2020
DOI: 10.1093/bioinformatics/btaa536
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HECNet: a hierarchical approach to enzyme function classification using a Siamese Triplet Network

Abstract: Motivation Understanding an enzyme’s function is one of the most crucial problem domains in computational biology. Enzymes are a key component in all organisms and many industrial processes as they help in fighting diseases and speed up essential chemical reactions. They have wide applications and therefore, the discovery of new enzymatic proteins can accelerate biological research and commercial productivity. Biological experiments, to determine an enzyme’s function, are time-consuming and r… Show more

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Cited by 26 publications
(17 citation statements)
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“…The model used in mlHECNet is similar to the one used in our study on mono‐functional enzymes 2 . There are two types of features: length dependent and length independent features.…”
Section: Methodsmentioning
confidence: 99%
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“…The model used in mlHECNet is similar to the one used in our study on mono‐functional enzymes 2 . There are two types of features: length dependent and length independent features.…”
Section: Methodsmentioning
confidence: 99%
“…The data gathered from PSSMs underwent normalization as performed by HECNet. 2 The PSSMs were vertically stacked together and the mean and variance, in depth, was calculated for each position. Hence, we acquired position specific means and standard deviations for the PSSM.…”
Section: Featuresmentioning
confidence: 99%
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“…Language modeling approaches on proteins [8], [17], [18] utilize protein sequences as an analogy to sentences in NLP. HECNet [19] employs the siamese network for enzyme function classification. In this classification framework, a domain specific feature extraction is used, while in the present work we produce generic embeddings for proteins.…”
Section: Introductionmentioning
confidence: 99%