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HiCOMB 2017 Keynote Talk
From Molecular Communication and Nano-Networking to Precision Medicine: A Cyber-Physical Systems PerspectiveProfessor Radu Marculescu, Carnegie Mellon University
HiCOMB 2017 Invited Talks
Algorithmic Advances in Transcript QuantificationRob Patro, Stony Brook University
Big Data Challenges in Personalized Medicine for Cancer
Jason McDermott, Pacific Northwest National Lab
Keynote Talk Abstract: Medicine and healthcare will undergo a profound revolution in the 21st century. This talk focuses on the emerging area of molecular communication and nano-networking which targets cell-based therapeutics. Cell-based therapeutics is a key component of precision medicine, i.e. the new paradigm for disease prevention and treatment aimed at providing customized healthcare solutions on a patient-to-patient basis. In recent years, there has been significant progress towards understanding the cellular behavior and controlling individual cells. However, a new perspective able to capture various interactions and emerging behaviors that manifest at population-level is critical to engineer cells behavior, reprogram the cell-cell communication, and develop new strategies that can control the dynamics of population of cells. To this end, we discuss a cyber-physical systems approach that combines the strengths of computational systems biology and emerging single-chip heterogeneous architectures in order to address two of the most important life science challenges of this century, namely microbiome and biofilm dynamics characterization.
Keynote Speaker Biography: Radu Marculescu is a Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University. He received his Ph.D. in Electrical Engineering from the University of Southern California (1998). He has received multiple best paper awards, NSF Career Award (2000), Outstanding Research Award from the College of Engineering (2013). He has organized several international symposia, conferences, workshops, and tutorials, as well as served as guest editor of special issues in archival journals and magazines. His research focuses on design methodologies for embedded and cyber-physical systems, biological systems, and social networks. Professor Marculescu is an IEEE Fellow.
Invited Talk 1 Abstract: Transcript-level quantification and differential analysis are a core part of many RNA-seq-based studies. Yet, efficient and accurate transcript-level analysis remains a challenge. This is due to both the computational burden associated with the need to align and quantify the tens or hundreds of millions of reads comprising typical experiments, as well as the difficult statistical inference problem posed by the need to resolve highly-ambiguous fragment assignments in light of complex splicing and prevalent technical biases. I will discuss our recent work in tackling these challenges, as implemented in the transcript quantification software Salmon. I will focus on the core algorithmic advances of our approach, including a lightweight replacement for alignment that quickly yields the core information necessary for accurate quantification, and a dual-phase inference algorithm that lets Salmon build a rich yet efficiently-optimizable likelihood through which to infer transcript abundances. I will also discuss how Salmon models and corrects, in silico, for numerous technical biases that arise in different RNA-seq protocols, thereby improving the accuracy of inference in experimental data considerably. Finally, I will discuss some of the avenues for downstream analysis opened up by fast and accurate tools like Salmon, as well as some of the remaining challenges in transcript-level quantification and differential analysis.
Invited Speaker Biography: Rob Patro is an assistant professor in the Department of Computer Science at Stony Brook University, where he joined in 2014. He earned his PhD in computer science from the University of Maryland at College Park in 2012 and served as a postdoctoral researcher in the Department of Computational Biology at Carnegie Mellon University from 2012 until 2014. At Stony Brook, he heads the COMBINE lab. His research focuses on scalable, efficient and robust algorithms for high-throughput genomic and transcriptomic analysis, as well as algorithms for inferring and comparing biological networks and studying their evolution.
Invited Talk 2 Abstract: The advent of multiple new technologies for measuring many components in biological systems offers a huge opportunity and challenge for researchers. How to make sense of the mountains of data that describe different aspects of the same, or similar, biological systems. The promise of personalized medicine, bringing specific diagnoses and treatment plans to patients based on their individualized molecular profiles, brings new opportunities for improving human health. I will describe some approaches we are taking to this big data problem in terms of statistical methods and data mining, network and pathway analysis, and generation of testable biological hypotheses. Specifically, I will discuss ongoing work on several projects to capture and interpret the molecular Ôomics profiles of several different types of tumors and to make sense of these profiles in an integrated way to interpret patient outcomes of pathogenesis and drug resistance.
Invited Speaker Biography: Dr. Jason McDermott is a senior research scientist at PNNL. He has extensive research experience in molecular and structural virology and data resource design, data integration and prediction of biological networks, bridging experimental and computational biology. Currently, his research interests include data integration of high-throughput "omics" data for biomarker discovery, developing systems biology models in a number of systems focusing on host-pathogen interactions, characterizing phylogenetic and functional relationships in complex eukaryotic microbial communities, and using network inference and topology to characterize organism-level phenotypes.
HiCOMB 2017 Call For Papers
The size and complexity of genome- and proteome-scale data sets in bioinformatics continues to grow at a furious pace, and the analysis of these complex, noisy, data sets demands efficient algorithms and high performance computer architectures. Hence high-performance computing has become an integral part of research and development in bioinformatics, computational biology, and medical and health informatics. The goal of this workshop is to provide a forum for discussion of latest research in developing high-performance computing solutions to data- and compute-intensive problems arising from all areas of computational life sciences. We are especially interested in parallel and distributed algorithms, memory-efficient algorithms, large scale data mining techniques including approaches for big data and cloud computing, algorithms on multicores, many-cores and GPUs, and design of high-performance software and hardware for biological applications.
The workshop will feature contributed papers as well as invited talks from reputed researchers in the field.
Topics of interest include but are not limited to:
- Bioinformatics data analytics
- Biological network analysis
- Cloud-enabled solutions for computational biology
- Computational genomics and metagenomics
- Computational proteomics and metaproteomics
- DNA assembly, clustering, and mapping
- Energy-aware high performance biological applications
- Gene identification and annotation
- High performance algorithms for computational systems biology
- High throughput, high dimensional data analysis: flow cytometry and related proteomic data
- Parallel algorithms for biological sequence analysis
- Molecular evolution and phylogenetic reconstruction algorithms
- Protein structure prediction and modeling
- Parallel algorithms in chemical genetics and chemical informatics
- Transcriptome analysis with RNASeq
Submission guidelines
To submit a paper, please upload a PDF file through Easy Chair at the HiCOMB 2017 Submission Site. Submitted manuscripts may not exceed ten (10) single-spaced double-column pages using a 10-point size font on 8.5x11 inch pages (IEEE conference style), including figures, tables, and references (see IPDPS Call for Papers for more details). All papers will be reviewed. Proceedings of the workshops will be distributed at the conference and are submitted for inclusion in the IEEE Explore Digital Library after the conference.
Important Dates
| Workshop submissions due: |
January 30, 2017
February 13, 2017 |
| Authors notification: | February 20, 2017 February 27, 2017 |
| Final Camera-ready papers due: | March 15, 2017 March 22, 2017 |
| Workshop: | May 29, 2017 |
Workshop Organizers
Alex Pothen
Computer Science Department
Purdue University
West Lafayette, IN, USA
Email:
Ananth Grama
Computer Science Department
Purdue University
West Lafayette, IN, USA
Email:
Program Committee
- Ariful Azad, Lawrence Berkeley Lab
- Daniela Besozzi, University of Milano-Bicocca, Italy
- Petros Drineas, Purdue University
- Niina S. Haiminen, IBM
- Ananth Kalyanaraman, Washington State University
- Daisuke Kihara, Purdue University
- Mehmet Koyuturk, Case Western Reserve University
- Alba Cristina Magalhaes Alves de Melo, University of Brasilia
- Folker Meyer, Argonne National Lab
- Rob Patro, Stony Brook University
- Saumyadipta Pyne, Indian Institute of Public Health, Hyderabad
- Sanjay Ranka, University of Florida
- Hagit Shatkay, University of Delaware
- Alexandros Stamatakis, Heidelberg Institute for Theoretical Studies
- Sharma Thankachan, Georgia Tech
- Jaroslaw Zola, University at Buffalo, SUNY
Steering Committee Members
- David A. Bader
College of Computing
Georgia Institute of Technology
Email:
- Srinivas Aluru
College of Computing
Georgia Institute of Technology
Email: