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標(biāo)題: Titlebook: Biological Networks in Human Health and Disease; Romana Ishrat Book 2023 The Editor(s) (if applicable) and The Author(s), under exclusive [打印本頁(yè)]

作者: Auditory-Nerve    時(shí)間: 2025-3-21 17:51
書(shū)目名稱(chēng)Biological Networks in Human Health and Disease影響因子(影響力)




書(shū)目名稱(chēng)Biological Networks in Human Health and Disease影響因子(影響力)學(xué)科排名




書(shū)目名稱(chēng)Biological Networks in Human Health and Disease網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱(chēng)Biological Networks in Human Health and Disease網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱(chēng)Biological Networks in Human Health and Disease被引頻次




書(shū)目名稱(chēng)Biological Networks in Human Health and Disease被引頻次學(xué)科排名




書(shū)目名稱(chēng)Biological Networks in Human Health and Disease年度引用




書(shū)目名稱(chēng)Biological Networks in Human Health and Disease年度引用學(xué)科排名




書(shū)目名稱(chēng)Biological Networks in Human Health and Disease讀者反饋




書(shū)目名稱(chēng)Biological Networks in Human Health and Disease讀者反饋學(xué)科排名





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Rede zur Verleihung des Kleist-Preises 2008his high volume of data brought the term “Big Data.” The term “Big Data” has several dimensions which contain a huge amount of unstructured and structured data sets. Analysis, interpretation, and complete extraction of meaningful information from the raw, structured, and unstructured datasets lead t
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Poetische Logik verwickelter Verh?ltnisse). Appropriate analysis of large-scale datasets (proteomics, genomes, multi-omics data, Htseq, RNA-seq, ChIPseq, and so on) is now a bottleneck. One of the most frequent ways of representing biological systems as complicated sets of binary interactions or relationships between diverse bio-entities i
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Poetische Logik verwickelter Verh?ltnissee advancement and promises of AI can provide in medicine have been increasing. On the other hand, the concept and application of quantitative matrices for the networks in biology started by Neumann in 2010 has now grown as a respective field of its own, i.e., Network Biology. It is time to join thes
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https://doi.org/10.1007/978-981-99-4242-8Biological networks; Big Data Analytics; Network Medicine; Machine Learning; Bioinformatics
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Romana IshratPresents tools of network biology for understanding disease dynamics.Examines opportunities and challenges in network medicine.Reviews the role of machine learning in analyzing the biological networks
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Rede zur Verleihung des Kleist-Preises 2008nized and unstructured data sets, therefore storing, transferring, processing, and searching the raw data is extremely challenging, and it cannot be managed using traditional database systems and software tools. The heterogeneous “Big data” set contains petabytes or exabytes of raw data, with billio
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Network Analysis Based Software Packages, Tools, and Web Servers to Accelerate Bioinformatics Reseaa variety a number of online and stand-alone tools that exist for network construction, analysis, Network functional module Identification, and visualization. Overall, this chapter reaches a very broad spectrum of researchers varying from experts to beginners and they can look up a genetic universe
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Role of R in Biological Network Analysis,k analysis in biology and medicine include discovering drug targets, determining the function of a protein or gene, inventing successful strategies for treating various diseases, and providing early identification of abnormalities. Several software tools, such as Gephi and Cytoscape, are built for n
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Networks Analytics of Heterogeneous Big Data,his high volume of data brought the term “Big Data.” The term “Big Data” has several dimensions which contain a huge amount of unstructured and structured data sets. Analysis, interpretation, and complete extraction of meaningful information from the raw, structured, and unstructured datasets lead t
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Network Medicine: Methods and Applications,s, and the discovery of therapeutic targets, and it is a rapidly growing arena for medical science and research, which comes with the possibilities to reform the system of disease diagnosis and its treatment. The NM uses topological and dynamic properties of the biological networks (protein–protein
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Machine Learning in Biological Networks,e advancement and promises of AI can provide in medicine have been increasing. On the other hand, the concept and application of quantitative matrices for the networks in biology started by Neumann in 2010 has now grown as a respective field of its own, i.e., Network Biology. It is time to join thes
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