will focus on open access public human data).

Potential topics include:

  • Coherent-phenotype mapping to haplotypes
  • Mapping haplotype blocks to ontologies
  • Structural Variants in Health and Disease
  • Haplotypes and RNA-seq
  • Complex Variant Structure
  • Annotation Structures for Complex Variants
  • Visualization of Complex Variants

Hackathon Logistics

The hackathon runs from 9 am – 6 pm each day, with the potential to extend into the evening hours each day. There will also be optional social events at the end of each day. Working groups of five to six individuals, with various backgrounds and expertise, will be formed into five to eight teams with an experienced leader. These teams will build pipelines and tools to analyze large datasets within a cloud infrastructure. On both days, we will come together to discuss progress on each of the topics, bioinformatics best practices, coding styles, etc.

There will be no registration fee associated with attending this event.

Note: Participants will need to bring their own laptop to this program. No financial support for travel, lodging, or meals is available for this event.

 

Datasets

Datasets will come from open access public repositories, with a focus on a number of trios produced by long read sequencing as a base graph and short read datasets in the sequence read archive that have been ported to cloud infrastructure, as well as derivative contigs of the above.

 

Products

All pipelines and other scripts, software, and programs generated in this hackathon will be added to a public GitHub repository designed for that purpose.

Manuscripts describing the design and usage of the software tools constructed by each team may be submitted to an appropriate journal such as the F1000Research hackathons channel, BMC Bioinformatics, GigaScience, Genome Research or PLoS Computational Biology. Ideally, we will present a graph genome, a number of protocols for associating short read omics data with it, and some derived datasets (e.g. variant calls) from such protocols.

 

Application

To apply, please complete this form. Initial applications are due on May 22, 2019 by 3 pm EDT. We will select participants based on the experience and motivation they indicate on the form.

Prior participants and applicants are especially encouraged to apply. The first round of accepted applicants will be notified on May 24 by 3 pm EDT, and will have until noon EDT on May 28 to confirm their participation (especially qualified applicants or those traveling internationally may receive acceptances earlier). If you confirm, you must be willing to commit to both days of the event, as confirming and not attending prevents other data scientists from attending this event.

 

Legal

Entrants retain ownership of all intellectual property rights (including moral rights) in the code submitted to as well as developed in the hackathon. Employees of the U.S. Government attending as part of their official duties retain no copyright in their work and their work is in the public domain in the U.S.

The Government disclaims any rights in the code submitted or developed in the hackathon.

Participants agree to publish the code and any related data in GitHub.

For more information, or with any questions, please contact Ben Busby (ben.busby@nih.gov).

Aug
12
Mon
2019
Novartis Quantitative Sciences Academia-to-Industry Hackathon
Aug 12 @ 9:00 am – Aug 23 @ 5:00 pm
Sep
3
Tue
2019
Rose Faghih, University of Houstin, “Wearable-Machine Interface Architectures for Neurocognitive Stress”
Sep 3 @ 11:00 am – 12:00 pm

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Bio

“Wearable-Machine Interface Architectures for Neurocognitive Stress”

Dr. Rose T. Faghih is an assistant professor of Electrical and Computer Engineering at the University of Houston where she directs the Computational Medicine Laboratory. She received a bachelor’s degree (summa cum laude) in Electrical Engineering (Honors Program Citation) from the University of Maryland, and S.M. and Ph.D. degrees in Electrical Engineering and Computer Science with a minor in Mathematics from MIT, where she was a member of the MIT Laboratory for Information and Decision Systems as well as the MIT-Harvard Neuroscience Statistics Research Laboratory. She completed her postdoctoral training at the Department of Brain and Cognitive Sciences and Picower Institute for Learning and Memory at MIT. Dr. Faghih has won multiple awards, including the 2016 IEEE-USA New Face of Engineering award. Her research interests include control, estimation, and system identification of neural and physiological systems. For more information, please visit the lab’s website: http://ComputationalMedicineLab.ece.uh.edu/

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Abstract

“Wearable-Machine Interface Architectures for Neurocognitive Stress”

The human body responds to neurocognitive stress in multiple ways through its autonomic nervous system. Changes in skin conductance measurements indicate sudomotor nerve activity, and could be used in inferring the underlying autonomic nervous system stimulation. We model skin conductance measurements using a state-space model with sparse impulsive events as inputs to the model. Then, we recover the timings and amplitudes of sudomotor nerve activity using a generalized cross-validation based sparse recovery approach. Subsequently, we relate arousal to the probability that a phasic driver impulse occurs in skin conductance signals to continuously track a neurocognitive-stress-related arousal level. Finally, we close the loop using fuzzy control. In particular, we design excitatory and inhibitory wearable machine-interface architectures to regulate neurocognitive-stress-related arousal. Results demonstrate a promising approach for tracking and regulating neurocognitive stress through wearable devices. Since wearable devices can be used conveniently in one’s daily life, wearable machine-interface architectures have a great potential to monitor and regulate one’s neurocognitive stress seamlessly in real-world situations.

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Oct
8
Tue
2019
Casey Overby Taylor, Johns Hopkins School of Medicine “Accelerating Clinical Decision Support for Data-Driven Personal Guidance”
Oct 8 @ 2:00 pm – 3:00 pm

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Bio

“Accelerating Clinical Decision Support for Data-Driven Personal Guidance”

Dr. Casey Overby Taylor is Assistant Professor in the Division of General Internal Medicine in Johns Hopkins School of Medicine (SoM) and is a Fellow in the Johns Hopkins Malone Center for Engineering in Healthcare. She has joint appointments in the Division of Health Sciences Informatics in the SoM and the Department of Health Policy and Management in the Johns Hopkins Bloomberg School of Public Health, and secondary appointments in the Biomedical Engineering and Computer Science Departments in the Johns Hopkins Whiting School of Engineering. Prior to her move to Hopkins in 2016, she was Assistant Professor in the University of Maryland Program for Personalized and Genomic Medicine. Dr. Taylor’s research draws from biomedical informatics and the related field of biomedical data science, to address the challenge of how to incorporate technology and digital approaches into clinical research and healthcare practices. She also draws from comparative effectiveness research approaches, including experience with conceptualizing and measuring implementation outcomes, to investigate the use of clinical decision support as a strategy to improve the adoption of clinically actionable guidelines. Dr. Taylor has previously received funding from AHRQ to develop clinical decision support using an implementation model that engages stakeholders and uses open source decision support platforms (R21 HS023390 [Overby]). Factors identified from that work have informed her current work to enable tailored and multifaceted strategies to implement clinical decision support. Feasibility is being studied through local and national collaborations, including the NIH-funded electronic medical records and genomics (eMERGE) Network where Dr. Taylor serves as co-Chair of the Electronic Health Records (EHR) integration workgroup.

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Abstract

“Accelerating Clinical Decision Support for Data-Driven Personal Guidance”

 

As healthcare practices are rapidly becoming more data-intensive, we are seeing a move from basic, rule-based, clinical decision support guidance to more data-driven and personalized guidance. Indeed, considering the multiple factors when assessing patient risk for poor health outcomes has potential to improve overall understanding of risk when compared to risk assessments accounting for a single factor alone.   The success of such approaches to advance healthcare practices, however, will depend on our capacity to deploy clinical decision support in the electronic health record in a timely manner. Despite considerable effort and resources, the adoption of clinically actionable risk assessments remains slow. This talk will explore ways that data science, biomedical informatics, and implementation science research are helping to realize the potential of emerging data-driven methods to direct healthcare practices in ways that are more comprehensive for patients by paving the path to create, approve and deploy clinical decision support within the electronic health record.

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JHU - Institute for Computational Medicine