BMC Med Inform Decis Mak - Generation and application of drug indication inference models using typed network motif comparison analysis.

Tópicos

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Resumo

CKGROUND: As the amount of publicly available biomedical data increases, discovering hidden knowledge from biomedical data (i.e., Undiscovered Public Knowledge (UPK) proposed by Swanson) became an important research topic in the biological literature mining field. Drug indication inference, or drug repositioning, is one of famous UPK tasks, which infers alternative indications for approved drugs. Many previous studies tried to find novel candidate indications of existing drugs, but these works have following limitations: 1) models are not fully automated which required manual modulations to desired tasks, 2) are not able to cover various biomedical entities, and 3) have inference limitations that those works could infer only pre-defined cases using limited patterns. To overcome these problems, we suggest a new drug indication inference model.METHODS: In this paper, we adopted the Typed Network Motif Comparison Algorithm (TNMCA) to infer novel drug indications using topology of given network. Typed Network Motifs (TNM) are network motifs, which store types of data, instead of values of data. TNMCA is a powerful inference algorithm for multi-level biomedical interaction data as TNMs depend on the different types of entities and relations. We utilized a new normalized scoring function as well as network exclusion to improve the inference results. To validate our method, we applied TNMCA to a public database, Comparative Toxicogenomics Database (CTD).RESULTS: The results show that enhanced TNMCA was able to infer meaningful indications with high performance (AUC = 0.801, 0.829) compared to the ABC model (AUC = 0.7050) and previous TNMCA model (AUC = 0.5679, 0.7469). The literature analysis also shows that TNMCA inferred meaningful results.CONCLUSIONS: We proposed and enhanced a novel drug indication inference model by incorporating topological patterns of given network. By utilizing inference models from the topological patterns, we were able to improve inference power in drug indication inferences.

Resumo Limpo

ckground amount public avail biomed data increas discov hidden knowledg biomed data ie undiscov public knowledg upk propos swanson becam import research topic biolog literatur mine field drug indic infer drug reposit one famous upk task infer altern indic approv drug mani previous studi tri find novel candid indic exist drug work follow limit model fulli autom requir manual modul desir task abl cover various biomed entiti infer limit work infer predefin case use limit pattern overcom problem suggest new drug indic infer modelmethod paper adopt type network motif comparison algorithm tnmca infer novel drug indic use topolog given network type network motif tnm network motif store type data instead valu data tnmca power infer algorithm multilevel biomed interact data tnms depend differ type entiti relat util new normal score function well network exclus improv infer result valid method appli tnmca public databas compar toxicogenom databas ctdresult result show enhanc tnmca abl infer meaning indic high perform auc compar abc model auc previous tnmca model auc literatur analysi also show tnmca infer meaning resultsconclus propos enhanc novel drug indic infer model incorpor topolog pattern given network util infer model topolog pattern abl improv infer power drug indic infer

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