Lationships between them are identified (see Figure 2). Biological NEs correspond to
Lationships between them are identified (see Figure 2). Biological NEs correspond to classes such as genes, proteins, cell lines, species, compounds, phenotypes, diseases, etc. Named entity recognition (NER) refers to the problem of labelling both the location (start, end) and the semantic class/type PubMed ID:https://www.ncbi.nlm.nih.gov/pubmed/26866270 of a NE in text, and normalisation refers to the process of mapping a NEFigure 2. An example of a text-mining pipeline. Given a sentence from a paper (A), named entities (NEs) are extracted (green for species entities, red for protein/gene entities, blue for relationship cues) (B); these entities are then normalised to a corresponding identifier scheme (C); and relationships between entities extracted (D). The final result in this case is a network which explicitly encodes the semantic relationships between NEs found in the sentence. Text taken from PMID:14613582.# HENRY STEWART PUBLICATIONS 1479 ?364. HUMAN GENOMICS. VOL 5. NO 1. 17 ?29 OCTOBERREVIEWHarmston, Filsell and Stumpfto a unique identifier (or set of identifiers). Following NER and normalisation it is useful to determine if a real relationship exists between two or more NEs, as well as the type of relationship. Simply identifying that NEs occur together in a contiguous block of text does hint at the existence of some form of relationship; however, this relationship may be completely speculative, or the text may state that a relationship between the NEs does not exist.14 In biological research papers, two entities can co-occur for many reasons, including functional, physical, syntenic and evolutionary relationships. The performance of TM systems is often evaluated using precision and recall metrics against manually curated gold standard corpora. Precision can be interpreted as the probability that a randomly selected result is a true positive and is calculated as the number of true positives obtained over the sum of true positives and false positives. Recall can be intuitively interpreted as the probability that a randomly selected positive result is correctly identified; it can be calculated as the number of true positives divided by the number of items that should be found (the sum of true positives and false negatives).NERBiology is a dynamic and ever-expanding research area. This means that there are millions of entity names in use, with new ones constantly being created (eg through genome annotation and drug development). Neologisms are prevalent in the literature; it has been jokingly commented: `Scientists would rather share each other’s underwear than use each other’s nomenclature’ (Keith Yamamoto). Biological NER thus tends to be more difficult than NER tasks in other domains (eg newswires) due to the variability of biological nomenclatures.15,16 A single gene can have many synonyms (eg P53, TP53 and TRP53 all refer to the same gene). Gene names are subject to morphological (eg transcription factor, transcriptional factor), orthographic (eg nuclear factor [NF] kappa B, NF kB), combinatorial (eg homologue of actin, actin homologue) and Ornipressin site inflectional variation (eg antibody, antibodies). The HUGO Gene Nomenclature committee (HGNC)was created with the aim of assigning a unique gene symbol to every gene; however, currently, not all genes have been assigned a name and there are still problems with gene names mentioned in the past literature. Gene names can overlap with other names relating to different entity types in the biological domain, as well as with words that are found i.
