Bioinformatics

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Bioinformatics is the application of computer science and information technology to the field of biology. (Wikipedia.org)






Conferences related to Bioinformatics

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2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

The conference program will consist of plenary lectures, symposia, workshops and invited sessions 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 poster sessions, will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE.

  • 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

    The conference will cover diverse topics ranging from biomedical engineering to healthcare technologies to medical and clinical applications. The conference program will consist of invited plenary lectures, symposia, workshops, invited sessions and oral and poster sessions of unsolicited contributions. All papers will be peer reviewed and accepted papers of up to 4 pages will appear in the Conference Proceedings and be indexed by IEEE Xplore and Medline/PubMed.

  • 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

    The conference program will consist of plenary lectures, symposia, workshops and invited sessions 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 poster sessions, will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE.

  • 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

    The Annual International Conference of the IEEE Engineering in Medicine and Biology Society covers a broad spectrum of topics from biomedical engineering and physics to medical and clinical applications. The conference program will consist of invited plenary lectures, symposia, workshops, invited sessions, oral and poster sessions of unsolicited contributions. All papers will be peer reviewed and accepted papers of up to 4 pages will appear in the Conference Proceedings and be indexed by PubMed and EI. Prop

  • 2012 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

    The annual conference of EMBS averages 2000 attendees from over 50 countries. The scope of the conference is general in nature to focus on the interdisciplinary fields of biomedical engineering. Themes included but not limited to are: Imaging, Biosignals, Biorobotics, Bioinstrumentation, Neural, Rehabilitation, Bioinformatics, Healthcare IT, Medical Devices, etc

  • 2011 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

    The annual conference of EMBS averages 2000 attendees from over 50 countries. The scope of the conference is general in nature to focus on the interdisciplinary fields of biomedical engineering. Themes included but not limited to are: Imaging, Biosignals, Biorobotics, Bioinstrumentation, Neural, Rehabilitation, Bioinformatics, Healthcare IT, Medical Devices, etc.

  • 2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

    The annual conference of EMBS averages 2000 attendees from over 50 countries. The scope of the conference is general in nature to focus on the interdisciplinary fields of biomedical engineering. Themes included but not limited to are: Imaging, Biosignals, Biorobotics, Bioinstrumentation, Neural, Rehabilitation, Bioinformatics, Healthcare IT, Medical Devices, etc

  • 2009 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

    The annual conference of EMBS averages 2000 attendees from over 50 countries. The scope of the conference is general in nature to focus on the interdisciplinary fields of biomedical engineering. Themes included but not limited to are: Imaging, Biosignals, Biorobotics, Bioinstrumentation, Neural, Rehabilitation, Bioinformatics, Healthcare IT, Medical Devices, etc

  • 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

    The general theme of EMBC'08 is "Personalized Healthcare through Technology", covering a broad spectrum of topics from biomedical and clinical engineering and physics to medical and clinical applications. Transfer of research results from academia to industry will also be a focus of the conference.

  • 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

  • 2005 27th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)


2014 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid)

The CCGrid symposium provides a major international forum for presenting and sharing recent research accomplishments and technological developments in the field of Cluster,Cloud and Grid computing. The symposium is attended by researchers and practitioners from both industry and academia. In 2013 the conference will be held in Chicago Illinois USA.


2014 IEEE Biomedical Circuits and Systems Conference (BioCAS)

Application, Scientific/Academic

  • 2013 IEEE Biomedical Circuits and Systems Conference (BioCAS)

    IEEE BioCAS 2013 covers biomedical circuits, systems and engineering topics including: Wireless, Wearable, and Implantable/Injectable Technology; Medical Information and Telecare Systems; Harvesting/Scavenging Energy for Biomedical Devices; Biometrics, Biomedical Signal Processing and Bioimaging Technology; Integrated Biomedical Systems, BioMEMS, Bio-sensors/actuators & Lab-on-chip; Bio-inspired & Biomolecular Circuits & Systems; Circuits for Biomedical Systems.

  • 2012 IEEE Biomedical Circuits and Systems Conference (BioCAS)

    There is an explosion of research activities in life sciences, physical sciences and engineering with application to medical problems in recent years. Such activities require inter-disciplinary collaborations among scientists, engineers, medical researchers and practitioners. This conference is the premier forum where everyone can share results and innovative solutions.

  • 2011 IEEE Biomedical Circuits and Systems Conference (BioCAS)

    IEEE BioCAS 2011 covers biomedical engineering topics including: Wireless, Wearable, and Implantable/Injectable Technology; Medical Information and Telecare Systems; Harvesting/Scavenging Energy for Biomedical Devices; Biometrics, Biomedical Signal Processing and Bioimaging Technology; Integrated Biomedical Systems, BioMEMS, Bio-sensors/actuators & Lab-on-chip; Bio-inspired & Biomolecular Circuits & Systems; Circuits for Biomedical Systems.

  • 2010 IEEE Biomedical Circuits and Systems Conference (BioCAS)

    The recent years have witnessed an explosion of research activities in the areas of life sciences, physical sciences and engineering with application to medical problems. Such activities require inter-disciplinary collaborations among scientists, engineers, medical researchers, and practitioners to solve complex real world problems. This conference is the premier forum where members of the IEEE Circuits & Systems Society present results and innovative solutions for today's health problems at frontiers of Bi

  • 2009 IEEE Biomedical Circuits and Systems Conference (BioCAS)

    The conference mission is to bring together scientists, engineers and medical researchers and practitioners to discuss cutting-edge research in biomedical circuits and systems. The scope of the conference is related to the multi-disciplinary and emerging areas of research at the crossroad of life sciences and circuits and systems engineering disciplines.

  • 2008 IEEE Biomedical Circuits and Systems Conference - Intelligent Biomedical Systems (BioCAS)

    The conference is multidisciplinary in nature comprising an eclectic mix of insightful tutorials, and technical sessions. The organization and planning of the conference enables members of our communities to broaden their knowledge in emerging areas of research at the interface of the life sciences and the circuits and systems engineering discipline.


2014 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)

CIBCB 2014 will bring together top researchers, practitioners, and students from around the world to discuss the latest advances in the field of Computational Intelligence and its application to real world problems in biology, bioinformatics, computational biology, chemical informatics, bioengineering, and related fields. Computational Intelligence approaches include artificial neural networks and learning systems, fuzzy logic, evolutionary algorithms, hybrid algorithms, and other emerging techniques.

  • 2013 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)

    The CIBCB 2013 symposium will bring together top researchers, practitioners, and students from around the world to discuss the latest advances in the field of Computational Intelligence and its application to real-world problems in theoretical and applied biology, bioinformatics, computational biology, chemical informatics, bioengineering and related fields. Computational Intelligence (CI) approaches include artificial neural networks, fuzzy logic, evolutionary computation, hybrid approaches and other emerging techniques including but not limited to ant colony optimization, particle swarm optimization, and support vector machines.The use of computational intelligence must play a substantial role in submitted papers.

  • 2012 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)

    The scope of this symposium covers the application of computational Intelligence techniques, such as evolutionary computation, neural networks and fuzzy systemsand, to real world problems in biology, including bioinformatics, computational biology, chemical informatics, bioengineering and related fields.

  • 2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)

    This symposium will bring together top researchers, practitioners, and students from around the world to discuss the latest advances in the field of Computational Intelligence and its application to real world problems in biology, bioinformatics, computational biology, chemical informatics, bioengineering and related fields.

  • 2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)

    computational intelligence for bioinformatics, computational biology

  • 2005 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology (CIBCB)


2014 IEEE Frontiers in Education Conference (FIE)

The Frontiers in Education (FIE)Conference is the major international conference about educational innovations and research in engineering and computing. FIE 2014 continues a long tradition of disseminating results in these areas. It is an ideal forum for sharing ideas; learning about developments in computer science, engineering, and technology education; and interacting with colleagues in these fields.

  • 2013 IEEE Frontiers in Education Conference (FIE)

    The Frontiers in Education (FIE)Conference is the major international conference about educational innovations and research in engineering and computing. FIE 2013 continues a long tradition of disseminating results in these areas. It is an ideal forum for sharing ideas; learning about developments in computer science, engineering, and technology education; and interacting with colleagues in these fields.

  • 2012 IEEE Frontiers in Education Conference (FIE)

    The Frontiers in Education (FIE)Conference is the major international conference about educational innovations and research in engineering and computing. FIE 2012 continues a long tradition of disseminating results in these areas. It is an ideal forum for sharing ideas; learning about developments in computer science, engineering, and technology education; and interacting with colleagues in these fields.

  • 2011 Frontiers in Education Conference (FIE)

    The Frontiers in Education (FIE) Conference is the major international conference about educational innovations and research in engineering and computing. FIE 2011 continues a long tradition of disseminating results in these areas. It is an ideal forum for sharing ideas; learning about developments in computer science, engineering, and technology education; and interacting with colleagues in these fields.

  • 2010 IEEE Frontiers in Education Conference (FIE)

    (FIE) Conference is a major international conference devoted to improvements in computer science, engineering, and technology (CSET) education. FIE 2008 continues a long tradition of disseminating educational research results and innovative practices in CSET education. It is an ideal forum for sharing ideas, learning about developments in CSET education, and interacting with colleagues.

  • 2009 IEEE Frontiers in Education Conference (FIE)

    FIE is a major international conference devoted to improvements in computer science, engineering and technology (CSET) education. FIE continues a loong tradition of disseminating educational research results and innovative practices in CSET education. It is an ideal forum for sharing ideas, learning about developments in CSET education, and interacting with colleagues.

  • 2008 IEEE Frontiers in Education Conference (FIE)

  • 2007 IEEE Frontiers in Education Conference (FIE)

    Globalization has dramatically changed engineering. Global engineering teams design products for global markets. Knowledge has no borders in a world where information flow is digitalized and sent worldwide in seconds. A core requirement of engineering globalization is an understanding of how the different cultures of the global marketplace shape product development, mult-national engineering teams, and consumer expectations. Engineering education must address this issue.


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Periodicals related to Bioinformatics

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Biomedical Engineering, IEEE Reviews in

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.


Biomedical Engineering, IEEE Transactions on

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.


Computational Biology and Bioinformatics, IEEE/ACM Transactions on

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; ...


Computer

Computer, the flagship publication of the IEEE Computer Society, publishes peer-reviewed technical content that covers all aspects of computer science, computer engineering, technology, and applications. Computer is a resource that practitioners, researchers, and managers can rely on to provide timely information about current research developments, trends, best practices, and changes in the profession.


Computers, IEEE Transactions on

Design and analysis of algorithms, computer systems, and digital networks; methods for specifying, measuring, and modeling the performance of computers and computer systems; design of computer components, such as arithmetic units, data storage devices, and interface devices; design of reliable and testable digital devices and systems; computer networks and distributed computer systems; new computer organizations and architectures; applications of VLSI ...


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Most published Xplore authors for Bioinformatics

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Xplore Articles related to Bioinformatics

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Predicting Future High-Cost Patients: A Real-World Risk Modeling Application

Sai T. Moturu; William G. Johnson; Huan Liu 2007 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2007), 2007

Health care data from patients in the Arizona Health Care Cost Containment System, Arizona's Medicaid program, provides a unique opportunity to exploit state-of-the-art data processing and analysis algorithms to mine the data and provide actionable results that can aid cost containment. This work addresses specific challenges in this real-life health care application to build predictive risk models for forecasting future ...


Micro-repetitive Structure of Genomic Sequences and the Identification of Ancient Repeat Elements

Abanish Singh; Cedric Feschotte; Nikola Stojanovic 2007 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2007), 2007

For many years the attempts to identify functional elements in genomic sequences through motif over- representation have been problematic, as every procedure to isolate such motifs resulted in a very large number of candidates with highly significant p-values. In this paper we postulate that most of these elements originate in ancient transpositional activity, with copies becoming so broken over time ...


Predicting Markov Chain Order in Genomic Sequences

Lenwood S. Heath; Amrita Pati 2007 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2007), 2007

Genomic sequences display characteristic features at various scales ranging from oligonucleotide frequencies to large organizational units such as genes. The generation of such a sequence, defined as a string over the alphabet SigmaDNA={A C, T, G}, can be approximated by a formal machine, a Markov chain having strings as states, whose parameters lend unique characteristics to the sequence. We present ...


Analysis of genome signature strength of SARS coronavirus using Self-Organizing Map neural network

Francis Thamburaj; Gopinath Ganapathy 2010 International Conference on Communication and Computational Intelligence (INCOCCI), 2010

The nucleotide usage patterns vary not only from organism to organism, but also between genes in the same genome. Each genome has its own characteristics. This unique identity, called genome signature, of a genome is multidimensional. One of the ways to probe into this area is to analyze the nucleotide sequence composition of the genome. In this paper, the nucleotide ...


Spatial and Temporal Characteristics of Carbon Tetrachloride Pollution in a Karst Aquifer

Xueqiang Zhu; Baoping Han; Yan Sun 2008 2nd International Conference on Bioinformatics and Biomedical Engineering, 2008

The karst aquifer in a northern city of China was polluted by carbon tetrachloride (CCl4). The pollution characteristics was studied by four years' monitoring CCl4 concentration of the karst groundwater in 15 typical wells and analysis of the CCl4 content in 206 soil samples from 17 boreholes in polluted source area. The CCl4 pollution plume is distributed as a belt-dumbbell ...


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Educational Resources on Bioinformatics

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eLearning

Predicting Future High-Cost Patients: A Real-World Risk Modeling Application

Sai T. Moturu; William G. Johnson; Huan Liu 2007 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2007), 2007

Health care data from patients in the Arizona Health Care Cost Containment System, Arizona's Medicaid program, provides a unique opportunity to exploit state-of-the-art data processing and analysis algorithms to mine the data and provide actionable results that can aid cost containment. This work addresses specific challenges in this real-life health care application to build predictive risk models for forecasting future ...


Micro-repetitive Structure of Genomic Sequences and the Identification of Ancient Repeat Elements

Abanish Singh; Cedric Feschotte; Nikola Stojanovic 2007 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2007), 2007

For many years the attempts to identify functional elements in genomic sequences through motif over- representation have been problematic, as every procedure to isolate such motifs resulted in a very large number of candidates with highly significant p-values. In this paper we postulate that most of these elements originate in ancient transpositional activity, with copies becoming so broken over time ...


Predicting Markov Chain Order in Genomic Sequences

Lenwood S. Heath; Amrita Pati 2007 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2007), 2007

Genomic sequences display characteristic features at various scales ranging from oligonucleotide frequencies to large organizational units such as genes. The generation of such a sequence, defined as a string over the alphabet SigmaDNA={A C, T, G}, can be approximated by a formal machine, a Markov chain having strings as states, whose parameters lend unique characteristics to the sequence. We present ...


Analysis of genome signature strength of SARS coronavirus using Self-Organizing Map neural network

Francis Thamburaj; Gopinath Ganapathy 2010 International Conference on Communication and Computational Intelligence (INCOCCI), 2010

The nucleotide usage patterns vary not only from organism to organism, but also between genes in the same genome. Each genome has its own characteristics. This unique identity, called genome signature, of a genome is multidimensional. One of the ways to probe into this area is to analyze the nucleotide sequence composition of the genome. In this paper, the nucleotide ...


Spatial and Temporal Characteristics of Carbon Tetrachloride Pollution in a Karst Aquifer

Xueqiang Zhu; Baoping Han; Yan Sun 2008 2nd International Conference on Bioinformatics and Biomedical Engineering, 2008

The karst aquifer in a northern city of China was polluted by carbon tetrachloride (CCl4). The pollution characteristics was studied by four years' monitoring CCl4 concentration of the karst groundwater in 15 typical wells and analysis of the CCl4 content in 206 soil samples from 17 boreholes in polluted source area. The CCl4 pollution plume is distributed as a belt-dumbbell ...


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IEEE-USA E-Books

  • Index

    In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs -- -kernels--for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replacing neural networks in a variety of fields, including engineering, information retrieval, and bioinformatics.Learning with Kernels provides an introduction to SVMs and related kernel methods. Although the book begins with the basics, it also includes the latest research. It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well- founded yet easy-to-use kernel algorithms and to understand and apply the powerful algorithms that have been developed over the last few years.

  • No title

    Graphical models (e.g., Bayesian and constraint networks, influence diagrams, and Markov decision processes) have become a central paradigm for knowledge representation and reasoning in both artificial intelligence and computer science in general. These models are used to perform many reasoning tasks, such as scheduling, planning and learning, diagnosis and prediction, design, hardware and software verification, and bioinformatics. These problems can be stated as the formal tasks of constraint satisfaction and satisfiability, combinatorial optimization, and probabilistic inference. It is well known that the tasks are computationally hard, but research during the past three decades has yielded a variety of principles and techniques that significantly advanced the state of the art. In this book we provide comprehensive coverage of the primary exact algorithms for reasoning with such models. The main feature exploited by the algorithms is the model's graph. We present inference-based, m ssage-passing schemes (e.g., variable-elimination) and search-based, conditioning schemes (e.g., cycle-cutset conditioning and AND/OR search). Each class possesses distinguished characteristics and in particular has different time vs. space behavior. We emphasize the dependence of both schemes on few graph parameters such as the treewidth, cycle-cutset, and (the pseudo-tree) height. We believe the principles outlined here would serve well in moving forward to approximation and anytime-based schemes. The target audience of this book is researchers and students in the artificial intelligence and machine learning area, and beyond.

  • Frontmatter

    The prelims comprise: Half Title Wiley Series Page Title Copyright Dedication Contents Foreword Preface About the Authors

  • Notation and Symbols

    In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs -- -kernels--for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replacing neural networks in a variety of fields, including engineering, information retrieval, and bioinformatics.Learning with Kernels provides an introduction to SVMs and related kernel methods. Although the book begins with the basics, it also includes the latest research. It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well- founded yet easy-to-use kernel algorithms and to understand and apply the powerful algorithms that have been developed over the last few years.

  • Index

    The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. _ Introduction to Machine Learnin_g is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. Subjects include supervised learning; Bayesian decision theory; parametric, semi-parametric, and nonparametric methods; multivariate analysis; hidden Markov models; reinforcement learning; kernel machines; graphical models; Bayesian estimation; and statistical testing. Machine learning is rapidly becoming a skill that computer science students must master before graduation. The third edition of _Introduction to Machin Learning_ reflects this shift, with added support for beginners, including selected solutions for exercises and additional example data sets (with code available online). Other substantial changes include discussions of outlier detection; ranking algorithms for perceptrons and support vector machines; matrix decomposition and spectral methods; distance estimation; new kernel algorithms; deep learning in multilayered perceptrons; and the nonparametric approach to Bayesian methods. All learning algorithms are explained so that students can easily move from the equations in the book to a computer program. The book can be used by both advanced undergraduates and graduate students. It will also be of interest to professionals who are concerned with the application of machine learning methods.

  • Introduction to Pattern Recognition and Data Mining

    This introductory chapter of the book provides a brief review of pattern recognition, data mining, and application of pattern recognition algorithms in data mining problems. The objective of the book is to provide some results of investigations, both theoretical and experimental, addressing the relevance of rough-fuzzy approaches to pattern recognition with real-life applications. The chapter first briefly presents a description of the basic concept, features, and techniques of pattern recognition. It then elaborates the data mining aspect, discussing its components, tasks involved, approaches, and application areas. The chapter next introduces the pattern recognition perspective of data mining and mentions related research challenges. It describes the role of soft computing in pattern recognition and data mining. Finally, the chapter discusses the scope and organization of the book. fuzzy logic

  • Probabilistic Modeling and Inference: Examples

    This chapter contains sections titled: The Simplest Sequence Models, Statistical Mechanics

  • Back Matter

    The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. _ Introduction to Machine Learnin_g is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. Subjects include supervised learning; Bayesian decision theory; parametric, semi-parametric, and nonparametric methods; multivariate analysis; hidden Markov models; reinforcement learning; kernel machines; graphical models; Bayesian estimation; and statistical testing. Machine learning is rapidly becoming a skill that computer science students must master before graduation. The third edition of _Introduction to Machin Learning_ reflects this shift, with added support for beginners, including selected solutions for exercises and additional example data sets (with code available online). Other substantial changes include discussions of outlier detection; ranking algorithms for perceptrons and support vector machines; matrix decomposition and spectral methods; distance estimation; new kernel algorithms; deep learning in multilayered perceptrons; and the nonparametric approach to Bayesian methods. All learning algorithms are explained so that students can easily move from the equations in the book to a computer program. The book can be used by both advanced undergraduates and graduate students. It will also be of interest to professionals who are concerned with the application of machine learning methods.

  • Identifying Protein Complexes from Protein-Protein Interaction Networks

    Identification of protein complexes has significant meaning in mining and analyzing function of modules in a protein network, as it reveals protein function and explains the mechanism of a particular biological process. Computation methods are also used to predict protein complexes from prior knowledge of known complexes. In protein-protein interaction (PPI) network, the clusters correspond to two types of module: protein complexes and functional modules; however, there is no clear distinction between them. The clusters predicted by the clustering methods discussed in this chapter are considered as protein complexes. The aim of many density-based algorithms is to detect the densely connected subgraphs. Hierarchical clustering is one of the most common methods of classification used in PPI networks to detect protein complexes or functional modules. In this chapter, the authors discuss some representative algorithms that have been proposed for the purpose of finding overlapping clusters.

  • Rough-Fuzzy Hybridization and Granular Computing

    During the past decade, there have been several attempts to derive hybrid methods by judiciously combining the merits of fuzzy logic and rough sets under the name rough-fuzzy or fuzzy-rough computing. The result is a more intelligent and robust system providing a human interpretable, low cost, approximate solution, as compared to traditional techniques. This chapter discusses some of the theoretical developments relevant to pattern recognition. It first briefly introduces the necessary notions of fuzzy sets and rough sets. The chapter then discusses the concepts of granular computing and fuzzy granulation and emergence of rough-fuzzy computing. It presents a mathematical framework of generalized rough sets for uncertainty handling and defining rough entropy. Finally, the chapter discusses various roughness and entropy measures with properties. fuzzy logic; fuzzy set theory; rough set theory



Standards related to Bioinformatics

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Standard for Bioinformatics Data Structures – Framework and Overview

The scope of this project is to develop a framework for standards and protocols, incorporating existing standards where appropriate, to support the bioinformatics sciences with common definition, storage and exchange of information between them. The project will define efforts in the area of nomenclature, databases, access protocols, benchmarks, and validation suites for a variety of bioinformatics data (e.g., genomics, proteomics, ...


Standard for Bioinformatics Standards for Flow Cytometry

This standard supports the common definition, storage and exchange of flow cytometry data. Existing standards are incorporated where appropriate.


Standard for Sequence Ontology

The Sequence Ontology is a well developed current working procedure in the Bioinformatics community, this work will formalize that methodology into a standard. The Sequence Ontology (SO) is designed for three different, but related, purposes. The first of these is to provide a structured controlled vocabulary for the description of features that may be describe by their spatial location upon ...



Jobs related to Bioinformatics

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