Literature DB >> 22845900

Using literature-based discovery to identify novel therapeutic approaches.

Dimitar Hristovski1, Thomas Rindflesch, Borut Peterlin.   

Abstract

We present a promising in silico paradigm called literature-based discovery (LBD) and describe its potential to identify novel pharmacologic approaches to treating diseases. The goal of LBD is to generate novel hypotheses by analyzing the vast biomedical literature. Additional knowledge resources, such as ontologies and specialized databases, are often used to supplement the published literature. MEDLINE, the largest and most important biomedical bibliographic database, is the most common source for exploiting LBD. There are two variants of LBD, open discovery and closed discovery. With open discovery we can, for example, try to find a novel therapeutic approach for a given disease, or find new therapeutic applications for an existing drug. With closed discovery we can find an explanation for a relationship between two concepts. For example, if we already have a hypothesis that a particular drug is useful for a particular disease, with closed discovery we can identify the mechanisms through which the drug could have a therapeutic effect on the disease. We briefly describe the methodology behind LBD and then discuss in more detail currently available LBD tools; we also mention in passing some of those no longer available. Next we present several examples in which LBD has been exploited for identifying novel therapeutic approaches. In conclusion, LBD is a powerful paradigm with considerable potential to complement more traditional drug discovery methods, especially for drug target discovery and for existing drug relabeling.

Entities:  

Mesh:

Year:  2013        PMID: 22845900     DOI: 10.2174/1871525711311010005

Source DB:  PubMed          Journal:  Cardiovasc Hematol Agents Med Chem        ISSN: 1871-5257


  18 in total

1.  Predicting Adverse Drug Effects from Literature- and Database-Mined Assertions.

Authors:  Mary K La; Alexander Sedykh; Denis Fourches; Eugene Muratov; Alexander Tropsha
Journal:  Drug Saf       Date:  2018-11       Impact factor: 5.606

2.  Using Literature-Based Discovery to Explain Adverse Drug Effects.

Authors:  Dimitar Hristovski; Andrej Kastrin; Dejan Dinevski; Anita Burgun; Lovro Žiberna; Thomas C Rindflesch
Journal:  J Med Syst       Date:  2016-06-18       Impact factor: 4.460

3.  Exploring Novel Computable Knowledge in Structured Drug Product Labels.

Authors:  Scott A Malec; Richard D Boyce
Journal:  AMIA Jt Summits Transl Sci Proc       Date:  2020-05-30

4.  SemMedDB: a PubMed-scale repository of biomedical semantic predications.

Authors:  Halil Kilicoglu; Dongwook Shin; Marcelo Fiszman; Graciela Rosemblat; Thomas C Rindflesch
Journal:  Bioinformatics       Date:  2012-10-08       Impact factor: 6.937

Review 5.  Recent advances in biomedical literature mining.

Authors:  Sendong Zhao; Chang Su; Zhiyong Lu; Fei Wang
Journal:  Brief Bioinform       Date:  2021-05-20       Impact factor: 11.622

6.  Predicting high-throughput screening results with scalable literature-based discovery methods.

Authors:  T Cohen; D Widdows; C Stephan; R Zinner; J Kim; T Rindflesch; P Davies
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2014-10-08

7.  Networks of neuroinjury semantic predications to identify biomarkers for mild traumatic brain injury.

Authors:  Michael J Cairelli; Marcelo Fiszman; Han Zhang; Thomas C Rindflesch
Journal:  J Biomed Semantics       Date:  2015-05-18

8.  Integration of data from omic studies with the literature-based discovery towards identification of novel treatments for neovascularization in diabetic retinopathy.

Authors:  Ales Maver; Dimitar Hristovski; Thomas C Rindflesch; Borut Peterlin
Journal:  Biomed Res Int       Date:  2013-11-24       Impact factor: 3.411

9.  Enabling online studies of conceptual relationships between medical terms: developing an efficient web platform.

Authors:  Aaron Albin; Xiaonan Ji; Tara B Borlawsky; Zhan Ye; Simon Lin; Philip Ro Payne; Kun Huang; Yang Xiang
Journal:  JMIR Med Inform       Date:  2014-10-07

10.  Large-scale structure of a network of co-occurring MeSH terms: statistical analysis of macroscopic properties.

Authors:  Andrej Kastrin; Thomas C Rindflesch; Dimitar Hristovski
Journal:  PLoS One       Date:  2014-07-09       Impact factor: 3.240

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.