Onto2Vec: joint vector-based representation of biological entities and their ontology-based annotations

Smaili, Fatima Zohra; Gao, Xin; Hoehndorf, Robert

doi:10.1093/bioinformatics/bty259

Cited by 96 publications

(115 citation statements)

References 34 publications

(68 reference statements)

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“…Onto2vec encodes GO terms into vectors by transforming their relationships on the GO tree into sentences, which are referred to as axioms in the original paper [21]. For example, the child-parent GO terms GO:0060611 and GO:0060612 are rewritten into the following sentence "GO:0060611 is_subclass GO:0060612".…”

Section: Onto2vecmentioning

confidence: 99%

“…In recent years, with the advancement of computing power, neural network (NN) encoders have been introduced to map GO terms into vectors based on the principle that the vectors of related GO terms should have similar values [7,21]. Once the GO vectors are created, then their distance metric naturally follows; for example, cosine similarity or Euclidean distance can be applied.…”

Section: Introductionmentioning

confidence: 99%

“…Given two GO terms, we train BERT2 and BERT3 to minimize the cosine loss of the two vectors representing these GO term. In BERT4, we reuse BERT1 framework, and simply replace the GO definitions with the GO names so that we convert GO names into vectors; this idea is similar to Onto2vec [21].…”

Section: Introductionmentioning

confidence: 99%

“…Second, we evaluate the 4 BERT encoders, the bidirectional Long Short Term Memory (biLSTM) in [7], Graph Convolution Network in [9], and Onto2vec in [21] in three downstream tasks: (1) measuring semantic similarity between GO terms, (2) asserting relationship for orthologs and interacting proteins based on their GO annotations and (3) predicting GO terms for protein sequences. For tasks 1 and 2, we include two IC methods: Resnik and Aggregated Information Content (AIC) [18,22].…”

Section: Introductionmentioning

confidence: 99%

“…This paper will focus on the second data resource, the Gene Ontology itself. On this end, there have been efforts in developing distance metric for GO terms [7,12,14,16,18,21,28]. Most traditional methods for computing semantic similarity of GO terms rely on the Information Content (IC) and the GO tree.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating Representations for Gene Ontology Terms

Duong

Uppunda

et al. 2019

Preprint

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Functions of proteins are annotated by Gene Ontology (GO) terms. As the amount of new sequences being collected is rising at a faster pace than the number of sequences being annotated with GO terms, there have been efforts to develop better annotation techniques. When annotating protein sequences with GO terms, one key auxiliary resource is the GO data itself. GO terms have definitions consisted of a few sentences describing some biological events, and are also arranged in a tree structure with specific terms being child nodes of generic terms. The definitions and positions of the GO terms on the GO tree can be used to construct vector representations for the GO terms. These GO vectors can then be integrated into existing prediction models to improve the classification accuracy. In this paper, we adapt the Bidirectional Encoder Representations from Transformers (BERT) to encode GO definitions into vectors. We evaluate BERT against the previous GO encoders in three tasks: (1) measuring similarity between GO terms (2) asserting relationship for orthologs and interacting proteins based on their GO annotations and (3) predicting GO terms for protein sequences. For task 3, we show that using GO vectors as additional prediction features increases the accuracy, primarily for GO terms with low occurrences in the manually annotated dataset. In all three tasks, BERT often outperforms the previous GO encoders.https://www.uniprot.org/

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Section: Onto2vecmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluating Representations for Gene Ontology Terms

Duong

Uppunda

et al. 2019

Preprint

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Ontology Completion Using Graph Convolutional Networks

Bouraoui

Schockaert

2019

Lecture Notes in Computer Science

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Many methods have been proposed to automatically extend knowledge bases, but the vast majority of these methods focus on finding plausible missing facts, and knowledge graph triples in particular. In this paper, we instead focus on automatically extending ontologies that are encoded as a set of existential rules. In particular, our aim is to find rules that are plausible, but which cannot be deduced from the given ontology. To this end, we propose a graphbased representation of rule bases. Nodes of the considered graphs correspond to predicates, and they are annotated with vectors encoding our prior knowledge about the meaning of these predicates. The vectors may be obtained from external resources such as word embeddings or they could be estimated from the rule base itself. Edges connect predicates that co-occur in the same rule and their annotations reflect the types of rules in which the predicates co-occur. We then use a neural network model based on Graph Convolutional Networks (GCNs) to refine the initial vector representation of the predicates, to obtain a representation which is predictive of which rules are plausible. We present experimental results that demonstrate the strong performance of this method.

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