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High-Performance Computing Infrastructure for South East Europe's Research Communities
This book is a collection of carefully reviewed papers presented during the HP-SEE User Forum, the meeting of the High-Performance Computing Infrastructure for South East Europe’s (HP-SEE) Research Communities, held in October 17-19, 2012, in Belgrade, Serbia. HP-SEE aims at supporting and integrating regional HPC infrastructures; implementing solutions for HPC in the region; and making HPC resources available to research communities in SEE, region, which are working in a number of scientific fields with specific needs for massively parallel execution on powerful computing resources. HP-SEE brings together research communities and HPC operators from 14 different countries and enables them to share HPC facilities, software, tools, data and research results, thus fostering collaboration and strengthening the regional and national human network; the project specifically supports research groups in the areas of computational physics, computational chemistry and the life sciences. The contributions presented in this book are organized in four main sections: computational physics; computational chemistry; the life sciences; and scientific computing and HPC operations.
Microbiome and Machine Learning, Volume II
Due to the success of Microbiome and Machine Learning, which collected research results and perspectives of researchers working in the field of machine learning (ML) applied to the analysis of microbiome data, we are launching the second volume to collate any new findings in the field to further our understanding and encourage the participation of experts worldwide in the discussion. The success of ML algorithms in the field is substantially due to their capacity to process high-dimensional data and deal with uncertainty and noise. However, to maximize the combinatory potential of these emerging fields (microbiome and ML), researchers have to deal with some aspects that are complex and inherently related to microbiome data. Microbiome data are convoluted, noisy and highly variable, and non-standard analytical methodologies are required to unlock their clinical and scientific potential. Therefore, although a wide range of statistical modelling and ML methods are available, their application is only sometimes optimal when dealing with microbiome data.
In Silico Drug Design
In Silico Drug Design: Repurposing Techniques and Methodologies explores the application of computational tools that can be utilized for this approach. The book covers theoretical background and methodologies of chem-bioinformatic techniques and network modeling and discusses the various applied strategies to systematically retrieve, integrate and analyze datasets from diverse sources. Other topics include in silico drug design methods, computational workflows for drug repurposing, and network-based in silico screening for drug efficacy. With contributions from experts in the field and the inclusion of practical case studies, this book gives scientists, researchers and R&D professionals in t...
Ranking of researchers and scientists in Greece in 2017 according to Google Scholar database
Scope: The classification of researchers and scientists in Greece in a unified list based on the citation impact and dissemination level of their scientific work according to Google Scholar database. Classification criteria: First criterion is h-index. In the case of equal h-index, the following scientometric indicators are used for the classification. The number of total citations, the i10-index, the total impact factor of scientist, the m-index or m-quotient of scientist. Information resource: The h-index, citations and i10-index derived from the public profiles of researchers in the Google Scholar database. In addition, the calculation of total impact factor and m-index of each researcher...
The Travels and Adventures of Serendipity
From the names of cruise lines and bookstores to an Australian ranch and a nudist camp outside of Atlanta, the word serendipity--that happy blend of wisdom and luck by which something is discovered not quite by accident--is today ubiquitous. This book traces the word's eventful history from its 1754 coinage into the twentieth century--chronicling along the way much of what we now call the natural and social sciences. The book charts where the term went, with whom it resided, and how it fared. We cross oceans and academic specialties and meet those people, both famous and now obscure, who have used and abused serendipity. We encounter a linguistic sage, walk down the illustrious halls of the ...
Dark Remedy
In this riveting medical detective story, Trent Stephens and Rock Brynner recount the history of thalidomide, from the epidemic of birth defects in the 1960's to the present day, as scientists work to create and test an alternative drug that captures thalidomide's curative properties without its cruel side effects. A parable about compassion-and the absence of it-Dark Remedy is a gripping account of thalidomide's extraordinary impact on the lives of individuals and nations over half a century.
Next Generation Sequencing
Next generation sequencing (NGS) has surpassed the traditional Sanger sequencing method to become the main choice for large-scale, genome-wide sequencing studies with ultra-high-throughput production and a huge reduction in costs. The NGS technologies have had enormous impact on the studies of structural and functional genomics in all the life sciences. In this book, Next Generation Sequencing Advances, Applications and Challenges, the sixteen chapters written by experts cover various aspects of NGS including genomics, transcriptomics and methylomics, the sequencing platforms, and the bioinformatics challenges in processing and analysing huge amounts of sequencing data. Following an overview of the evolution of NGS in the brave new world of omics, the book examines the advances and challenges of NGS applications in basic and applied research on microorganisms, agricultural plants and humans. This book is of value to all who are interested in DNA sequencing and bioinformatics across all fields of the life sciences.
Kernel-based Data Fusion for Machine Learning
Data fusion problems arise frequently in many different fields. This book provides a specific introduction to data fusion problems using support vector machines. In the first part, this book begins with a brief survey of additive models and Rayleigh quotient objectives in machine learning, and then introduces kernel fusion as the additive expansion of support vector machines in the dual problem. The second part presents several novel kernel fusion algorithms and some real applications in supervised and unsupervised learning. The last part of the book substantiates the value of the proposed theories and algorithms in MerKator, an open software to identify disease relevant genes based on the integration of heterogeneous genomic data sources in multiple species. The topics presented in this book are meant for researchers or students who use support vector machines. Several topics addressed in the book may also be interesting to computational biologists who want to tackle data fusion challenges in real applications. The background required of the reader is a good knowledge of data mining, machine learning and linear algebra.
