Overview of the gene ontology task at BioCreative IV

Abstract

Gene Ontology (GO) annotation is a common task among model organism databases (MODs) for capturing gene function data from journal articles. It is a time-consuming and labor-intensive task, and is thus often considered as one of the bottlenecks in literature curation. There is a growing need for semiautomated or fully automated GO curation techniques that will help database curators to rapidly and accurately identify gene function information in full-length articles. Despite multiple attempts in the past, few studies have proven to be useful with regard to assisting real-world GO curation. The shortage of sentence-level training data and opportunities for interaction between text-mining developers and GO curators has limited the advances in algorithm development and corresponding use in practical circumstances. To this end, we organized a text-mining challenge task for literature-based GO annotation in BioCreative IV. More specifically, we developed two subtasks: (i) to automatically locate text passages that contain GO-relevant information (a text retrieval task) and (ii) to automatically identify relevant GO terms for the genes in a given article (a concept-recognition task). With the support from five MODs, we provided teams with >4000 unique text passages that served as the basis for each GO annotation in our task data. Such evidence text information has long been recognized as critical for text-mining algorithm development but was never made available because of the high cost of curation. In total, seven teams participated in the challenge task. From the team results, we conclude that the state of the art in automatically mining GO terms from literature has improved over the past decade while much progress is still needed for computer-assisted GO curation. Future work should focus on addressing remaining technical challenges for improved performance of automatic GO concept recognition and incorporating practical benefits of text-mining tools into real-world GO annotation.Database URL: http://www.biocreative.org/tasks/biocreative-iv/track-4-GO/.

ModEx: A text mining system for extracting mode of regulation of Transcription Factor-gene regulatory interaction

“…ER methods, typically divide the task into two steps, (1) identify the entities and their location in the context, and (2) assign unique identifiers to the entities [23]. Fortunately, multiple terminological databases, such as Gene Ontology [25], UMBLS [26], BioLexicon [26], and Biothesaurus [26] provide information on biological entities and name variations and can be used to detect biological entities such as genes or proteins [27,28].…”

Text mining resources for the life sciences

“…This resource has drawn a remarkable attention from the TM and NLP community. However on close examination, the extraction of GO concepts from text content is still a research subject since it has proven to be challenging (59). …”

Pressing needs of biomedical text mining in biocuration and beyond: opportunities and challenges

“…The foremost of these difficulties is that challenge tasks are often simplified or abstracted versions of the real-world problems. For example, although biocurators routinely use the full text of an article (56, 57), challenge tasks often only utilize the abstract due to difficulties in accessing full text articles and processing full text. A consequence of this simplification of the real-world problem is that even systems that perform well on challenge tasks yield significantly lower results when evaluated in practical real-world settings.…”