5,471 resources related to Computational Biology
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The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted papers will be peer reviewed. Accepted high quality papers will be presented in oral and postersessions, will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE
The world's premier EDA and semiconductor design conference and exhibition. DAC features over 60 sessions on design methodologies and EDA tool developments, keynotes, panels, plus the NEW User Track presentations. A diverse worldwide community representing more than 1,000 organizations attends each year, from system designers and architects, logic and circuit designers, validation engineers, CAD managers, senior managers and executives to researchers and academicians from leading universities.
The ICASSP meeting is the world's largest and most comprehensive technical conference focused on signal processing and its applications. The conference will feature world-class speakers, tutorials, exhibits, and over 50 lecture and poster sessions.
The Frontiers in Education (FIE) Conference is a major international conference focusing on educational innovations and research in engineering and computing education. FIE 2019 continues a long tradition of disseminating results in engineering and computing education. It is an ideal forum for sharing ideas, learning about developments and interacting with colleagues inthese fields.
The CDC is the premier conference dedicated to the advancement of the theory and practice of systems and control. The CDC annually brings together an international community of researchers and practitioners in the field of automatic control to discuss new research results, perspectives on future developments, and innovative applications relevant to decision making, automatic control, and related areas.
The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...
The Transactions on Biomedical Circuits and Systems addresses areas at the crossroads of Circuits and Systems and Life Sciences. The main emphasis is on microelectronic issues in a wide range of applications found in life sciences, physical sciences and engineering. The primary goal of the journal is to bridge the unique scientific and technical activities of the Circuits and Systems ...
The IEEE Reviews in Biomedical Engineering will review the state-of-the-art and trends in the emerging field of biomedical engineering. This includes scholarly works, ranging from historic and modern development in biomedical engineering to the life sciences and medicine enabled by technologies covered by the various IEEE societies.
Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.
Specific topics of interest include, but are not limited to, sequence analysis, comparison and alignment methods; motif, gene and signal recognition; molecular evolution; phylogenetics and phylogenomics; determination or prediction of the structure of RNA and Protein in two and three dimensions; DNA twisting and folding; gene expression and gene regulatory networks; deduction of metabolic pathways; micro-array design and analysis; proteomics; ...
IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2013
The long-term cost of developing and maintaining a computational pipeline that depends upon data integration and sophisticated workflow logic is too high to even contemplate "what if" or ad hoc type queries. In this paper, we introduce a novel application building interface for computational biology research, called VizBuilder, by leveraging a recent query language called BioFlow for life sciences databases. ...
IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2013
An enormous amount of sequence data has been generated with the development of new DNA sequencing technologies, which presents great challenges for computational biology problems such as haplotype phasing. Although arduous efforts have been made to address this problem, the current methods still cannot efficiently deal with the incoming flood of large-scale data. In this paper, we propose a flow ...
IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2018
Machine learning is an integral part of computational biology, and has already shown its use in various applications, such as prognostic tests. In the last few years in the non-biological machine learning community, ensembling techniques have shown their power in data mining competitions such as the Netflix challenge; however, such methods have not found wide use in computational biology. In ...
IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2007
This paper reviews the application of multiobjective optimization in the fields of bioinformatics and computational biology. A survey of existing work, organized by application area, forms the main body of the review, following an introduction to the key concepts in multiobjective optimization. An original contribution of the review is the identification of five distinct "contexts," giving rise to multiple objectives: ...
2007 IEEE 7th International Symposium on BioInformatics and BioEngineering, 2007
Over the past several years there have been repeated analyses of the potential value of petascale bioinformatics and computational biology applications, as well as analyses of the system engineering steps required to implement applications and systems at such scale. Most recently and notably, Snavely et al. published the "Workshop Report: Petascale applications in biological sciences". By one measure the era ...
EMBC 2011-Keynote Lectures and Panel Discussion-PT I-Subra Suresh
EMBC 2012 Theme Speaker: Dr. James Bassingthwaighte
Christos H. Papadimitriou accepts the IEEE John von Neumann Medal - Honors Ceremony 2016
Honors 2020: Michael I. Jordan Wins the IEEE John von Neumann Medal
From Biology to Robot and Back
Planning in Robotics and Beyond - ICRA 2020
A Conversation About Mind/Brain Research and AI Development: IEEE TechEthics Interview
IMS 2015: Luca Pierantoni - A New Challenge in Computational Engineering
Computational Intelligence for Brain Computer Interface
An Introduction to Computational Intelligence in Multi-Criteria Decision-Making: The Intersection of Search, Preference Tradeoff
Using Computational Intelligence to automate Craniofacial Superimposition for Skeleton-based Human Identification
Gary P. Fogel: Applications of Computational Intelligence in Biomedicine
ICASSP 2012 Plenary-Dr. Mitsuo Kawato
EMBC 2011-Program-Systems in Synthetic Biology (Part I)-Pamela A. Silver
Norbert Wiener in the 21st Century Conference Concept
Interview with Jerry Mendel, 2012: CIS Oral History Project
Interview with Ron Yager, 2016: CIS Oral History Project
Interview with Bart Kosko, 2012: CIS Oral History Project
Interview with Shun'ichi Amari, 2014: CIS Oral History Project
The long-term cost of developing and maintaining a computational pipeline that depends upon data integration and sophisticated workflow logic is too high to even contemplate "what if" or ad hoc type queries. In this paper, we introduce a novel application building interface for computational biology research, called VizBuilder, by leveraging a recent query language called BioFlow for life sciences databases. Using VizBuilder, it is now possible to develop ad hoc complex computational biology applications at throw away costs. The underlying query language supports data integration and workflow construction almost transparently and fully automatically, using a best effort approach. Users express their application by drawing it with VizBuilder icons and connecting them in a meaningful way. Completed applications are compiled and translated as BioFlow queries for execution by the data management system LifeDB, for which VizBuilder serves as a front end. We discuss VizBuilder features and functionalities in the context of a real life application after we briefly introduce BioFlow. The architecture and design principles of VizBuilder are also discussed. Finally, we outline future extensions of VizBuilder. To our knowledge, VizBuilder is a unique system that allows visually designing computational biology pipelines involving distributed and heterogeneous resources in an ad hoc manner.
An enormous amount of sequence data has been generated with the development of new DNA sequencing technologies, which presents great challenges for computational biology problems such as haplotype phasing. Although arduous efforts have been made to address this problem, the current methods still cannot efficiently deal with the incoming flood of large-scale data. In this paper, we propose a flow network model to tackle haplotype phasing problem, and explain some classical haplotype phasing rules based on this model. By incorporating the heuristic knowledge obtained from these classical rules, we design an algorithm FNphasing based on the flow network model. Theoretically, the time complexity of our algorithm is <i>O</i> (n<sup>2</sup>m+m<sup>2</sup>), which is better than that of 2SNP, one of the most efficient algorithms currently. After testing the performance of FNphasing with several simulated data sets, the experimental results show that when applied on large-scale data sets, our algorithm is significantly faster than the state-of-the-art Beagle algorithm. FNphasing also achieves an equal or superior accuracy compared with other approaches.
Machine learning is an integral part of computational biology, and has already shown its use in various applications, such as prognostic tests. In the last few years in the non-biological machine learning community, ensembling techniques have shown their power in data mining competitions such as the Netflix challenge; however, such methods have not found wide use in computational biology. In this work, we endeavor to show how ensembling techniques can be applied to practical problems, including problems in the field of bioinformatics, and how they often outperform other machine learning techniques in both predictive power and robustness. Furthermore, we develop a methodology of ensembling, Multi-Swarm Ensemble (MSWE) by using multiple particle swarm optimizations and demonstrate its ability to further enhance the performance of ensembles.
This paper reviews the application of multiobjective optimization in the fields of bioinformatics and computational biology. A survey of existing work, organized by application area, forms the main body of the review, following an introduction to the key concepts in multiobjective optimization. An original contribution of the review is the identification of five distinct "contexts," giving rise to multiple objectives: These are used to explain the reasons behind the use of multiobjective optimization in each application area and also to point the way to potential future uses of the technique
Over the past several years there have been repeated analyses of the potential value of petascale bioinformatics and computational biology applications, as well as analyses of the system engineering steps required to implement applications and systems at such scale. Most recently and notably, Snavely et al. published the "Workshop Report: Petascale applications in biological sciences". By one measure the era of petascale computing in biology began in 2006 with the successful clocking of the Riken Institute Protein Explorer system at 1.0 PetaFLOPS. Still, the state of the art of current applications in bioinformatics and computational biology is generally yet orders of magnitude away from petascale, especially in terms of actual performance. The purpose of this is lecture is to survey the current state of the art in computational biology and bioinformatics at scale. Suggested topics for papers and posters include, but are not limited to, the following specific subjects: What is the current upper limit of scale of applications in bioinformatics and computational biology? What are the factors limiting scalability of these applications? Can we, as recommended by Snavely et al., identify candidate petascale applications in any of the following areas: biomolecular structure modeling, modeling complex biological systems, genomics, customized patient care, ecological components of earth systems modeling, infections disease modeling, or other areas? What are the best ways to measure performance scalability of bioinformatics and computational biology applications? Can we measure what really counts in terms of next generation bioinformatics applications with FLOPS and bytes? The NSF workshop organized by Snavely, Jacobs, and Bader identified several specific applications as candidates for scaling. The resulting report called for attention to progress in scaling applications by identifying problems, resolving those problems, and trying to anticipate problems at a larger scales and making the step to larger scales. Presentations that discuss the steps, challenges, and solutions to incremental scaling of bioinformatics and computational biology applications are particularly encouraged. Practice and experience papers related to this topic will be of particular value to the scientific community as we strive toward petascale applications.
Finding approximately conserved sequences, called motifs, across multiple DNA or protein sequences is an important problem in computational biology. In this paper, we consider the (l; d) motif search problem of identifying one or more motifs of length l present in at least q of the n given sequences, with each occurrence differing from the motif in at most d substitutions. The problem is known to be NP-complete, and the largest solved instance reported to date is (26;11). We propose a novel algorithm for the (l; d) motif search problem using streaming execution over a large set of non-deterministic finite automata (NFA). This solution is designed to take advantage of the micron automata processor, a new technology close to deployment that can simultaneously execute multiple NFA in parallel. We demonstrate the capability for solving much larger instances of the (l; d) motif search problem using the resources available within a single automata processor board, by estimating run-times for problem instances (39; 18) and (40; 17). The paper serves as a useful guide to solving problems using this new accelerator technology.
We introduce RLIMS-P version 2.0, an enhanced rule-based information extraction (IE) system for mining kinase, substrate, and phosphorylation site information from scientific literature. Consisting of natural language processing and IE modules, the system has integrated several new features, including the capability of processing full-text articles and generalizability towards different post-translational modifications (PTMs). To evaluate the system, sets of abstracts and full-text articles, containing a variety of textual expressions, were annotated. On the abstract corpus, the system achieved F-scores of 0.91, 0.92, and 0.95 for kinases, substrates, and sites, respectively. The corresponding scores on the full-text corpus were 0.88, 0.91, and 0.92. It was additionally evaluated on the corpus of the 2013 BioNLP-ST GE task, and achieved an F-score of 0.87 for the phosphorylation core task, improving upon the results previously reported on the corpus. Full- scale processing of all abstracts in MEDLINE and all articles in PubMed Central Open Access Subset has demonstrated scalability for mining rich information in literature, enabling its adoption for biocuration and for knowledge discovery. The new system is generalizable and it will be adapted to tackle other major PTM types. RLIMS-P 2.0 online system is available online (http://proteininformationresource.org/rlimsp/) and the developed corpora are available from iProLINK (http://proteininformationresource.org/iprolink/).
The S100 family is a class of calcium-regulated proteins with EF-hand. They are widely distributed and are implicated in diverse intracellular and extracellular physiological processes. A study of the S100 family using computational biology methods such as multiple sequence alignment, structural alignment and the construction of an evolutionary tree will promote understanding of S100 protein structures and their function, and could provide suggestions for crystallization.
The Largest Common Point-set (LCP) and the Pattern Matching (PM) problems have received much attention in the fields of pattern matching, computer vision and computational biology. Perhaps, the most important application of these problems is the protein structural alignment, which seeks to find a superposition of a pair of input proteins that maximizes a given protein structure similarity metric. Although it has been shown that LCP and PM are both tractable problems, the running times of existing algorithms are high- degree polynomials. Here, we present novel methods for finding approximate and exact threshold-LCP and threshold-PM for r-separated sets, in general, and protein 3D structures, in particular. Improved running times of our methods are achieved by building upon several different, previously published techniques.
The multistate perfect phylogeny problem is a classic problem in computational biology. When no perfect phylogeny exists, it is of interest to find a set of characters to remove in order to obtain a perfect phylogeny in the remaining data. This is known as the character removal problem. We show how to use chordal graphs and triangulations to solve the character removal problem for an arbitrary number of states, which was previously unsolved. We outline a preprocessing technique that speeds up the computation of the minimal separators of a graph. Minimal separators are used in our solution to the missing data character removal problem and to Gusfield's solution of the perfect phylogeny problem with missing data.
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