Biological Network Analysis

Biological Network Analysis
Author: Pietro Hiram Guzzi,Swarup Roy
Publsiher: Elsevier
Total Pages: 210
Release: 2020-05-11
ISBN: 0128193514
Category: Science
Language: EN, FR, DE, ES & NL

Biological Network Analysis Book Excerpt:

Biological Network Analysis: Trends, Approaches, Graph Theory, and Algorithms considers three major biological networks, including Gene Regulatory Networks (GRN), Protein-Protein Interaction Networks (PPIN), and Human Brain Connectomes. The book's authors discuss various graph theoretic and data analytics approaches used to analyze these networks with respect to available tools, technologies, standards, algorithms and databases for generating, representing and analyzing graphical data. As a wide variety of algorithms have been developed to analyze and compare networks, this book is a timely resource. Presents recent advances in biological network analysis, combining Graph Theory, Graph Analysis, and various network models Discusses three major biological networks, including Gene Regulatory Networks (GRN), Protein-Protein Interaction Networks (PPIN) and Human Brain Connectomes Includes a discussion of various graph theoretic and data analytics approaches

Recent Advances in Biological Network Analysis

Recent Advances in Biological Network Analysis
Author: Byung-Jun Yoon,Xiaoning Qian
Publsiher: Springer Nature
Total Pages: 217
Release: 2021-01-13
ISBN: 3030571734
Category: Medical
Language: EN, FR, DE, ES & NL

Recent Advances in Biological Network Analysis Book Excerpt:

This book reviews recent advances in the emerging field of computational network biology with special emphasis on comparative network analysis and network module detection. The chapters in this volume are contributed by leading international researchers in computational network biology and offer in-depth insight on the latest techniques in network alignment, network clustering, and network module detection. Chapters discuss the advantages of the respective techniques and present the current challenges and open problems in the field. Recent Advances in Biological Network Analysis: Comparative Network Analysis and Network Module Detection will serve as a great resource for graduate students, academics, and researchers who are currently working in areas relevant to computational network biology or wish to learn more about the field. Data scientists whose work involves the analysis of graphs, networks, and other types of data with topological structure or relations can also benefit from the book's insights.

Deep Learning for Biological Network Analysis

Deep Learning for Biological Network Analysis
Author: Jianye Hao,Zhongyu Wei,Jiajie Peng,Yulan He
Publsiher: Frontiers Media SA
Total Pages: 135
Release: 2022-02-07
ISBN: 2889742946
Category: Science
Language: EN, FR, DE, ES & NL

Deep Learning for Biological Network Analysis Book Excerpt:

Analysis of Biological Networks

Analysis of Biological Networks
Author: Björn H. Junker,Falk Schreiber
Publsiher: John Wiley & Sons
Total Pages: 368
Release: 2011-09-20
ISBN: 1118209915
Category: Computers
Language: EN, FR, DE, ES & NL

Analysis of Biological Networks Book Excerpt:

An introduction to biological networks and methods for theiranalysis Analysis of Biological Networks is the first book of itskind to provide readers with a comprehensive introduction to thestructural analysis of biological networks at the interface ofbiology and computer science. The book begins with a brief overviewof biological networks and graph theory/graph algorithms and goeson to explore: global network properties, network centralities,network motifs, network clustering, Petri nets, signal transductionand gene regulation networks, protein interaction networks,metabolic networks, phylogenetic networks, ecological networks, andcorrelation networks. Analysis of Biological Networks is a self-containedintroduction to this important research topic, assumes no expertknowledge in computer science or biology, and is accessible toprofessionals and students alike. Each chapter concludes with asummary of main points and with exercises for readers to test theirunderstanding of the material presented. Additionally, an FTP sitewith links to author-provided data for the book is available fordeeper study. This book is suitable as a resource for researchers in computerscience, biology, bioinformatics, advanced biochemistry, and thelife sciences, and also serves as an ideal reference text forgraduate-level courses in bioinformatics and biologicalresearch.

Recent Advances in Biological Network Analysis

Recent Advances in Biological Network Analysis
Author: Byung-Jun Yoon,Xiaoning Qian
Publsiher: Springer
Total Pages: 217
Release: 2022-01-14
ISBN: 9783030571757
Category: Medical
Language: EN, FR, DE, ES & NL

Recent Advances in Biological Network Analysis Book Excerpt:

This book reviews recent advances in the emerging field of computational network biology with special emphasis on comparative network analysis and network module detection. The chapters in this volume are contributed by leading international researchers in computational network biology and offer in-depth insight on the latest techniques in network alignment, network clustering, and network module detection. Chapters discuss the advantages of the respective techniques and present the current challenges and open problems in the field. Recent Advances in Biological Network Analysis: Comparative Network Analysis and Network Module Detection will serve as a great resource for graduate students, academics, and researchers who are currently working in areas relevant to computational network biology or wish to learn more about the field. Data scientists whose work involves the analysis of graphs, networks, and other types of data with topological structure or relations can also benefit from the book's insights.

Statistical and Evolutionary Analysis of Biological Networks

Statistical and Evolutionary Analysis of Biological Networks
Author: Michael P. H. Stumpf
Publsiher: World Scientific
Total Pages: 170
Release: 2010
ISBN: 1848164343
Category: Bayesian statistical decision theory
Language: EN, FR, DE, ES & NL

Statistical and Evolutionary Analysis of Biological Networks Book Excerpt:

Networks provide a very useful way to describe a wide range of different data types in biology, physics and elsewhere. Apart from providing a convenient tool to visualize highly dependent data, networks allow stringent mathematical and statistical analysis. In recent years, much progress has been achieved to interpret various types of biological network data such as transcriptomic, metabolomic and protein interaction data as well as epidemiological data. Of particular interest is to understand the organization, complexity and dynamics of biological networks and how these are influenced by network evolution and functionality. This book reviews and explores statistical, mathematical and evolutionary theory and tools in the understanding of biological networks. The book is divided into comprehensive and self-contained chapters, each of which focuses on an important biological network type, explains concepts and theory and illustrates how these can be used to obtain insight into biologically relevant processes and questions. There are chapters covering metabolic, transcriptomic, protein interaction and epidemiological networks as well as chapters that deal with theoretical and conceptual material. The authors, who contribute to the book, are active, highly regarded and well-known in the network community. Sample Chapter(s). Chapter 1: A Network Analysis Primer (350 KB). Contents: A Network Analysis Primer (M P H Stumpf & C Wiuf); Evolutionary Analysis of Protein Interaction Networks (C Wiuf & O Ratmann); Motifs in Biological Networks (F Schreiber & H SchwAbbermeyer); Bayesian Analysis of Biological Networks: Clusters, Motifs, Cross-Species Correlations (J Berg & M Lnssig); Network Concepts and Epidemiological Models (R R Kao & I Z Kiss); Evolutionary Origin and Consequences of Design Properties of Metabolic Networks (T Pfeiffer & S Bonhoeffer); Protein Interactions from an Evolutionary Perspective (F Pazos & A Valencia); Statistical Null Models for Biological Network Analysis (W P Kelly et al.). Readership: Academics, researchers, postgraduates and advanced undergraduates in bioinformatics. Biologists, mathematicians/statisticians, physicists and computer scientists.

Computational Network Analysis with R

Computational Network Analysis with R
Author: Matthias Dehmer,Yongtang Shi,Frank Emmert-Streib
Publsiher: John Wiley & Sons
Total Pages: 368
Release: 2016-08-09
ISBN: 3527694374
Category: Medical
Language: EN, FR, DE, ES & NL

Computational Network Analysis with R Book Excerpt:

This new title in the well-established "Quantitative Network Biology" series includes innovative and existing methods for analyzing network data in such areas as network biology and chemoinformatics. With its easy-to-follow introduction to the theoretical background and application-oriented chapters, the book demonstrates that R is a powerful language for statistically analyzing networks and for solving such large-scale phenomena as network sampling and bootstrapping. Written by editors and authors with an excellent track record in the field, this is the ultimate reference for R in Network Analysis.

Summarizing Biological Networks

Summarizing Biological Networks
Author: Sourav S. Bhowmick,Boon-Siew Seah
Publsiher: Springer
Total Pages: 146
Release: 2017-04-17
ISBN: 331954621X
Category: Computers
Language: EN, FR, DE, ES & NL

Summarizing Biological Networks Book Excerpt:

This book focuses on the data mining, systems biology, and bioinformatics computational methods that can be used to summarize biological networks. Specifically, it discusses an array of techniques related to biological network clustering, network summarization, and differential network analysis which enable readers to uncover the functional and topological organization hidden in a large biological network. The authors also examine crucial open research problems in this arena. Academics, researchers, and advanced-level students will find this book to be a comprehensive and exceptional resource for understanding computational techniques and their applications for a summary of biological networks.

Analysis of Biological Networks

Analysis of Biological Networks
Author: Björn H. Junker,Falk Schreiber
Publsiher: Wiley-Interscience
Total Pages: 392
Release: 2008-03-14
ISBN: 0470253460
Category: Computers
Language: EN, FR, DE, ES & NL

Analysis of Biological Networks Book Excerpt:

An introduction to biological networks and methods for their analysis Analysis of Biological Networks is the first book of its kind to provide readers with a comprehensive introduction to the structural analysis of biological networks at the interface of biology and computer science. The book begins with a brief overview of biological networks and graph theory/graph algorithms and goes on to explore: global network properties, network centralities, network motifs, network clustering, Petri nets, signal transduction and gene regulation networks, protein interaction networks, metabolic networks, phylogenetic networks, ecological networks, and correlation networks. Analysis of Biological Networks is a self-contained introduction to this important research topic, assumes no expert knowledge in computer science or biology, and is accessible to professionals and students alike. Each chapter concludes with a summary of main points and with exercises for readers to test their understanding of the material presented. Additionally, an FTP site with links to author-provided data for the book is available for deeper study. This book is suitable as a resource for researchers in computer science, biology, bioinformatics, advanced biochemistry, and the life sciences, and also serves as an ideal reference text for graduate-level courses in bioinformatics and biological research.

Statistical and Machine Learning Approaches for Network Analysis

Statistical and Machine Learning Approaches for Network Analysis
Author: Matthias Dehmer,Subhash C. Basak
Publsiher: John Wiley & Sons
Total Pages: 344
Release: 2012-06-26
ISBN: 111834698X
Category: Mathematics
Language: EN, FR, DE, ES & NL

Statistical and Machine Learning Approaches for Network Analysis Book Excerpt:

Explore the multidisciplinary nature of complex networks through machine learning techniques Statistical and Machine Learning Approaches for Network Analysis provides an accessible framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph measures for classification. By providing different approaches based on experimental data, the book uniquely sets itself apart from the current literature by exploring the application of machine learning techniques to various types of complex networks. Comprised of chapters written by internationally renowned researchers in the field of interdisciplinary network theory, the book presents current and classical methods to analyze networks statistically. Methods from machine learning, data mining, and information theory are strongly emphasized throughout. Real data sets are used to showcase the discussed methods and topics, which include: A survey of computational approaches to reconstruct and partition biological networks An introduction to complex networks—measures, statistical properties, and models Modeling for evolving biological networks The structure of an evolving random bipartite graph Density-based enumeration in structured data Hyponym extraction employing a weighted graph kernel Statistical and Machine Learning Approaches for Network Analysis is an excellent supplemental text for graduate-level, cross-disciplinary courses in applied discrete mathematics, bioinformatics, pattern recognition, and computer science. The book is also a valuable reference for researchers and practitioners in the fields of applied discrete mathematics, machine learning, data mining, and biostatistics.

Systems Biology for Signaling Networks

Systems Biology for Signaling Networks
Author: Sangdun Choi
Publsiher: Springer Science & Business Media
Total Pages: 908
Release: 2010-08-09
ISBN: 1441957979
Category: Science
Language: EN, FR, DE, ES & NL

Systems Biology for Signaling Networks Book Excerpt:

System Biology encompasses the knowledge from diverse fields such as Molecular Biology, Immunology, Genetics, Computational Biology, Mathematical Biology, etc. not only to address key questions that are not answerable by individual fields alone, but also to help in our understanding of the complexities of biological systems. Whole genome expression studies have provided us the means of studying the expression of thousands of genes under a particular condition and this technique had been widely used to find out the role of key macromolecules that are involved in biological signaling pathways. However, making sense of the underlying complexity is only possible if we interconnect various signaling pathways into human and computer readable network maps. These maps can then be used to classify and study individual components involved in a particular phenomenon. Apart from transcriptomics, several individual gene studies have resulted in adding to our knowledge of key components that are involved in a signaling pathway. It therefore becomes imperative to take into account of these studies also, while constructing our network maps to highlight the interconnectedness of the entire signaling pathways and the role of that particular individual protein in the pathway. This collection of articles will contain a collection of pioneering work done by scientists working in regulatory signaling networks and the use of large scale gene expression and omics data. The distinctive features of this book would be: Act a single source of information to understand the various components of different signaling network (roadmap of biochemical pathways, the nature of a molecule of interest in a particular pathway, etc.), Serve as a platform to highlight the key findings in this highly volatile and evolving field, and Provide answers to various techniques both related to microarray and cell signaling to the readers.

Social Network Analysis Applied to Team Sports Analysis

Social Network Analysis Applied to Team Sports Analysis
Author: Filipe Manuel Clemente,Fernando Manuel Lourenço Martins,Rui Sousa Mendes
Publsiher: Springer
Total Pages: 93
Release: 2015-11-06
ISBN: 3319258559
Category: Technology & Engineering
Language: EN, FR, DE, ES & NL

Social Network Analysis Applied to Team Sports Analysis Book Excerpt:

Explaining how graph theory and social network analysis can be applied to team sports analysis, This book presents useful approaches, models and methods that can be used to characterise the overall properties of team networks and identify the prominence of each team player. Exploring the different possible network metrics that can be utilised in sports analysis, their possible applications and variances from situation to situation, the respective chapters present an array of illustrative case studies. Identifying the general concepts of social network analysis and network centrality metrics, readers are shown how to generate a methodological protocol for data collection. As such, the book provides a valuable resource for students of the sport sciences, sports engineering, applied computation and the social sciences.

Applications and Methods in Genomic Networks

Applications and Methods in Genomic Networks
Author: Kimberly Glass,Maud Fagny,Marieke Lydia Kuijjer
Publsiher: Frontiers Media SA
Total Pages: 135
Release: 2022-07-01
ISBN: 2889764826
Category: Science
Language: EN, FR, DE, ES & NL

Applications and Methods in Genomic Networks Book Excerpt:

Handbook of Systems Biology

Handbook of Systems Biology
Author: Marian Walhout,Marc Vidal,Job Dekker
Publsiher: Academic Press
Total Pages: 552
Release: 2012-12-31
ISBN: 012385945X
Category: Science
Language: EN, FR, DE, ES & NL

Handbook of Systems Biology Book Excerpt:

This book provides an entry point into Systems Biology for researchers in genetics, molecular biology, cell biology, microbiology and biomedical science to understand the key concepts to expanding their work. Chapters organized around broader themes of Organelles and Organisms, Systems Properties of Biological Processes, Cellular Networks, and Systems Biology and Disease discuss the development of concepts, the current applications, and the future prospects. Emphasis is placed on concepts and insights into the multi-disciplinary nature of the field as well as the importance of systems biology in human biological research. Technology, being an extremely important aspect of scientific progress overall, and in the creation of new fields in particular, is discussed in 'boxes' within each chapter to relate to appropriate topics. 2013 Honorable Mention for Single Volume Reference in Science from the Association of American Publishers' PROSE Awards Emphasizes the interdisciplinary nature of systems biology with contributions from leaders in a variety of disciplines Includes the latest research developments in human and animal models to assist with translational research Presents biological and computational aspects of the science side-by-side to facilitate collaboration between computational and biological researchers

Theoretical Physics for Biological Systems

Theoretical Physics for Biological Systems
Author: Paola Lecca,Angela Re
Publsiher: CRC Press
Total Pages: 146
Release: 2019-01-24
ISBN: 1351374311
Category: Medical
Language: EN, FR, DE, ES & NL

Theoretical Physics for Biological Systems Book Excerpt:

Quantum physics provides the concepts and their mathematical formalization that lend themselves to describe important properties of biological networks topology, such as vulnerability to external stress and their dynamic response to changing physiological conditions. A theory of networks enhanced with mathematical concepts and tools of quantum physics opens a new area of biological physics, the one of systems biological physics.

High Confidence Network Predictions from Big Biological Data

High Confidence Network Predictions from Big Biological Data
Author: Rasmus Magnusson
Publsiher: Linköping University Electronic Press
Total Pages: 86
Release: 2020-05-04
ISBN: 9179298877
Category: Electronic books
Language: EN, FR, DE, ES & NL

High Confidence Network Predictions from Big Biological Data Book Excerpt:

Biology functions in a most intriguing fashion, with human cells being regulated by multiplex networks of proteins and their dependent systems that control everything from proliferation to cell death. Notably, there are cases when these networks fail to function properly. In some diseases there are multiple small perturbations that push the otherwise healthy cells into a state of malfunction. These maladies are referred to as complex diseases, and include common disorders such as allergy, diabetes type II, and multiple sclerosis, and due to their complexity there is no universally defined approach to fully understand their pathogenesis or pathophysiology. While these perturbations can be measured using high-throughput technologies, the interplay of these perturbations is generally to complex to understand without any structured mathematical analysis. There is today numerous such methods that put the small perturbations of complex diseases into relation of interactions among each other. However, the methods have historically struggled with notable uncertainty in their predictions. This uncertainty can be addressed by at least two different approaches. First, mechanistically realistic mathematical modelling is an approach that has the capacity to accurately describe almost any biological system, but such models can to-date only describe small systems and networks. Secondly, large-scale mathematical modelling approaches exist, but the faithfulness of the models to the underlying biology has been compromised to achieve algorithms that are computationally effective. In this Ph.D. thesis, I suggest how high confidence predictions of network interactions can be extracted from big biological. First, I show how large-scale data can be used when building high-quality ODE models (Paper I). Secondly, by developing the software LASSIM, I show how ODE models can be expanded to the size of entire cell systems (Paper II). However, while LASSIM showed that powerful non-linear ODE-modelling can be applied to understand big biological data, it still remained a machine learning-based approach in contrast to hypothesis-driven model development. Instead, two more studies revolving around large-scale modelling approaches were initiated. The third study suggested that ambiguities in model selection and interaction identification greatly compromise the accuracy of available tools, and that the novel software of Paper III, LiPLike, can be used to remove such predictions. Intriguingly, while LiPLike was able to effectively discard false identifications, the accuracy of predictions remained relatively low. This low accuracy was thought to arise from model simplifications, and therefore the next study aimed at finding methods that come closer to the true biological system (Paper IV). In particular, the study aimed at predicting protein abundance -the true mediators of biological functionality- from the much more easily accessible mRNA levels, and found that such models could be used to get several new insights on protein mechanisms, which was exemplified by the identification of important biomarkers of autoimmune diseases. The analysis of big biological data and the underlying networks is a centrepiece of understanding both diseases and how cell functionality is orchestrated. The work that is presented in this Ph.D. thesis represents a journey between fields with different views on how these networks should be inferred. In particular, it aimed to combine the accuracy of small-scale mechanistic modelling with the system-spanning potential of large-scale linear system modelling, and this thesis thus provides a tool-bench of methods and insights on how knowledge can be extracted from big biological data, and in extension it is a small step towards a generation of new comprehensions of biological systems and complex diseases. Biologiska system är komplexa att förstå och det är först relativt nyligen man på ett strukturerat sätt börjat att analysera biologiska data genom matematisk analys. Ett av de tydligaste områden där en matematisk analys av biologiska system behövs är vid studier av komplexa sjukdomar. Sådana sjukdomar, till vilka åkommor som multipel skleros, diabetes typ II och allergi hör, uppstår genom en komplicerad kombination av arv och miljö som inte är helt förstådd. Studier av komplexa sjukdomar har dock kunnat identifiera många små potentiella störningar över hela det biologiska systemet, men ingen av dessa störningar är individuellt avgörande för att utveckla en komplex sjukdom. Denna svåröverskådlighet förhindrar traditionella analyser för att finna ursprunget till sjukdomen, och går det inte förstå en sjukdom försämras möjligheterna att till exempel hitta nya läkemedel eller att ställa diagnos. För att förstå hur systemen bakom komplexa sjukdomar fungerar, eller inte fungerar, tas olika prover vilka ofta resulterar i enorma mängder data. Dessa datamängder är oftast så stora att vi människor inte kan tolka dem genom att bara läsa talen, utan vi måste använda olika typer av matematiska modeller och datorprogram för att sådan data ska berätta något för oss. Inom två överlappande fält som kommit att kallas systembiologi och bioinformatik har metoder för att analysera biologiska data haft en snabb utveckling de senaste 50 åren. Dessa metoder har haft som mål att svara på flertalet frågor, och ett framträdande mål har varit att identifiera skillnader mellan hur friska och sjuka celler fungerar. En stor del av cellens funktioner regleras av olika nätverk av proteiner, och ett annat mål har varit att förstå hur dessa nätverk regleras. Ytterligare ett mål har varit att identifiera mätbara värden, så kallade biomarkörer, som kan användas för att identifiera sjukdom hos patienter. De metoder som används för att svara på dessa frågor kan grovt delas in i två grupper, mekanistisk modellering och storskalig modellering, med respektive styrkor och svagheter. Mekanistisk modellering har potentialen att ge mycket träffsäkra prediktioner, men kräver mycket manuellt arbete och har därför varit en alltför tidskrävande metod för att applicera på stora biologiska datamängder. Storskalig modellering klarar enkelt av stora datamängder, men har i stället haft en så låg tillförlitlighet att metoder vars förutsägelser är bättre än slumpen i många fall kunnat betraktats som bra. Denna doktorsavhandling kretsar kring utvecklingen och användandet av metoder för att analysera stora mängder av biologiska data, och har i fyra arbeten ämnat att förbättra metoder inom både småskalig mekanistisk modellering (artikel I och II) och storskalig modellering (artikel III och IV). Artikel I analyserade hur diabetes typ II påverkar fettcellers svar på insulin och hur denna insulinsignal kan beskrivas matematiskt. Detta första arbete var begränsat till just små modeller, och en naturlig utveckling var att undersöka om mekanistiska modeller kan skalas upp och beskriva system som täcker en större del av cellens funktionalitet. Detta möjliggjordes i artikel II genom LASSIM, en metod och programvara som kan expandera små mekanistiska modeller till mångdubbel storlek. Under skapandet av LASSIM stod det dock klart att storskalig modellering förblir en metod som är mycket tidskrävande. Därför syftade artikel III till att förbättra tillförlitligheten för prediktioner från befintliga metoder som kan hantera stora datamängder. Mer specifikt föreslog artikel III en ny algoritm, LiPLike, som kan användas för att ta bort prediktioner som saknar konfidens i data. Även om det gick att observera hur LiPLike kunde förbättra tillförlitligheten för etablerade metoder var flera av LiPLikes prediktioner fortfarande fel, vilket kunde antas bero på att den underliggande biologin skiljer sig från det matematiska modellantagande som låg till grund för studien. Därför inleddes den sista delen i denna avhandling, vilken syftade att utreda hur data kan beskrivas på mer biologiskt relevanta sätt. Även om det är proteiner som främst reglerar cellens system, baseras majoriteten av matematiska modeller på ett förstadium till proteiner som kallas mRNA. Anledningen till detta är att det både är svårt och kostsamt att mäta proteiner i ett prov, vilket gör att man istället förlitar sig på mRNA. I artikel IV användes matematisk modellering för att prediktera mängden protein i olika typer av immunceller. Dessa modeller visade sig vara användbara för att identifiera mätbara markörer för olika sjukdomar. Därmed går det använda mRNA-data på sätt som tar modeller närmare verkligheten, och som i förlängningen kan höja tillförlitligheten hos matematiska prediktioner. Forskningen är bara i början av ett långt arbete för att förstå hur celler fungerar, samt hur komplexa sjukdomar uppstår. En central del i detta arbete är att systematiskt beskriva de underliggande system som styr cellen, och detta går nästan enbart att uppnå genom en strukturerad matematisk analys. Denna avhandling kan sammanfattas som en serie arbeten som dels skalar upp storleken på modelleringsmetoder som tidigare varit begränsade till små modeller, och dels höjer tillförlitligheten på mer beräkningseffektiva modeller. Dessa bidrag kommer förhoppningsvis ligga till grund för en ökad förståelse för hur biologiska system bör analyseras och i förlängningen hur komplexa sjukdomar kan motverkas.

Pathway Analysis for Drug Discovery

Pathway Analysis for Drug Discovery
Author: Anton Yuryev
Publsiher: John Wiley & Sons
Total Pages: 304
Release: 2008-09-17
ISBN: 0470399260
Category: Science
Language: EN, FR, DE, ES & NL

Pathway Analysis for Drug Discovery Book Excerpt:

This book introduces drug researchers to the novel computational approaches of pathway analysis and explains the existing applications that can save time and money in the drug discovery process. It covers traditional computational methods and software for pathway analysis microarray, proteomics, and metabolomics. It explains pathway reconstruction of diseases and toxic states, pathway analysis in various phases, dynamic modeling of drug responses, and more. This is a core resource for drug discovery and pharmaceutical industry researchers, chemists, and biologists and for professionals in related fields.

Clustering Challenges in Biological Networks

Clustering Challenges in Biological Networks
Author: Anonim
Publsiher: Unknown
Total Pages: 135
Release: 2022
ISBN: 9814474193
Category: Electronic Book
Language: EN, FR, DE, ES & NL

Clustering Challenges in Biological Networks Book Excerpt:

Algorithms in Computational Molecular Biology

Algorithms in Computational Molecular Biology
Author: Mourad Elloumi,Albert Y. Zomaya
Publsiher: John Wiley & Sons
Total Pages: 1080
Release: 2011-04-04
ISBN: 1118101987
Category: Science
Language: EN, FR, DE, ES & NL

Algorithms in Computational Molecular Biology Book Excerpt:

This book represents the most comprehensive and up-to-date collection of information on the topic of computational molecular biology. Bringing the most recent research into the forefront of discussion, Algorithms in Computational Molecular Biology studies the most important and useful algorithms currently being used in the field, and provides related problems. It also succeeds where other titles have failed, in offering a wide range of information from the introductory fundamentals right up to the latest, most advanced levels of study.

Data Mining for Bioinformatics Applications

Data Mining for Bioinformatics Applications
Author: He Zengyou
Publsiher: Woodhead Publishing
Total Pages: 100
Release: 2015-06-09
ISBN: 008100107X
Category: Computers
Language: EN, FR, DE, ES & NL

Data Mining for Bioinformatics Applications Book Excerpt:

Data Mining for Bioinformatics Applications provides valuable information on the data mining methods have been widely used for solving real bioinformatics problems, including problem definition, data collection, data preprocessing, modeling, and validation. The text uses an example-based method to illustrate how to apply data mining techniques to solve real bioinformatics problems, containing 45 bioinformatics problems that have been investigated in recent research. For each example, the entire data mining process is described, ranging from data preprocessing to modeling and result validation. Provides valuable information on the data mining methods have been widely used for solving real bioinformatics problems Uses an example-based method to illustrate how to apply data mining techniques to solve real bioinformatics problems Contains 45 bioinformatics problems that have been investigated in recent research