ArticleBMC medical genomics2018
Mut2Vec: distributed representation of cancerous mutations.
Article in BMC medical genomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed.
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- Transformer-based representation learning for robust gene expression modeling and cancer prognosis.Scientific reports · 2025Article
- Language Modelling Techniques for Analysing the Impact of Human Genetic Variation.Bioinformatics and biology insights · 2025Review
- A best-match approach for gene set analyses in embedding spaces.Genome research · 2024Article
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- Deep learning in cancer genomics and histopathology.Genome medicine · 2024Review
- Mutation-Attention (MuAt): deep representation learning of somatic mutations for tumour typing and subtyping.Genome medicine · 2023Article
- Natural Language Processing to Extract Meaningful Information from a Corpus of Written Knowledge in Breast Cancer: Transforming Books into Data.Breast care (Basel, Switzerland) · 2023Article
- Organizing the bacterial annotation space with amino acid sequence embeddings.BMC bioinformatics · 2022Article
- Incorporating Machine Learning into Established Bioinformatics Frameworks.International journal of molecular sciences · 2021Review
- Fully-Connected Neural Networks with Reduced Parameterization for Predicting Histological Types of Lung Cancer from Somatic Mutations.Biomolecules · 2020Article
- A Novel System for Functional Determination of Variants of Uncertain Significance using Deep Convolutional Neural Networks.Scientific reports · 2020Article
- Modeling aspects of the language of life through transfer-learning protein sequences.BMC bioinformatics · 2019Article
- A pan-cancer somatic mutation embedding using autoencoders.BMC bioinformatics · 2019Article
- Molecular Cavity Topological Representation for Pattern Analysis: A NLP Analogy-Based Word2Vec Method.International journal of molecular sciences · 2019Article
- A deep learning genome-mining strategy for biosynthetic gene cluster prediction.Nucleic acids research · 2019Article
- Probabilistic variable-length segmentation of protein sequences for discriminative motif discovery (DiMotif) and sequence embedding (ProtVecX).Scientific reports · 2019Article
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4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundEmbedding techniques for converting high-dimensional sparse data into low-dimensional distributed representations have been gaining popularity in various fields of research. In deep learning models, embedding is commonly used and proven to be more effective than naive binary representation. However, yet no attempt has been made to embed highly sparse mutation profiles into densely distributed representations. Since binary representation does not capture biological context, its use is limited in many applications such as discovering novel driver mutations. Additionally, training distributed representations of mutations is challenging due to a relatively small amount of available biological data compared with the large amount of text corpus data in text mining fields.
methodsWe introduce Mut2Vec, a novel computational pipeline that can be used to create a distributed representation of cancerous mutations. Mut2Vec is trained on cancer profiles using Skip-Gram since cancer can be characterized by a series of co-occurring mutations. We also augmented our pipeline with existing information in the biomedical literature and protein-protein interaction networks to compensate for the data insufficiency.
resultsTo evaluate our models, we conducted two experiments that involved the following tasks: a) visualizing driver and passenger mutations, b) identifying novel driver mutations using a clustering method. Our visualization showed a clear distinction between passenger mutations and driver mutations. We also found driver mutation candidates and proved that these were true driver mutations based on our literature survey. The pre-trained mutation vectors and the candidate driver mutations are publicly available at http://infos.korea.ac.kr/mut2vec .
conclusionsWe introduce Mut2Vec that can be utilized to generate distributed representations of mutations and experimentally validate the efficacy of the generated mutation representations. Mut2Vec can be used in various deep learning applications such as cancer classification and drug sensitivity prediction.
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