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TCR Sequence Representations Using Deep, Contextualized Language Models

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.advisor Jokinen, Emmi
dc.contributor.author Dumitrescu, Alexandru
dc.date.accessioned 2021-03-21T18:05:52Z
dc.date.available 2021-03-21T18:05:52Z
dc.date.issued 2021-03-15
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/103090
dc.description.abstract The recent advents of deep, contextual language models have brought significant improvements to various complex tasks such as neural machine translation or document generation. Models similar to those used in natural language have also started to grow in popularity in the bioinformatics field. The sequence information of proteins can be represented as strings of characters, each denoting one unique amino acid. This fact has led researchers to successfully experiment with amino acid vector representations that are learned and computed with models similar to those used in the natural language field. T cell receptors (TCRs) are sequences of proteins that form through the (random) recombination of the so-called variable (V), diversity (D), and joining (J) gene segments. These sequences are responsible for determining the epitope specificities of T cells and, in turn, their ability to recognize foreign pathogens. The physicochemical properties of each amino acid in a TCR and how the TCR protein folds determine what pathogens the T cell recognizes. This thesis presents and compares various ways of extracting contextual embeddings from T cell receptor proteins, using only their sequence information. We implement and test adaptations of character level Embeddings from Language Models (ELMO) and fine-tune Bidirectional Encoder Representations from Transformers (BERT) models using only sequences of amino acids coming from human TCR proteins. We then test the language models we train using only TCRs on an additional task that classifies a TCR based on its epitope specificity. We show how much the language model's task performance affects the TCR epitope classifier. Finally, we compare our approach to other state-of-the-art methods for TCR epitope classification. en
dc.format.extent 70 + 12
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.title TCR Sequence Representations Using Deep, Contextualized Language Models en
dc.type G2 Pro gradu, diplomityö fi
dc.contributor.school Perustieteiden korkeakoulu fi
dc.subject.keyword leep Learning en
dc.subject.keyword ELMO (Embeddings from Language Models) en
dc.subject.keyword BERT (Bidirectional Encoder Representations from Transformers) en
dc.subject.keyword T-cell receptor en
dc.subject.keyword complementary determining region en
dc.subject.keyword epitope en
dc.identifier.urn URN:NBN:fi:aalto-202103212369
dc.programme.major Alexandru Dumitrescu fi
dc.programme.mcode SCI3044 fi
dc.type.ontasot Master's thesis en
dc.type.ontasot Diplomityö fi
dc.contributor.supervisor Lähdesmäki, Harri
dc.programme Master’s Programme in Computer, Communication and Information Sciences fi
local.aalto.electroniconly yes
local.aalto.openaccess yes

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