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    Fassetti, Fabio.
    Discriminative Pattern Discovery on Biological Networks [Electronic resource] / Fabio. Fassetti, Rombo, Simona E., Serrao, Cristina. ; . - 1st ed. 2017. - [S. l. : s. n.]. - X, 45 p. 4 illus. - ISBN 9783319634777
    Зміст:

Рубрики: Bioinformatics.
   Pattern recognition.
   Data mining.
   Gene expression.
   Computational Biology/Bioinformatics.
   Pattern Recognition.
   Data Mining and Knowledge Discovery.
   Bioinformatics.
   Gene Expression.
Анотація: This work provides a review of biological networks as a model for analysis, presenting and discussing a number of illuminating analyses. Biological networks are an effective model for providing insights about biological mechanisms. Networks with different characteristics are employed for representing different scenarios. This powerful model allows analysts to perform many kinds of analyses which can be mined to provide interesting information about underlying biological behaviors. The text also covers techniques for discovering exceptional patterns, such as a pattern accounting for local similarities and also collaborative effects involving interactions between multiple actors (for example genes). Among these exceptional patterns, of particular interest are discriminative patterns, namely those which are able to discriminate between two input populations (for example healthy/unhealthy samples). In addition, the work includes a discussion on the most recent proposal on discovering discriminative patterns, in which there is a labeled network for each sample, resulting in a database of networks representing a sample set. This enables the analyst to achieve a much finer analysis than with traditional techniques, which are only able to consider an aggregated network of each population.
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Rombo, Simona E.; Serrao, Cristina.; Fassetti, Fabio. \.\
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    3rd International Winter School and Conference on Network Science [Electronic resource] : netSci-X 2017 / / ed.: Shmueli, Erez., Barzel, Baruch., Puzis, Rami. - 1st ed. 2017. - [S. l. : s. n.]. - VI, 130 p. 32 illus., 17 illus. in color. - ISBN 9783319554716
    Зміст:

Рубрики: Physics.
   Computer simulation.
   Social sciences—Data processing.
   Social sciences—Computer programs.
   Sociophysics.
   Econophysics.
   Bioinformatics.
   Applications of Graph Theory and Complex Networks.
   Simulation and Modeling.
   Computational Social Sciences.
   Data-driven Science, Modeling and Theory Building.
   Computational Biology/Bioinformatics.
Анотація: This book contains original research chapters related to the interdisciplinary field of complex networks spanning biological and environmental networks, social, technological, and economic networks. Many natural phenomena can be modeled as networks where nodes are the primitive compounds and links represent their interactions, similarities, or distances of sorts. Complex networks have an enormous impact on research in various fields like biology, social sciences, engineering, and cyber-security to name a few. The topology of a network often encompasses important information on the functionality and dynamics of the system or the phenomenon it represents. Network science is an emerging interdisciplinary discipline that provides tools and insights to researchers in a variety of domains. NetSci-X is the central winter conference within the field and brings together leading researchers and innovators to connect, meet, and establish interdisciplinary channels for collaboration. It is the largest and best known event in the area of network science. This text demonstrates how ideas formulated by authors with different backgrounds are transformed into models, methods, and algorithms that are used to study complex systems across different domains and will appeal to researchers and students within in the field. .
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Shmueli, Erez. \ed.\; Barzel, Baruch. \ed.\; Puzis, Rami. \ed.\
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    Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support [Electronic resource] : third International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 14, Proceedings / / ed. Cardoso, M. Jorge. [et al.]. - 1st ed. 2017. - [S. l. : s. n.]. - XIX, 385 p. 169 illus. - ISBN 9783319675589
Рубрики: Optical data processing.
   Artificial intelligence.
   Health informatics.
   Bioinformatics.
   Logic design.
   Image Processing and Computer Vision.
   Artificial Intelligence.
   Health Informatics.
   Computational Biology/Bioinformatics.
   Logic Design.
Анотація: This book constitutes the refereed joint proceedings of the Third International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2017, and the 6th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2017, held in conjunction with the 20th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2017, in Québec City, QC, Canada, in September 2017. The 38 full papers presented at DLMIA 2017 and the 5 full papers presented at ML-CDS 2017 were carefully reviewed and selected. The DLMIA papers focus on the design and use of deep learning methods in medical imaging. The ML-CDS papers discuss new techniques of multimodal mining/retrieval and their use in clinical decision support.
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Cardoso, M. Jorge. \ed.\; Arbel, Tal. \ed.\; Carneiro, Gustavo. \ed.\; Syeda-Mahmood, Tanveer. \ed.\; Tavares, João Manuel R.S. \ed.\; Moradi, Mehdi. \ed.\; Bradley, Andrew. \ed.\; Greenspan, Hayit. \ed.\; Papa, João Paulo. \ed.\; Madabhushi, Anant. \ed.\; Nascimento, Jacinto C. \ed.\; Cardoso, Jaime S. \ed.\; Belagiannis, Vasileios. \ed.\; Lu, Zhi. \ed.\
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    Bioinformatics and Biomedical Engineering [Electronic resource] : 5th International Work-Conference, IWBBIO 2017, Granada, Spain, April 26–28, 2017, Proceedings, Part II / / ed.: Rojas, Ignacio., Ortuño, Francisco. - 1st ed. 2017. - [S. l. : s. n.]. - XXXVI, 741 p. 275 illus. - ISBN 9783319561547
    Зміст:

Рубрики: Bioinformatics.
   Health informatics.
   Data mining.
   Optical data processing.
   Computer graphics.
   Computer organization.
   Computational Biology/Bioinformatics.
   Health Informatics.
   Data Mining and Knowledge Discovery.
   Image Processing and Computer Vision.
   Computer Graphics.
   Computer Systems Organization and Communication Networks.
Анотація: This two volume set LNBI 10208 and LNBI 10209 constitutes the proceedings of the 5th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2017, held in Granada, Spain, in April 2017. The 122 papers presented were carefully reviewed and selected from 309 submissions. The scope of the conference spans the following areas: advances in computational intelligence for critical care; bioinformatics for healthcare and diseases; biomedical engineering; biomedical image analysis; biomedical signal analysis; biomedicine; challenges representing large-scale biological data; computational genomics; computational proteomics; computational systems for modeling biological processes; data driven biology - new tools, techniques and resources; eHealth; high-throughput bioinformatic tools for genomics; oncological big data and new mathematical tools; smart sensor and sensor-network architectures; time lapse experiments and multivariate biostatistics.
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Rojas, Ignacio. \ed.\; Ortuño, Francisco. \ed.\
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    11th International Conference on Practical Applications of Computational Biology & Bioinformatics [Electronic resource] / ed. Fdez-Riverola, Florentino. [et al.]. - 1st ed. 2017. - [S. l. : s. n.]. - XIV, 330 p. 97 illus. - ISBN 9783319608167
    Зміст:

Рубрики: Computational intelligence.
   Artificial intelligence.
   Bioinformatics.
   Computational Intelligence.
   Artificial Intelligence.
   Computational Biology/Bioinformatics.
Анотація: Biological and biomedical research are increasingly driven by experimental techniques that challenge our ability to analyse, process and extract meaningful knowledge from the underlying data. The impressive capabilities of next-generation sequencing technologies, together with novel and constantly evolving, distinct types of omics data technologies, have created an increasingly complex set of challenges for the growing fields of Bioinformatics and Computational Biology. The analysis of the datasets produced and their integration call for new algorithms and approaches from fields such as Databases, Statistics, Data Mining, Machine Learning, Optimization, Computer Science and Artificial Intelligence. Clearly, Biology is more and more a science of information and requires tools from the computational sciences. In the last few years, we have seen the rise of a new generation of interdisciplinary scientists with a strong background in the biological and computational sciences. In this context, the interaction of researchers from different scientific fields is, more than ever, of foremost importance in boosting the research efforts in the field and contributing to the education of a new generation of Bioinformatics scientists. The PACBB’17 conference was intended to contribute to this effort and promote this fruitful interaction, with a technical program that included 39 papers spanning many different sub-fields in Bioinformatics and Computational Biology. Further, the conference promoted the interaction of scientists from diverse research groups and with a distinct background (computer scientists, mathematicians, biologists).
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Fdez-Riverola, Florentino. \ed.\; Mohamad, Mohd Saberi. \ed.\; Rocha, Miguel. \ed.\; De Paz, Juan F. \ed.\; Pinto, Tiago. \ed.\
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    Intelligent Computing Theories and Application [Electronic resource] : 13th International Conference, ICIC 2017, Liverpool, UK, August 7-10, 2017, Proceedings, Part II / / ed.: Huang, De-Shuang., Jo, Kang-Hyun., Figueroa-García, Juan Carlos. - 1st ed. 2017. - [S. l. : s. n.]. - XXIII, 841 p. 274 illus. - ISBN 9783319633121
    Зміст:

Рубрики: Application software.
   Optical data processing.
   Pattern recognition.
   Bioinformatics.
   Artificial intelligence.
   Computers.
   Information Systems Applications (incl. Internet).
   Image Processing and Computer Vision.
   Pattern Recognition.
   Computational Biology/Bioinformatics.
   Artificial Intelligence.
   Computation by Abstract Devices.
Анотація: This three-volume set LNCS 10361, LNCS 10362, and LNAI 10363 constitutes the refereed proceedings of the 13th International Conference on Intelligent Computing, ICIC 2017, held in Liverpool, UK, in August 2017. The 221 full papers and 15 short papers of the three proceedings volumes were carefully reviewed and selected from 639 submissions. This second volume of the set comprises 74 papers. The papers are organized in topical sections such as Pattern Recognition; Image Processing; Virtual Reality and Human-Computer Interaction; Healthcare Informatics Theory and Methods; Genetic Algorithms; Blind Source Separation; Intelligent Fault Diagnosis; Machine Learning; Knowledge Discovery and Data Mining; Gene Expression Array Analysis; Systems Biology; Modeling, Simulation, and Optimization of Biological Systems; Intelligent Computing in Computational Biology; Computational Genomics; Computational Proteomics; Gene Regulation Modeling and Analysis; SNPs and Haplotype Analysis; Protein-Protein Interaction Prediction; Protein Structure and Function Prediction; Next-Gen Sequencing and Metagenomics; Structure Prediction and Folding; Biomarker Discovery; Applications of Machine Learning Techniques to Computational Proteomics, Genomics, and Biological Sequence Analysis; Biomedical Image Analysis; Human-Machine Interaction: Shaping Tools Which Will Shape Us; Protein and Gene Bioinformatics: Analysis, Algorithms and Applications; Special Session on Computer Vision based Navigation; Neural Networks: Theory and Application.
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Huang, De-Shuang. \ed.\; Jo, Kang-Hyun. \ed.\; Figueroa-García, Juan Carlos. \ed.\
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    Advanced Computational Methods in Life System Modeling and Simulation [Electronic resource] : international Conference on Life System Modeling and Simulation, LSMS 2017 and International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2017, Nanjing, China, September 22-24, 2017, Proceedings, Part I / / ed. Fei, Minrui. [et al.]. - 1st ed. 2017. - [S. l. : s. n.]. - XVIII, 609 p. 348 illus. - ISBN 9789811063701
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Рубрики: Computer simulation.
   Bioinformatics.
   Health informatics.
   Simulation and Modeling.
   Computational Biology/Bioinformatics.
   Health Informatics.
Анотація: The three-volume set CCIS 761, CCIS 762, and CCIS 763 constitutes the thoroughly refereed proceedings of the International Conference on Life System Modeling and Simulation, LSMS 2017, and of the International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2017, held in Nanjing, China, in September 2017. The 208 revised full papers presented were carefully reviewed and selected from over 625 submissions. The papers of this volume are organized in topical sections on: Biomedical Signal Processing; Computational Methods in Organism Modeling; Medical Apparatus and Clinical Applications; Bionics Control Methods, Algorithms and Apparatus; Modeling and Simulation of Life Systems; Data Driven Analysis; Image and Video Processing; Advanced Fuzzy and Neural Network Theory and Algorithms; Advanced Evolutionary Methods and Applications; Advanced Machine Learning Methods and Applications; Intelligent Modeling, Monitoring, and Control of Complex Nonlinear Systems; Advanced Methods for Networked Systems; Control and Analysis of Transportation Systems; Advanced Sliding Mode Control and Applications; Advanced Analysis of New Materials and Devices; Computational Intelligence in Utilization of Clean and Renewable Energy Resources; Intelligent Methods for Energy Saving and Pollution Reduction; Intelligent Methods in Developing Electric Vehicles, Engines and Equipment; Intelligent Computing and Control in Power Systems; Modeling, Simulation and Control in Smart Grid and Microgrid; Optimization Methods; Computational Methods for Sustainable Environment.
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Дод.точки доступу:
Fei, Minrui. \ed.\; Ma, Shiwei. \ed.\; Li, Xin. \ed.\; Sun, Xin. \ed.\; Jia, Li. \ed.\; Su, Zhou. \ed.\
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    Heat Shock Proteins in Veterinary Medicine and Sciences [Electronic resource] : published under the Sponsorship of the Association for Institutional Research (AIR) and the Association for the Study of Higher Education (ASHE) / / ed.: Asea, Alexzander A. A., Kaur, Punit. - 1st ed. 2017. - [S. l. : s. n.]. - IX, 398 p. 21 illus., 12 illus. in color. - ISBN 9783319733777
    Зміст:

Рубрики: Cell biology.
   Bioinformatics.
   Biophysics.
   Biological physics.
   Biology—Technique.
   Cell Biology.
   Computational Biology/Bioinformatics.
   Biological and Medical Physics, Biophysics.
   Biological Techniques.
Анотація: The book Heat Shock Proteins in Veterinary Medicine and Sciences provides the most comprehensive review on contemporary knowledge on the role of heat shock proteins in veterinary medicine and sciences. Using an integrative approach to understanding heat shock protein physiology, the contributors provide a synopsis of novel mechanisms by which HSP are involved in the regulation of normal physiological and pathophysiological conditions. Key basic and clinical research laboratories from major universities and veterinary hospitals around the world contribute chapters that review present research activity and importantly project the field into the future. The book is a must read for veterinary doctors, researchers, postdoctoral fellows and graduate students in the fields of Veterinary Medicine, Animal Physiology, Animal Husbandry, Biotechnology, Molecular Medicine, Microbiology and Pathology.
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Asea, Alexzander A. A. \ed.\; Kaur, Punit. \ed.\
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    Information Technology in Bio- and Medical Informatics [Electronic resource] : 8th International Conference, ITBAM 2017, Lyon, France, August 28–31, 2017, Proceedings / / ed. Bursa, Miroslav. [et al.]. - 1st ed. 2017. - [S. l. : s. n.]. - X, 135 p. 37 illus. - ISBN 9783319642659
Рубрики: Data mining.
   Information storage and retrieval.
   Artificial intelligence.
   Bioinformatics.
   Application software.
   Computers.
   Data Mining and Knowledge Discovery.
   Information Storage and Retrieval.
   Artificial Intelligence.
   Computational Biology/Bioinformatics.
   Computer Appl. in Social and Behavioral Sciences.
   Computing Milieux.
Анотація: This book constitutes the refereed proceedings of the 8th International Conference on Information Technology in Bio- and Medical Informatics, ITBAM 2017, held in Lyon, France, in August 2017. The 3 revised full papers and 6 poster papers presented were carefully reviewed and selected from 15 submissions. The papers address a broad range of topics in applications of information technology to biomedical engineering and medical informatics.
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Bursa, Miroslav. \ed.\; Holzinger, Andreas. \ed.\; Renda, M. Elena. \ed.\; Khuri, Sami. \ed.\
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10.

    Mahjoubfar, Ata.
    Artificial Intelligence in Label-free Microscopy [Electronic resource] : biological Cell Classification by Time Stretch / / Ata. Mahjoubfar, Chen, Claire Lifan., Jalali, Bahram. ; . - 1st ed. 2017. - [S. l. : s. n.]. - XXXIII, 134 p. 52 illus. in color. - ISBN 9783319514482
    Зміст:

Рубрики: Biomedical engineering.
   Electronics.
   Microelectronics.
   Optical data processing.
   Bioinformatics.
   Biomedical Engineering and Bioengineering.
   Electronics and Microelectronics, Instrumentation.
   Image Processing and Computer Vision.
   Bioinformatics.
Анотація: This book introduces time-stretch quantitative phase imaging (TS-QPI), a high-throughput label-free imaging flow cytometer developed for big data acquisition and analysis in phenotypic screening. TS-QPI is able to capture quantitative optical phase and intensity images simultaneously, enabling high-content cell analysis, cancer diagnostics, personalized genomics, and drug development. The authors also demonstrate a complete machine learning pipeline that performs optical phase measurement, image processing, feature extraction, and classification, enabling high-throughput quantitative imaging that achieves record high accuracy in label -free cellular phenotypic screening and opens up a new path to data-driven diagnosis. • Demonstrates how machine learning is used in high-speed microscopy imaging to facilitate medical diagnosis; • Provides a systematic and comprehensive illustration of time stretch technology; • Enables multidisciplinary application, including industrial, biomedical, and artificial intelligence.
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Chen, Claire Lifan.; Jalali, Bahram.; Mahjoubfar, Ata. \.\
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11.

    GeNeDis 2016 [Electronic resource] : geriatrics / / ed. Vlamos, Panayiotis. - 1st ed. 2017. - [S. l. : s. n.]. - VII, 300 p. 50 illus., 36 illus. in color. - ISBN 9783319573489
    Зміст:

Mobile applications improve quality of life on citizens with disorientation: The 'NeverLost App' paradigm.- Performance Management in Healthcare Organizations: Concept and Practicum.- Musculoskeletal problems among Greek perioperative nurses in regional hospitals in southern Peloponnese.- Exploring the notion of context in medical data.- vhMentor: An ontology supported mobile agent system for pervasive health care monitoring -- Internet Addiction of Young Greek Adults: Psychological Aspects and Information Privacy.- A DICOM based collaborative platform for real-time medical tele-consultation on medical images.- Exploring amyloidogenicity of Clusterin: a structural and bioinformatics analysis.- Exploring amyloidogenicity of Clusterin: a structural and bioinformatics analysis.- Applications for Electrical Impedance Tomography (EIT) and Electrical Properties of the Human Body.- Application of Theranostics in Oncology.- Sport Participation and Ageing: Evidence from a marathon event.- IoT Applications with 5G connectivity in Medical Tourism Sector Management: Third-party Service Scenarios.- Recognizing Emotional States using Speech Information.- Real Time 3D Representation and Tracking of Guidewire for Image Guided Cardiovascular Interventions.- Utilizing a Homecare Platform for Remote Monitoring of Patients with Idiopathic Pulmonary Fibrosis.- IoT Contextual Factors on Health Care.- Methods and Patterns for User-friendly Quantum Programming.- Bullying in Virtual Learning Communities.- Programmatic Assessment of Professionalism in Psychiatry Education: A Literature Review and Implementation Guide.- Opinion Mining for Educational Video Lectures.- Cognitive enhancement using ICT and its ethical implications.- Smart Health Caring Home: a systematic review of smart home care for elders and chronic disease patients.- SSEv: A new small samples evaluator, based on modified survival curves and its web-based application.- Efficient Health Information Management based on Patient-Generated Digital Data -- Quality of life in elderly bladder cancer patients following a cystectomy.- Long-term survival of women with breast cancer. Overview supportive care needs assessment instruments -- Safe use of defibrillators: a case study in greek registered nurses -- Quality of life in elderly cancer patients undergoing chemotherapy.

Рубрики: Neurosciences.
   Bioinformatics.
   Neurosciences.
   Bioinformatics.
Анотація: The 2nd World Congress on Genetics, Geriatrics and Neurodegenerative Disease Research (GeNeDis 2016), will focus on recent advances in geriatrics and neurodegeneration, ranging from basic science to clinical and pharmaceutical developments and will provide an international focum for the latest scientific discoveries, medical practices, and care initiatives. Advances information technologies will be discussed along with their implications for various research, implementation, and policy concerns. In addition, the conference will address European and global issues in the funding of long-term care and medico-social policies regarding elderly people. GeNeDis 2016 takes place in Sparta, Greece, 20-23 October, 2016. This volume focuses on the sessions that address geriatrics.
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Vlamos, Panayiotis. \ed.\
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12.

    Lavor, Carlile.
    An Introduction to Distance Geometry applied to Molecular Geometry [Electronic resource] / Carlile. Lavor, Liberti, Leo., A. Lodwick, Weldon., Mendonça da Costa, Tiago. ; . - 1st ed. 2017. - [S. l. : s. n.]. - IX, 54 p. 27 illus. in color. - ISBN 9783319571836
    Зміст:

Рубрики: Application software.
   Bioinformatics.
   Applied mathematics.
   Engineering mathematics.
   Computer science—Mathematics.
   Computer Applications.
   Bioinformatics.
   Applications of Mathematics.
   Math Applications in Computer Science.
Анотація: This book is a pedagogical presentation aimed at advanced undergraduate students, beginning graduate students and professionals who are looking for an introductory text to the field of Distance Geometry, and some of its applications. This versions profits from feedback acquired at undergraduate/graduate courses in seminars and a number of workshops. .
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Liberti, Leo.; A. Lodwick, Weldon.; Mendonça da Costa, Tiago.; Lavor, Carlile. \.\
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13.

    Bezerianos, Anastasios.
    Computational Methods for Processing and Analysis of Biological Pathways [Electronic resource] / Anastasios. Bezerianos, Dragomir, Andrei., Balomenos, Panos. ; . - 1st ed. 2017. - [S. l. : s. n.]. - VIII, 87 p. 11 illus. in color. - ISBN 9783319538686
    Зміст:

Рубрики: Bioinformatics.
   Systems biology.
   Computational Biology/Bioinformatics.
   Systems Biology.
Анотація: This work offers a guided walkthrough of one of the most promising research areas in modern life sciences, enabling a deeper understanding of involved concepts and methodologies via an interdisciplinary view, focusing on both well-established approaches and cutting-edge research. Highlighting what pathway analysis can offer to both the experimentalist and the modeler, the text opens with an introduction to a general methodology that outlines common workflows shared by several methods. This is followed by a review of pathway and sub-pathway based approaches for systems pharmacology. The work then presents an overview of pathway analysis methods developed to model the temporal aspects of drug- or disease-induced perturbations and extract relevant dynamic themes. The text concludes by discussing several state-of-the-art methods in pathway analysis, which address the important problem of identifying differentially expressed pathways and sub-pathways.
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Dragomir, Andrei.; Balomenos, Panos.; Bezerianos, Anastasios. \.\
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14.

    Statistical Analysis of Proteomics, Metabolomics, and Lipidomics Data Using Mass Spectrometry [Electronic resource] / ed.: Datta, Susmita., Mertens, Bart J. A. - 1st ed. 2017. - [S. l. : s. n.]. - VIII, 295 p. 106 illus., 83 illus. in color. - ISBN 9783319458090
    Зміст:

Рубрики: Statistics .
   Biostatistics.
   Metabolism.
   Bioinformatics.
   Analytical chemistry.
   Mathematical statistics.
   Statistics for Life Sciences, Medicine, Health Sciences.
   Biostatistics.
   Metabolomics.
   Computational Biology/Bioinformatics.
   Analytical Chemistry.
   Probability and Statistics in Computer Science.
Анотація: This book presents an overview of computational and statistical design and analysis of mass spectrometry-based proteomics, metabolomics, and lipidomics data. This contributed volume provides an introduction to the special aspects of statistical design and analysis with mass spectrometry data for the new omic sciences. The text discusses common aspects of design and analysis between and across all (or most) forms of mass spectrometry, while also providing special examples of application with the most common forms of mass spectrometry. Also covered are applications of computational mass spectrometry not only in clinical study but also in the interpretation of omics data in plant biology studies. Omics research fields are expected to revolutionize biomolecular research by the ability to simultaneously profile many compounds within either patient blood, urine, tissue, or other biological samples. Mass spectrometry is one of the key analytical techniques used in these new omic sciences. Liquid chromatography mass spectrometry, time-of-flight data, and Fourier transform mass spectrometry are but a selection of the measurement platforms available to the modern analyst. Thus in practical proteomics or metabolomics, researchers will not only be confronted with new high dimensional data types—as opposed to the familiar data structures in more classical genomics—but also with great variation between distinct types of mass spectral measurements derived from different platforms, which may complicate analyses, comparison, and interpretation of results. Susmita Datta received her PhD in statistics from the University of Georgia. She is a tenured professor in the Department of Biostatistics at the University of Florida. Before joining the University of Florida she was a professor and a distinguished university scholar at the University of Louisville. She is a Fellow of the American Association for the Advancement of Science, American Statistical Association, and an elected member of the International Statistical Institute. She is past president of the Caucus for Women in Statistics, and she actively supports research and education for women in STEM fields. Bart Mertens received his PhD in statistical sciences from University College London, Department of Statistical Sciences, on statistical analysis methods for spectrometry data. He is currently Associate Professor at the Department of Medical Statistics and Bioinformatics of the Leiden University Medical Centre, where he has been working in both research and consulting for statistical analysis methodology with mass spectrometry proteomic data for more than 10 years.
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Datta, Susmita. \ed.\; Mertens, Bart J. A. \ed.\
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15.

    Bioinformatics and Biomedical Engineering [Electronic resource] : 5th International Work-Conference, IWBBIO 2017, Granada, Spain, April 26–28, 2017, Proceedings, Part I / / ed.: Rojas, Ignacio., Ortuño, Francisco. - 1st ed. 2017. - [S. l. : s. n.]. - XXXIII, 673 p. 250 illus. - ISBN 9783319561486
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16.

    Aeroecology [Electronic resource] / ed. Chilson, Phillip B. [et al.]. - 1st ed. 2017. - [S. l. : s. n.]. - XV, 497 p. 119 illus., 95 illus. in color. - ISBN 9783319685762
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Prologue/Foreword -- Background -- Chapter 1: A tutorial treatment of the boundary layer and lower free atmosphere -- Chapter 2: A general treatment on how air is habitat to a host of diverse species -- Chapter 3: Coupling of weather and biology, including impacts of weather on biological behavior -- Chapter 4: Flight physiology (insects, birds and bats) -- Chapter 5: Navigation / Orientation (insects, birds and bats) -- Chapter 6: Fundamentals of migration -- Chapter 7: A broader treatment of entomology in the context of aeroecology along with methods of observing and furthering our understanding of insects -- Mehtods of Observation -- Chapter 8: Historical overview of methodologies, field observations, banding acoustics, other methods -- Chapter 9: Overview of the central questions related to monitoring the movements of individuals and why a deeper and more exact understanding of animal behavior at the individual level -- Chapter 10: Discussion of why thermal imaging techniques are needed and being used to track individuals and ensembles of individuals. Provide an overview of thermal imaging techniques for biological studies and how computer science is helping to visualize and model the results. Computer visualization of thermal data of bats -- Chapter 11: Transition from individual behavior to group behavior -- Chapter 12: Aeroecology and recent technological developments, ability to process, mosaick, and represent huge amounts of weather radar data in real time and create archives, resulting products represent for meteorologists and biologists, implementation of the Buler & Diehl algorithm, the prospects of Level III biological products -- Aeroecological case studies/Applications -- Chapter 13: Stop-over behavior of birds (and bats) and issues pertaining to land use -- Chapter 14: Linking population ecology to aeroecology -- Chapter 15: A general treatment of phenology and its significance -- Chapter 16: Aerosphere as a network connector -- Chapter 17: Interaction with human activities. An overview of aeroecological impacts: wind power, disease (if here then remove from chapter above and vice versa), aviation safety (aircraft / bird collisions), ecosystem services.

Рубрики: Zoology.
   Animal ecology.
   Meteorology.
   Bioinformatics.
   Geoecology.
   Environmental geology.
   Zoology.
   Animal Ecology.
   Meteorology.
   Computational Biology/Bioinformatics.
   Geoecology/Natural Processes.
Анотація: This book consists of a diverse collection of chapters that seeks to broaden our fundamental understanding of the ecological function and biological importance of the Earth’s lower atmosphere, which provides a huge living space for billions of animals moving within and across continents. Their migration, dispersal and foraging activities connect water and land habitats within and across continents. Drawing upon the wide-ranging experience of the authors, the book takes an inherently interdisciplinary approach that serves to introduce the reader to the topic of aeroecology, frame some of the basic biological questions that can be addressed within the context of aeroecology, and highlight several existing and emerging technologies that are being used to promote aeroecological studies. The book begins with several background chapters, that provide introduction into such topics as atmospheric science, the concept of the habitat, animal physiology, and methods of navigation. It then continues with a broad discussion of observational methods available to and used by aeroecologists. Finally, several targeted examples of aeroecological studies are presented. Following the development of the chapters, the reader is provided with a unifying framework for investigating how the dynamic properties of meteorological conditions at local, regional, and global scales affect the organisms that depend on the air for foraging and movement. Material presented in the book should be of interest to anyone wishing to gain a comprehensive understanding of the aerosphere itself and the myriad airborne organisms that inhabit and depend upon this environment for their existence. The material should be accessible to a diverse set of readers at all stages of training and across a range of research expertise.
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Дод.точки доступу:
Chilson, Phillip B. \ed.\; Frick, Winifred F. \ed.\; Kelly, Jeffrey F. \ed.\; Liechti, Felix. \ed.\
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17.

    Handbook of Computational Chemistry [Electronic resource] / ed. Leszczynski, Jerzy. [et al.]. - 2nd ed. 2017. - [S. l. : s. n.]. - 588 illus., 315 illus. in color. eReference. - ISBN 9783319272825
    Зміст:

Рубрики: Chemistry, Physical and theoretical.
   Bioinformatics.
   Nanotechnology.
   Mathematical physics.
   Bioinformatics .
   Computational biology .
   Theoretical and Computational Chemistry.
   Computational Biology/Bioinformatics.
   Nanotechnology.
   Mathematical Applications in the Physical Sciences.
   Computer Appl. in Life Sciences.
Анотація: The first part briefly describes different methods used in computational chemistry without going into exhaustive details of theory. Basic assumptions common to the majority of computational methods based on either quantum or statistical mechanics are outlined. Particular attention is paid to the limits of their applicability. The second part consists of a series of sections exemplifying the various, most important applications of computational chemistry. Molecular structures, modeling of various properties of molecules and chemical reactions are discussed. Both ground and excited state properties are covered in the gas phase as well as in solutions. Solid state materials and nanomaterials are described in part three. Amongst the topics covered are clusters, periodic structures, and nano-systems. Special emphasis is placed on the environmental effects of nanostructures. Part four is devoted to an important class of materials – biomolecules. It focuses on interesting models for biological systems that are studied by computational chemists. RNA, DNA, and proteins are discussed in detail. Examples are given for calculations of their properties and interactions. The role of solvents in biologically significant reactions is revealed, as well as the relationship between molecular structure and function of various classes of biomolecules. Part five features new bonus material devoted to Chemoinformatics. This area is vital for many applications of computational methods. The section includes a discussion of basic ideas such as molecular structure, molecular descriptors and chemical similarity. Additionally, QSAR techniques and screening methods are covered. Also, available open source chemoinformatics software is presented and discussed.
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Дод.точки доступу:
Leszczynski, Jerzy. \ed.\; Kaczmarek-Kedziera, Anna. \ed.\; Puzyn, Tomasz. \ed.\; G. Papadopoulos, Manthos. \ed.\; Reis, Heribert. \ed.\; K. Shukla, Manoj. \ed.\
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18.

    Stübler, Sabine.
    Modelling Proteasome Dynamics in a Bayesian Framework [Electronic resource] / Sabine. Stübler ; . - 1st ed. 2017. - [S. l. : s. n.]. - XV, 96 p. 30 illus., 10 illus. in color. - ISBN 9783658201678
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Рубрики: Immunology.
   Bioinformatics.
   Cell biology.
   Immunology.
   Bioinformatics.
   Cell Biology.
Анотація: Sabine Stübler compares different proteasome isoforms and subtypes in terms of their transport and active site-related parameters applying an existing computational model. In a second step, the author extends this model to be able to describe the influence of proteasome inhibitors in in vitro experiments. The computational model, which describes the hydrolysis of short fluorogenic peptides by the 20S proteasome, is calibrated to experimental data from different proteasome isoforms using an approximate Bayesian computation approach. The dynamics of proteasome inhibitors are included into the model in order to demonstrate how to modulate the inhibitor’s transport parameters for strong or isoform-specific inhibition. Contents Structure and Function of the Proteasome Approaches to Model Proteasome Dynamics Comparison of the Dynamics of Proteasome Subtypes Inhibitor Influence on the Catalytic Subunits Inhibitor Influence on a Compartmentalised Short Fluorogenic Peptide Model Target Groups Lecturers and students of systems biology, immunology and cell biology Practitioners from the fields of systems biology, immunology and cell biology About the Author Sabine Stübler works as PhD student in the Computational Physiology Group at the Institute of Biochemistry and Biology, University of Potsdam. Her research focus currently is on developing a novel systems pharmacology model.
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Stübler, Sabine. \.\
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19.

    Big Data Factories [Electronic resource] : collaborative Approaches / / ed.: Matei, Sorin Adam., Jullien, Nicolas., Goggins, Sean P. - 1st ed. 2017. - [S. l. : s. n.]. - VI, 141 p. 18 illus., 14 illus. in color. - ISBN 9783319591865
    Зміст:

Рубрики: Data mining.
   Big data.
   Bioinformatics.
   Application software.
   Research—Moral and ethical aspects.
   Data Mining and Knowledge Discovery.
   Big Data/Analytics.
   Bioinformatics.
   Computer Appl. in Social and Behavioral Sciences.
   Research Ethics.
Анотація: The book proposes a systematic approach to big data collection, documentation and development of analytic procedures that foster collaboration on a large scale. This approach, designated as “data factoring” emphasizes the need to think of each individual dataset developed by an individual project as part of a broader data ecosystem, easily accessible and exploitable by parties not directly involved with data collection and documentation. Furthermore, data factoring uses and encourages pre-analytic operations that add value to big data sets, especially recombining and repurposing. The book proposes a research-development agenda that can undergird an ideal data factory approach. Several programmatic chapters discuss specialized issues involved in data factoring (documentation, meta-data specification, building flexible, yet comprehensive data ontologies, usability issues involved in collaborative tools, etc.). The book also presents case studies for data factoring and processing that can lead to building better scientific collaboration and data sharing strategies and tools. Finally, the book presents the teaching utility of data factoring and the ethical and privacy concerns related to it. Chapter 9 of this book is available open access under a CC BY 4.0 license at link.springer.com.
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Дод.точки доступу:
Matei, Sorin Adam. \ed.\; Jullien, Nicolas. \ed.\; Goggins, Sean P. \ed.\
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20.

    Biomarkers for Endometriosis [Electronic resource] : state of the Art / / ed. D'Hooghe, Thomas. - 1st ed. 2017. - [S. l. : s. n.]. - XII, 267 p. 8 illus., 3 illus. in color. - ISBN 9783319598567
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Рубрики: Reproductive medicine.
   Bioinformatics.
   Human genetics.
   Reproductive Medicine.
   Bioinformatics.
   Human Genetics.
Анотація: This book presents an overview of the diagnostic performance of non- or semi-invasive tests for endometriosis in peripheral blood, endometrium, saliva, peritoneal fluid and urine. The value of existing and emerging systems biology technologies for biomarker development is addressed in several chapters on genetics, microarrays, proteomics and metabolomics. Although tests with high sensitivity and acceptable specificity have been developed, sometimes validated in independent populations and seem promising, more research is needed to translate these data into clinical benefit for patients and coordinate efforts internationally to standarize analysis, reports and operating procedures. The gold standard to diagnose endometriosis is currently through laparoscopic inspection with histological confirmation, a surgical procedure with rare but significant potential risks for the patients. A non-invasive test for endometriosis would be critical for the early detection of endometriosis of symptomatic women with pelvic pain and/or subfertility with normal ultrasound. This would include nearly all cases of minimal-mild endometriosis, some cases of moderate-severe endometriosis without a clearly visible ovarian endometrioma and cases with pelvic adhesions and/or other pelvic pathology, who might benefit from surgery to improve pelvic pain and/or subfertility. Such a test would also be useful in symptomatic women with ultrasound imaging suspicious for endometriosis, since it may be difficult to differentiate an ovarian endometrioma from other ovarian cysts and since the quality of ultrasound imaging is highly variable worldwide.
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D'Hooghe, Thomas. \ed.\
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