Big Semantic Data Processing in the Life Sciences Domain
- 294 Downloads
Big semantic data processing in the life sciences deals with a set of graph-based techniques and methods used to integrate or analyze empirical evidence obtained in the course of life sciences or biomedical research.
The twofold ambition behind biomedical research and development is to either create new knowledge or apply it for treatment and prevention of disease. In life sciences, much of the research is dedicated toward understanding living systems – not just for the sake of knowledge but also to harness and control them. The promise hidden in big biomedical data processing is its potential to accelerate those efforts with predictive analytics informed by empirical results.
The mutability and adaptability inherent in all living things means that medical success requires a deep understanding of genomics: knowing how the flu virus evolves helps devise vaccines to...
KeywordsLife Science Domain The Cancer Genome Atlas (TCGA) Biomedical Experts Open PHACTS Knowledge Graph
- Black DL (2003) Mechanisms of alternative pre-messenger RNA splicing. Annu Rev Biochem 72:291–336. https://doi.org/10.1146/annurev.biochem.72.121801.161720CrossRefGoogle Scholar
- Chen B, Ding Y, Wang H, et al (2010) Chem2Bio2RDF: A Linked Open Data Portal for Systems Chemical Biology. In: Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01. pp 232–239Google Scholar
- Garcia A, Lopez F, Garcia L, et al (2017) Biotea, semantics for PubMed Central. https://doi.org/10.7287/peerj.preprints.3469v1
- Hasnain A, Fox R, Decker S, Deus H (2012) Cataloguing and Linking Life Sciences LOD Cloud. In: 1st International Workshop on Ontology Engineering in a Data-driven World OEDW 2012. pp 1–11Google Scholar
- Hasnain A, Kamdar MR, Hasapis P, et al (2014) Linked Biomedical Dataspace: Lessons Learned Integrating Data for Drug Discovery BT. In: Mika P, Tudorache T, Bernstein A, et al. (eds) The Semantic Web – ISWC 2014. Springer International Publishing, Cham, pp 114–130Google Scholar
- Jentzsch A, Zhao J, Hassanzadeh O, et al (2009) Linking open drug data. In: Proc I-SEMANTICS 2009, GrazGoogle Scholar
- Vieira A (2016) Knowledge Representation in Graphs using Convolutional Neural Networks. Comput Res Repos abs/1612.02255Google Scholar
- Wang M (2017) Predicting Rich Drug-Drug Interactions via Biomedical Knowledge Graphs and Text Jointly Embedding. Compuring Resour Repos abs/1712.08875Google Scholar
- Wild DJ, Ding Y, Sheth AP, et al (2011) Systems chemical biology and the Semantic Web: what they mean for the future of drug discovery research. Drug Discov Today. https://doi.org/10.1016/j.drudis.2011.12.019