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Introducing π-HelixNovo for practical large-scale de novo peptide sequencing

2023-08-27 · bioRxiv 2023 8 · Tingpeng Yang, Tianze Ling, Boyan Sun, Zhendong Liang, Fan Xu, Xiansong Huang, Linhai Xie, Yonghong He, Leyuan Li, Fuchu He, Yu Wang, Cheng Chang

De novo peptide sequencing is a promising approach for novel peptide discovery. We use a novel concept of complementary spectra to enhance ion information and propose a de novo sequencing model π-HelixNovo based on Transformer architecture. π-HelixNovo outperforms other state-of-the-art models and enhances the taxonomic resolution of gut metaproteome, taking a significant step forward in de novo sequencing.

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Code (1)

PHOENIXcenter/pi-HelixNovo 공식 구현 pytorch

Tasks

de novo peptide sequencing

Methods 이 논문이 사용한 방법론

Attention 설명 없음
Linear Layer A Linear Layer is a projection $\mathbf{XW + b}$.
Dense Connections Dense Connections, or Fully Connected Connections, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output…
Dropout Dropout is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is…
Softmax The Softmax output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$…
Absolute Position Encodings Absolute Position Encodings are a type of position embeddings for [Transformer-based models] where positional encodings are…
Multi-Head Attention 설명 없음
Residual Connection 설명 없음

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