Matches in SemOpenAlex for { <https://semopenalex.org/work/W3044499156> ?p ?o ?g. }
- W3044499156 endingPage "50" @default.
- W3044499156 startingPage "39" @default.
- W3044499156 abstract "The abundance of high-throughput data and technical refinements in graph theories have allowed network analysis to become an effective approach for various medical fields. This chapter introduces co-expression, Bayesian, and regression-based network construction methods, which are the basis of network analysis. Various methods in network topology analysis are explained, along with their unique features and applications in biomedicine. Furthermore, we explain the role of network embedding in reducing the dimensionality of networks and outline several popular algorithms used by researchers today. Current literature has implemented different combinations of topology analysis and network embedding techniques, and we outline several studies in the fields of genetic-based disease prediction, drug–target identification, and multi-level omics integration." @default.
- W3044499156 created "2020-07-29" @default.
- W3044499156 creator A5045201341 @default.
- W3044499156 creator A5087651522 @default.
- W3044499156 date "2020-01-01" @default.
- W3044499156 modified "2023-09-24" @default.
- W3044499156 title "Applications of Network Analysis in Biomedicine" @default.
- W3044499156 cites W1530323443 @default.
- W3044499156 cites W1544009106 @default.
- W3044499156 cites W1743429370 @default.
- W3044499156 cites W1966327575 @default.
- W3044499156 cites W2004078197 @default.
- W3044499156 cites W2008620264 @default.
- W3044499156 cites W2014165627 @default.
- W3044499156 cites W2016273060 @default.
- W3044499156 cites W2024775366 @default.
- W3044499156 cites W2029835563 @default.
- W3044499156 cites W2048638896 @default.
- W3044499156 cites W2058293922 @default.
- W3044499156 cites W2079364348 @default.
- W3044499156 cites W2082257952 @default.
- W3044499156 cites W2084224084 @default.
- W3044499156 cites W2084364487 @default.
- W3044499156 cites W2088545291 @default.
- W3044499156 cites W2097498614 @default.
- W3044499156 cites W2099300649 @default.
- W3044499156 cites W2106870542 @default.
- W3044499156 cites W2112090702 @default.
- W3044499156 cites W2112262143 @default.
- W3044499156 cites W2112811019 @default.
- W3044499156 cites W2124533460 @default.
- W3044499156 cites W2125941741 @default.
- W3044499156 cites W2126498993 @default.
- W3044499156 cites W2127033580 @default.
- W3044499156 cites W2130901965 @default.
- W3044499156 cites W2131681506 @default.
- W3044499156 cites W2132202037 @default.
- W3044499156 cites W2132623845 @default.
- W3044499156 cites W2139516171 @default.
- W3044499156 cites W2140207977 @default.
- W3044499156 cites W2146545950 @default.
- W3044499156 cites W2146653758 @default.
- W3044499156 cites W2146858327 @default.
- W3044499156 cites W2148103594 @default.
- W3044499156 cites W2152558529 @default.
- W3044499156 cites W2154947819 @default.
- W3044499156 cites W2157943660 @default.
- W3044499156 cites W2159675211 @default.
- W3044499156 cites W2162142896 @default.
- W3044499156 cites W2165533101 @default.
- W3044499156 cites W2166140491 @default.
- W3044499156 cites W2169528473 @default.
- W3044499156 cites W2171878761 @default.
- W3044499156 cites W2393319904 @default.
- W3044499156 cites W2607497028 @default.
- W3044499156 cites W2729684734 @default.
- W3044499156 cites W2735272571 @default.
- W3044499156 cites W2745192145 @default.
- W3044499156 cites W2749279690 @default.
- W3044499156 cites W2757523215 @default.
- W3044499156 cites W2769019754 @default.
- W3044499156 cites W2770983033 @default.
- W3044499156 cites W2805086457 @default.
- W3044499156 cites W2806387533 @default.
- W3044499156 cites W2884668708 @default.
- W3044499156 cites W2890210261 @default.
- W3044499156 cites W2903230268 @default.
- W3044499156 cites W2905215896 @default.
- W3044499156 cites W2908304275 @default.
- W3044499156 cites W2914174212 @default.
- W3044499156 cites W2929682486 @default.
- W3044499156 cites W2930902918 @default.
- W3044499156 cites W2937762029 @default.
- W3044499156 cites W2946099214 @default.
- W3044499156 cites W2952631022 @default.
- W3044499156 cites W2962049406 @default.
- W3044499156 cites W2962756421 @default.
- W3044499156 cites W3031415405 @default.
- W3044499156 cites W3104097132 @default.
- W3044499156 cites W3105705953 @default.
- W3044499156 doi "https://doi.org/10.1007/978-1-0716-0904-0_4" @default.
- W3044499156 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/32710313" @default.
- W3044499156 hasPublicationYear "2020" @default.
- W3044499156 type Work @default.
- W3044499156 sameAs 3044499156 @default.
- W3044499156 citedByCount "1" @default.
- W3044499156 countsByYear W30444991562021 @default.
- W3044499156 crossrefType "book-chapter" @default.
- W3044499156 hasAuthorship W3044499156A5045201341 @default.
- W3044499156 hasAuthorship W3044499156A5087651522 @default.
- W3044499156 hasConcept C111030470 @default.
- W3044499156 hasConcept C119599485 @default.
- W3044499156 hasConcept C119857082 @default.
- W3044499156 hasConcept C124101348 @default.
- W3044499156 hasConcept C127413603 @default.
- W3044499156 hasConcept C132525143 @default.
- W3044499156 hasConcept C154945302 @default.
- W3044499156 hasConcept C199845137 @default.