Search Results for author:
Found 7 papers, 6 papers with code
Computationally instrument-resolution-independent de novo peptide sequencing for high-resolution devices
De novo peptide sequencing is the key technology for finding novel peptides from mass spectra.
PointIso: Point Cloud Based Deep Learning Model for Detecting Arbitrary-Precision Peptide Features in LC-MS Map through Attention Based Segmentation
A promising technique of discovering disease biomarkers is to measure the relative protein abundance in multiple biofluid samples through liquid chromatography with tandem mass spectrometry (LC-MS/MS) based quantitative proteomics.
DeepNovoV2: Better de novo peptide sequencing with deep learning
Personalized cancer vaccines are envisioned as the next generation rational cancer immunotherapy.
Deep learning enables de novo peptide sequencing from data-independent-acquisition mass spectrometry
We present DeepNovo-DIA, a de novo peptide-sequencing method for data-independent acquisition (DIA) mass spectrometry data.
DeepIso: A Deep Learning Model for Peptide Feature Detection
In this paper we propose a novel deep learning based model, DeepIso, that uses Convolutional Neural Networks (CNNs) to scan an LC-MS map to detect peptide features and estimate their abundance.
Protein identification with deep learning: from abc to xyz
We combine two modules de novo sequencing and database search into a single deep learning framework for peptide identification, and integrate de Bruijn graph assembly technique to offer a complete solution to reconstruct protein sequences from tandem mass spectrometry data.
De novo peptide sequencing by deep learning
In this study, we propose a deep neural network model, DeepNovo, for de novo peptide sequencing.
