Supplementary Issue: Computational Advances in Cancer Informatics (a)

By Yuan Luo, G. Riedlinger, Peter Szolovits

103 citations recorded by the source corpus.

Prioritization of cancer implicated genes has received growing attention as an effective way to reduce wet lab cost by computational analysis that ranks candidate genes according to the likelihood that experimental verifications will succeed. A multitude of gene prioritization tools have been developed, each integrating different data sources covering gene sequences, differential expressions, function annotations, gene regulations, protein domains, protein interactions, and pathways. This review places existing gene prioritization tools against the backdrop of an integrative Omic hierarchy view toward cancer and focuses on the analysis of their text mining components. We explain the relatively slow progress of text mining in gene prioritization, identify several challenges to current text mining methods, and highlight a few directions where more effective text mining algorithms may improve the overall prioritization task and where prioritizing the pathways may be more desirable than prioritizing only genes.

Subfields: Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

Citations in the corpus

Natural Language Processing for EHR-Based Computational Phenotyping (2018, background): “geneous data sources in addition to structured data, such as clinical narratives, public databases, social media, biomedical literature [15], [88], [101], [111], [115], [175], [176].”
Bridging semantics and syntax with graph algorithms —state-of-the-art of extracting biomedical relations (2015, background): “Such relation mining has shown its value in the prioritization of cancerous genes for further validation from a large number of candidates [21].”
Bridging semantics and syntax with graph algorithms - state-of-the-art of extracting biomedical relations (2017, background): “Such relation mining has shown its value in the prioritization of cancerous genes for further validation from a large number of candidates [26].”
Natural Language Processing for EHR-Based Pharmacovigilance: A Structured Review (2017, background): “This paper assumes that the reader has a passing knowledge of biomedical NLP; we refer the reader to introductions and surveys on the latest applications [15, 16, 17] for more information.”