Mut2Vec: distributed representation of cancerous mutations

By Sunkyu Kim, Heewon Lee, Keonwoo Kim, Jaewoo Kang. 2018

48 citations recorded by the source corpus.

Embedding 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. We 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. To 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. We 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.

Subfields: Cancer Research, Genetics, Molecular Biology

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Citations in the corpus

Modeling aspects of the language of life through transfer-learning protein sequences (2019, background): “More specialized implementations are mut2vec [90] learning mutations in cancer, and phoscontext2vec [91] identifying phosphorylation sites.”
A deep learning genome-mining strategy for biosynthetic gene cluster prediction (2019, unknown): “As-gari et al. (39) recently adopted word embedding and neural networks to improve classification of protein families using amino acid sequence vectors and Kim et al. (40) has also introduced Mut2Vec for representation of cancerous mutations.”
Modeling the language of life – Deep Learning Protein Sequences (2019, background): “More specialized implementations are mut2vec [83] learning 30 mutations in cancer, and phoscontext2vec [84] identifying phosphorylation sites.”
Fully-Connected Neural Networks with Reduced Parameterization for Predicting Histological Types of Lung Cancer from Somatic Mutations (2020, background): “Moreover, embedding techniques can provide insights into how the model captures each input feature in a particular context by distributed representation, reflecting the semantic relationship between variables [13,14].”