Computational solutions for omics data
By Bonnie Berger, Jian Peng, Mona Singh. Nature reviews genetics, 2013
328 citations recorded by the source corpus.
High-throughput experimental technologies are generating increasingly massive and complex genomic data sets. The sheer enormity and heterogeneity of these data threaten to make the arising problems computationally infeasible. Fortunately, powerful algorithmic techniques lead to software that can answer important biomedical questions in practice. In this Review, we sample the algorithmic landscape, focusing on state-of-the-art techniques, the understanding of which will aid the bench biologist in analysing omics data. We spotlight specific examples that have facilitated and enriched analyses of sequence, transcriptomic and network data sets.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/b23631cfc2400368864fe619f32d9b7942bc64d0
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In Molecular Biology
Citations in the corpus
- Methods for the integration of multi-omics data: mathematical aspects (2016, background): “For example, Berger et al. [1] described integrative approaches in one of the sections of their work, which is also focused on tools for the analysis of single omics layers, while Kristensen et al. [2] presented objectives, methods and computational tools of integrative genomics, with a particular…”
- Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine (2020, background): “To effectively implement healthcare data analytic processes, various big data management challenges (1,9,13,14, 15,16,17,18,19) have to be overcome, which include inadequacy of analyzable clinical data (20); existence of multiple data standards, structures, types and formats; rapid growth in heterog”
- From big data analysis to personalized medicine for all: challenges and opportunities (2015, background): “…already exist and are adopted by companies such as Google, Apple, Amazon, and Facebook to tackle the fairly homogenous big data (i.e. user data) [37], the heterogeneous nature of omics data presents a new challenge that requires sufficient understanding of the underlying biological concepts and…”