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)


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


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.


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; ...


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 ...


Education, IEEE Transactions on

Educational methods, technology, and programs; history of technology; impact of evolving research on education.


Information Technology in Biomedicine, IEEE Transactions on

Telemedicine, teleradiology, telepathology, telemonitoring, telediagnostics, 3D animations in health care, health information networks, clinical information systems, virtual reality applications in medicine, broadband technologies, and global information infrastructure design for health care.


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Most published Xplore authors for Bioinformatics

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Xplore Articles related to Bioinformatics

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Incorporating estimated motion in real-time background subtraction

Minglun Gong; Li Cheng 2011 18th IEEE International Conference on Image Processing, 2011

Many existing background subtraction approaches model background color only and detect foreground as outliers, and hence may confuse background changes or noises with true foreground. We present a novel algorithm that utilizes motion cues computed from an optical flow algorithm. The additional motion information allows aligning moving foreground objects over time so that models can be built for foreground as ...


A Study of the Autofluorescence Spectrum of the Tissues in Various Places of Rats during the Period of Medium-Intensity Aerobic Exercise

Wenjun Ren; Binnan Zhang; Zhenghong Xu; Zhenxi Zhang; Zheng Li 2010 4th International Conference on Bioinformatics and Biomedical Engineering, 2010

Aim: The purpose of the paper is to investigate autofluorescence characteristics of tissues (mainly the adipose tissue) and organs in various places of the exercising rats. Methods: The laser-induced fluorescence (LIF) technique is used to research into autofluorescence characteristics of tissues and organs of various places of the exercising rats after one medium-intensity treadmill exercise up to exhaustion. Peculiar fluorescence ...


Selecting Graph Cut Solutions via Global Graph Similarity

Canh Hao Nguyen; Nicolas Wicker; Hiroshi Mamitsuka IEEE Transactions on Neural Networks and Learning Systems, 2014

Graph cut is a common way of clustering nodes on similarity graphs. As a clustering method, it does not give a unique solution under usually used loss functions. We specifically show the problem in similarity graph-based clustering setting that the resulting clusters might be even disconnected. This is counter-intuitive as one wish to have good clustering solutions in the sense ...


Interaction Techniques for Selecting and Manipulating Subgraphs in Network Visualizations

Michael J. McGuffin; Igor Jurisica IEEE Transactions on Visualization and Computer Graphics, 2009

We present a novel and extensible set of interaction techniques for manipulating visualizations of networks by selecting subgraphs and then applying various commands to modify their layout or graphical properties. Our techniques integrate traditional rectangle and lasso selection, and also support selecting a node's neighbourhood by dragging out its radius (in edges) using a novel kind of radial menu. Commands ...


Comparison of stability measures for feature selection

Peter Drotár; Zdeněk Smékal 2015 IEEE 13th International Symposium on Applied Machine Intelligence and Informatics (SAMI), 2015

The feature selection is inevitable part of machine learning techniques in biomedical engineering and bioinformatics. Feature selection methods are used to select the most discriminative features, e.g. for disease classification. Even if there are plenty of feature selection methods the stability of these algorithms is still open question. Another issue with assessing the stability of feature selection is that there ...


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Educational Resources on Bioinformatics

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eLearning

Incorporating estimated motion in real-time background subtraction

Minglun Gong; Li Cheng 2011 18th IEEE International Conference on Image Processing, 2011

Many existing background subtraction approaches model background color only and detect foreground as outliers, and hence may confuse background changes or noises with true foreground. We present a novel algorithm that utilizes motion cues computed from an optical flow algorithm. The additional motion information allows aligning moving foreground objects over time so that models can be built for foreground as ...


A Study of the Autofluorescence Spectrum of the Tissues in Various Places of Rats during the Period of Medium-Intensity Aerobic Exercise

Wenjun Ren; Binnan Zhang; Zhenghong Xu; Zhenxi Zhang; Zheng Li 2010 4th International Conference on Bioinformatics and Biomedical Engineering, 2010

Aim: The purpose of the paper is to investigate autofluorescence characteristics of tissues (mainly the adipose tissue) and organs in various places of the exercising rats. Methods: The laser-induced fluorescence (LIF) technique is used to research into autofluorescence characteristics of tissues and organs of various places of the exercising rats after one medium-intensity treadmill exercise up to exhaustion. Peculiar fluorescence ...


Selecting Graph Cut Solutions via Global Graph Similarity

Canh Hao Nguyen; Nicolas Wicker; Hiroshi Mamitsuka IEEE Transactions on Neural Networks and Learning Systems, 2014

Graph cut is a common way of clustering nodes on similarity graphs. As a clustering method, it does not give a unique solution under usually used loss functions. We specifically show the problem in similarity graph-based clustering setting that the resulting clusters might be even disconnected. This is counter-intuitive as one wish to have good clustering solutions in the sense ...


Interaction Techniques for Selecting and Manipulating Subgraphs in Network Visualizations

Michael J. McGuffin; Igor Jurisica IEEE Transactions on Visualization and Computer Graphics, 2009

We present a novel and extensible set of interaction techniques for manipulating visualizations of networks by selecting subgraphs and then applying various commands to modify their layout or graphical properties. Our techniques integrate traditional rectangle and lasso selection, and also support selecting a node's neighbourhood by dragging out its radius (in edges) using a novel kind of radial menu. Commands ...


Comparison of stability measures for feature selection

Peter Drotár; Zdeněk Smékal 2015 IEEE 13th International Symposium on Applied Machine Intelligence and Informatics (SAMI), 2015

The feature selection is inevitable part of machine learning techniques in biomedical engineering and bioinformatics. Feature selection methods are used to select the most discriminative features, e.g. for disease classification. Even if there are plenty of feature selection methods the stability of these algorithms is still open question. Another issue with assessing the stability of feature selection is that there ...


More eLearning Resources

IEEE-USA E-Books

  • References

    An unprecedented wealth of data is being generated by genome sequencing projects and other experimental efforts to determine the structure and function of biological molecules. The demands and opportunities for interpreting these data are expanding rapidly. Bioinformatics is the development and application of computer methods for management, analysis, interpretation, and prediction, as well as for the design of experiments. Machine learning approaches (e.g., neural networks, hidden Markov models, and belief networks) are ideally suited for areas where there is a lot of data but little theory, which is the situation in molecular biology. The goal in machine learning is to extract useful information from a body of data by building good probabilistic models--and to automate the process as much as possible.In this book Pierre Baldi and Sÿren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed both at biologists and biochemists who need to understand new data-driven algorithms and at those with a primary background in physics, mathematics, statistics, or computer science who need to know more about applications in molecular biology.This new second edition contains expanded coverage of probabilistic graphical models and of the applications of neural networks, as well as a new chapter on microarrays and gene expression. The entire text has been extensively revised.

  • Index

    An unprecedented wealth of data is being generated by genome sequencing projects and other experimental efforts to determine the structure and function of biological molecules. The demands and opportunities for interpreting these data are expanding rapidly. Bioinformatics is the development and application of computer methods for management, analysis, interpretation, and prediction, as well as for the design of experiments. Machine learning approaches (e.g., neural networks, hidden Markov models, and belief networks) are ideally suited for areas where there is a lot of data but little theory, which is the situation in molecular biology. The goal in machine learning is to extract useful information from a body of data by building good probabilistic models--and to automate the process as much as possible.In this book Pierre Baldi and Sÿren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed both at biologists and biochemists who need to understand new data-driven algorithms and at those with a primary background in physics, mathematics, statistics, or computer science who need to know more about applications in molecular biology.This new second edition contains expanded coverage of probabilistic graphical models and of the applications of neural networks, as well as a new chapter on microarrays and gene expression. The entire text has been extensively revised.

  • Identifying Calcium Binding Sites in Proteins

    Identification of the calcium binding sites in proteins is one of the main barriers to understanding the role of calcium in biological systems. Numerous efforts have been devoted to predicting and visualizing calcium binding sites in proteins with high accuracy and speed. Several methods, such as FEATURE, protein seqFEATURE, or MetSite, used statistical approaches for recognizing calcium binding sites based on a variety of physical and chemical features in calcium binding sites and nonsites. To overcome the disadvantages of the above mentioned approaches, this chapter illustrates three methods based on the geometric and other key features of calcium binding sites: GG, GG2.0, and GSVM. It explains datasets, performance measurement, and detailed implementation of these three methods. Experimental results and discussion are finally presented in the chapter.

  • New Trends in DataMining Technology

    Discover the next generation of data-mining tools and technologyThis book brings together an international team of eighty experts to present readers with the next generation of data-mining applications. Unlike other publications that take a strictly academic and theoretical approach, this book features authors who have successfully developed data-mining solutions for a variety of customer types. Presenting their state-of-the-art methodologies and techniques, the authors show readers how they can analyze enormous quantities of data and make new discoveries by connecting key pieces of data that may be spread across several different databases and file servers. The latest data- mining techniques that will revolutionize research across a wide variety of fields including business, science, healthcare, and industry are all presented. Organized by application, the twenty-five chapters cover applications in: Industry and business Science and engineering Bioinformatics and biotechnology Medicine and pharmaceuticals Web and text-mining Security New trends in data-mining technology And much more . . .Readers from a variety of disciplines will learn how the next generation of data-mining applications can radically enhance their ability to analyze data and open the doors to new opportunities. Readers will discover: New data-mining tools to automate the evaluation and qualification of sales opportunities The latest tools needed for gene mapping and proteomic data analysis Sophisticated techniques that can be engaged in crime fighting and preventionWith its coverage of the most advanced applications, Next Generation of Data-Mining Applications is essential reading for all researchers working in data mining or who are tasked with making se nse of an ever-growing quantity of data. The publication also serves as an excellent textbook for upper-level undergraduate and graduate courses in computer science, information management, and statistics.

  • Review of Imbalanced Data Learning for Protein Methylation Prediction

    In this chapter, the authors focus on the study of computational predictions on this particular PTM-protein arginine methylation. They provide a comprehensive review of the study of arginine methylation prediction. They extensively investigate all existing methylation prediction methods and servers; thoroughly review all feature extracting schemes used for sequence encoding; and carefully summarize and compare all processing steps in their methodologies, including data collection, feature extraction and selection, classifier training and evaluation, and result discussion. Finally, the authors suggest several future directions that are worthy of continuous research on methylation predictions.

  • The Challenges Facing Genomic Informatics

    Computational molecular biology, or bioinformatics, draws on the disciplines of biology, mathematics, statistics, physics, chemistry, computer science, and engineering. It provides the computational support for functional genomics, which links the behavior of cells, organisms, and populations to the information encoded in the genomes, as well as for structural genomics. At the heart of all large-scale and high-throughput biotechnologies, it has a growing impact on health and medicine.This survey of computational molecular biology covers traditional topics such as protein structure modeling and sequence alignment, and more recent ones such as expression data analysis and comparative genomics. It combines algorithmic, statistical, database, and AI- based methods for studying biological problems. The book also contains an introductory chapter, as well as one on general statistical modeling and computational techniques in molecular biology. Each chapter presents a self- contained review of a specific subject.Not for sale in China, including Hong Kong

  • Low-Density Separation

    In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This first comprehensive overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research.Semi- Supervised Learning first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low- density separation assumption, graph-based methods, and algorithms that perform two-step learning. The book then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of extensive benchmark experiments. Finally, the book looks at interesting directions for SSL research. The book closes with a discussion of the relationship between semi-supervised learning and transduction.Olivier Chapelle and Alexander Zien are Research Scientists and Bernhard Schölkopf is Professor and Director at the Max Planck Institute for Biological Cybernetics in Tÿbingen. Schölkopf is coauthor of Learning with Kernels (MIT Press, 2002) and is a coeditor of Advances in Kernel Methods: Support Vector Learning (1998), Advances in Large- Margin Classifiers (2000), and Kernel Methods in Computational B iology (2004), all published by The MIT Press.</P

  • Analysis and Prediction of Protein Posttranslational Modification Sites

    Posttranslational modification (PTM) plays key roles in many cellular processes such as signaling, cellular differentiation, protein degradation, protein stability, gene expression regulation, protein function regulation, and protein interactions. It has been estimated that there are more than 200 types of PTMs mediated by enzymes consisting of 5% of the proteome. PTM site prediction is a binary classification problem. Musite framework was designed for PTM site prediction in three processes: (i) data collection and preparation, (ii) feature extraction, and (iii) classifier/prediction model training and evaluation. With its GNU GPL open-source license and extensible API, Musite provides an open framework for PTM site prediction applications. The Musite framework contains six core modules. The authors provide standalone and web server versions of Musite, both with intuitive graphical user interface (GUI) to access the prediction models that they trained.

  • Selection of Discriminative Genes from Microarray Data

    This chapter establishes the effectiveness of the fuzzy equivalence partition matrix (FEPM) for the problem of gene selection from microarray data and compares its performance with some existing methods on a set of microarray gene expression data sets. It first briefly introduces various evaluation criteria used for computing both the relevance and redundancy of the genes. To measure both gene-class relevance and gene-gene redundancy using information theoretic measures such as entropy, mutual information, and f-information measures, the true density functions of continuous-valued genes have to be approximated. The chapter presents several approaches to approximate the true probability density function for continuous-valued gene expression data. It then describes the problem of gene selection from microarray data sets using information theoretic approaches. Finally, the chapter reports a few case studies and a comparison among different approximation methods. fuzzy logic; numerical analysis; probability density function

  • Support Vector Machines

    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.



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 ...