Deep Question Answering for protein annotation

Gobeill, Julien; Gaudinat, Arnaud; Pasche, Emilie; Vishnyakova, Dina; Gaudet, Pascale; Bairoch, Amos Marc; Ruch, Patrick

doi:10.1093/database/bav081

Cited by 22 publications

(23 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the meantime, prediction methods can help close the sequence-annotation gap [87,91], but with respect to deep annotations of function, in silico methods remain as limited as their experimental ‘teachers’ [39,92,93]. Machine learning plays critical roles in capturing protein function from the vast biomedical data resources [39,89].…”

Section: Precision Medicine: Proteins and Disease Mechanismsmentioning

confidence: 99%

Protein function in precision medicine: deep understanding with machine learning

2016

View full text Add to dashboard Cite

Precision medicine and personalized health efforts propose leveraging complex molecular, medical and family history, along with other types of personal data toward better life. We argue that this ambitious objective will require advanced and specialized machine learning solutions. Simply skimming some low-hanging results off the data wealth might have limited potential. Instead, we need to better understand all parts of the system to define medically relevant causes and effects: how do particular sequence variants affect particular proteins and pathways? How do these effects, in turn, cause the health or disease-related phenotype? Toward this end, deeper understanding will not simply diffuse from deeper machine learning, but from more explicit focus on understanding protein function, context-specific protein interaction networks, and impact of variation on both.

show abstract

Section: Precision Medicine: Proteins and Disease Mechanismsmentioning

confidence: 99%

Protein function in precision medicine: deep understanding with machine learning

2016

View full text Add to dashboard Cite

show abstract

“…We are only aware of three other biomedical QA services, as surveyed in (Bauer and Berleant, 2012), namely, askHERMES (Cao et al, 2011), EAGLi (Gobeill et al, 2015) and HONQA (Cruchet et al, 2009). However, none of the these systems performs robustly to most question types.…”

Section: Related Workmentioning

confidence: 99%

Assessing the performance of Olelo, a real-time biomedical question answering application

Neves¹,

Eckert²,

Folkerts³

et al. 2017

BioNLP 2017

View full text Add to dashboard Cite

Question answering (QA) can support physicians and biomedical researchers to find answers to their questions in the scientific literature. Such systems process large collections of documents in real time and include many natural language processing (NLP) procedures. We recently developed Olelo, a QA system for biomedicine which includes various NLP components, such as question processing, document and passage retrieval, answer processing and multi-document summarization. In this work, we present an evaluation of our system on the the fifth BioASQ challenge. We participated with the current state of the application and with an extension based on semantic role labeling that we are currently investigating. In addition to the BioASQ evaluation, we compared our system to other on-line biomedical QA systems in terms of the response time and the quality of the answers.

show abstract

“…EAGLi (Gobeill et al, 2015) provides answers based on concepts from the Gene Ontology (GO). Even when no answers are found for a question, EAGLi always outputs a list of relevant publications.…”

Section: Related Workmentioning

confidence: 99%

Proceedings of ACL 2017, System Demonstrations

Zhang

2017

View full text Add to dashboard Cite

We present the first open-source tool for annotating morphosyntactic tense, mood and voice for English, French and German verbal complexes. The annotation is based on a set of language-specific rules, which are applied on dependency trees and leverage information about lemmas, morphological properties and POS-tags of the verbs. Our tool has an average accuracy of about 76%. The tense, mood and voice features are useful both as features in computational modeling and for corpuslinguistic research. IntroductionNatural language employs, among other devices such as temporal adverbials, tense and aspect to locate situations in time and to describe their temporal structure (Deo, 2012). The tool presented here addresses the automatic annotation of morphosyntactic tense, i.e., the tense-aspect combinations, expressed in the morphology and syntax of verbal complexes (VC). VCs are sequences of verbal tokens within a verbal phrase. We address German, French and English, in which the morphology and syntax also includes information on mood and voice. Morphosyntactic tenses do not always correspond to semantic tense (Deo, 2012). For example, the morphosyntactic tense of the English sentence "He is leaving at noon." is present progressive, while the semantic tense is future. In the remainder of this paper, we use the term tense to refer to the morphological tense and aspect information encoded in finite verbal complexes.Corpus-linguistic research, as well as automatic modeling of mono-and cross-lingual use of tense, mood and voice will strongly profit from a reliable automatic method for identifying these clausal features. They may, for instance, be used to classify texts with respect to the epoch or region in which they have been produced, or for assigning texts to a specific author. Moreover, in crosslingual research, tense, mood, and voice have been used to model the translation of tense between different language pairs (Santos, 2004; Loáiciga et al., 2014; Ramm and Fraser, 2016)). Identifying the morphosyntactic tense is also a necessary prerequisite for identifying the semantic tense in synthetic languages such as English, French or German (Reichart and Rappoport, 2010). The extracted tense-mood-voice (TMV) features may also be useful for training models in computational linguistics, e.g., for modeling of temporal relations (Costa and Branco, 2012; UzZaman et al., 2013).As illustrated by the examples in Figure 1, relevant information for determining TMV is given by syntactic dependencies and partially by partof-speech (POS) tags output by analyzers such as Mate (Bohnet and Nivre, 2012). However, the parser's output is not sufficient for determining TMV features; morphological features and lexical information needs to be taken into account as well. Learning TMV features from an annotated corpus would be an alternative; however, to the best of our knowledge, no such large-scale corpora exist.A sentence may contain more than one VC, and the tokens belonging to a VC are not always contiguous in the sentence (see VCs A...

show abstract

Deep Question Answering for protein annotation

Cited by 22 publications

References 22 publications

Protein function in precision medicine: deep understanding with machine learning

Protein function in precision medicine: deep understanding with machine learning

Assessing the performance of Olelo, a real-time biomedical question answering application

Proceedings of ACL 2017, System Demonstrations

Contact Info

Product

Resources

About