Deep functional synthesis: a machine learning approach to gene functional enrichment

Wang, Sheng; Ma, Jianzhu; Fong, Samson; Rensi, Stefano; Peng, Jian; Pratt, Dexter; Altman, Russ B.; Ideker, Trey

doi:10.1101/824086

Cited by 7 publications

(5 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Third, we observed that BioTranslator's performance improvement relative to comparison approaches is larger on the smaller collection of phenotype-pathway associations than on the larger collection of phenotype-gene associations. Finally, compared to literature mining approaches that focus on existing phrases in scientific paper text [81][82][83] , the sentence generated by our method can be a novel sentence, and thus can better describe new discoveries that have never been covered in existing literature or controlled vocabularies.…”

Section: Discussionmentioning

confidence: 99%

Multilingual translation for zero-shot biomedical classification using BioTranslator

Woicik

Poon

et al. 2023

Nat Commun

Self Cite

View full text Add to dashboard Cite

Existing annotation paradigms rely on controlled vocabularies, where each data instance is classified into one term from a predefined set of controlled vocabularies. This paradigm restricts the analysis to concepts that are known and well-characterized. Here, we present the novel multilingual translation method BioTranslator to address this problem. BioTranslator takes a user-written textual description of a new concept and then translates this description to a non-text biological data instance. The key idea of BioTranslator is to develop a multilingual translation framework, where multiple modalities of biological data are all translated to text. We demonstrate how BioTranslator enables the identification of novel cell types using only a textual description and how BioTranslator can be further generalized to protein function prediction and drug target identification. Our tool frees scientists from limiting their analyses within predefined controlled vocabularies, enabling them to interact with biological data using free text.

show abstract

Section: Discussionmentioning

confidence: 99%

Multilingual translation for zero-shot biomedical classification using BioTranslator

Woicik

Poon

et al. 2023

Nat Commun

Self Cite

View full text Add to dashboard Cite

show abstract

“…Our method complements these existing efforts by using a novel natural language processing perspective and fills in an important gap towards automating GO curation. Another line of related works is automatically generating the term name for a set of genes or proteins [46][47][48]. Compared to these approaches, we generate a free text that contains a few sentences, which are more informative than a simple term name.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

ProTranslator: Zero-Shot Protein Function Prediction Using Textual Description

Wang

2022

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

Accurately finding proteins and genes that have a certain function is the prerequisite for a broad range of biomedical applications. Despite the encouraging progress of existing computational approaches in protein function prediction, it remains challenging to annotate proteins to a novel function that is not collected in the Gene Ontology and does not have any annotated proteins. This limitation, a "side effect" from the widely-used multi-label classification problem setting of protein function prediction, hampers the progress of studying new pathways and biological processes, and further slows down research in various biomedical areas. Here, we tackle this problem by annotating proteins to a function only based on its textual description so that we don't need to know any associated proteins for this function.The key idea of our method ProTranslator is to redefine protein function prediction as a machine translation problem, which translates the description word sequence of a function to the amino acid sequence of a protein. We can then transfer annotations from functions that have similar textual description to annotate a novel function. We observed substantial improvement in annotating novel functions and sparsely annotated functions on CAFA3, SwissProt and GOA datasets. We further demonstrated how our method accurately predicted gene members for a given pathway in Reactome, KEGG and MSigDB only based on the pathway description. Finally, we showed how ProTranslator enabled us to generate the textual description instead of the function label for a set of proteins, providing a new scheme for protein function prediction. We envision ProTranslator will give rise to a protein function "search engine" that returns a list of proteins based on the free text queried by the user.

show abstract

“…Graph2text and data2text, which aim at generating text from structured data, have attracted increasing attention (Marcheggiani and Perez-Beltrachini, 2018;Cai and Lam, 2020;Yao et al, 2020;Guo et al, 2020;Wang et al, 2019). Among them, AMR-to-text Generation and knowledge graph to text generation also consider graph structures.…”

Section: Relate Workmentioning

confidence: 99%

Graphine: A Dataset for Graph-aware Terminology Definition Generation

Liu

Wang

et al. 2021

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Self Cite

View full text Add to dashboard Cite

Precisely defining the terminology is the first step in scientific communication. Developing neural text generation models for definition generation can circumvent the laborintensity curation, further accelerating scientific discovery. Unfortunately, the lack of large-scale terminology definition dataset hinders the process toward definition generation. In this paper, we present a large-scale terminology definition dataset Graphine covering 2,010,648 terminology definition pairs, spanning 227 biomedical subdisciplines. Terminologies in each subdiscipline further form a directed acyclic graph, opening up new avenues for developing graph-aware text generation models. We then proposed a novel graphaware definition generation model Graphex that integrates transformer with graph neural network. Our model outperforms existing text generation models by exploiting the graph structure of terminologies. We further demonstrated how Graphine can be used to evaluate pretrained language models, compare graph representation learning methods and predict sentence granularity. We envision Graphine to be a unique resource for definition generation and many other NLP tasks in biomedicine. 1

show abstract

Deep functional synthesis: a machine learning approach to gene functional enrichment

Cited by 7 publications

References 35 publications

Multilingual translation for zero-shot biomedical classification using BioTranslator

Multilingual translation for zero-shot biomedical classification using BioTranslator

ProTranslator: Zero-Shot Protein Function Prediction Using Textual Description

Graphine: A Dataset for Graph-aware Terminology Definition Generation

Contact Info

Product

Resources

About