ArticleScientific reports2019
DeepIso: A Deep Learning Model for Peptide Feature Detection from LC-MS map.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed.
- Artificial Intelligence and Machine Learning in Mass Spectrometry-Based Proteomics and Metabolomics.Methods in molecular biology (Clifton, N.J.) · 2027Article
- Multimodal Transformer Fusion of PTR-ToF-MS Volatiles and Targeted Non-Volatile Metabolites Enables Data-Driven Grading of Baimudan White Tea.Rapid communications in mass spectrometry : RCM · 2026Article
- Article
- Protein Design Enters the Artificial Intelligence Era: Foundations, Tools, and Emerging Paradigms.Computational and structural biotechnology journal · 2026Review
- The Future of a Myriad of Accelerated Biodiscoveries Lies in AI-Powered Mass Spectrometry and Multiomics Integration.Journal of mass spectrometry : JMS · 2025Article
- A Fast Neural Network for Isotopic Charge State Assignment.Journal of the American Chemical Society · 2025Article
- Automated classification in turtles genus Malayemys using ensemble multiview image based on improved YOLOv8 with CNN.Scientific reports · 2024Article
- An end-to-end deep learning framework for translating mass spectra to de-novo molecules.Communications chemistry · 2023Article
- TopFD: A Proteoform Feature Detection Tool for Top-Down Proteomics.Analytical chemistry · 2023Article
- Mass Spectrometry-Based Proteomics Workflows in Cancer Research: The Relevance of Choosing the Right Steps.Cancers · 2023Review
- Deep Learning Based MS2 Feature Detection for Data-Independent Shotgun Proteomics.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2022Article
- Article
- PD-BertEDL: An Ensemble Deep Learning Method Using BERT and Multivariate Representation to Predict Peptide Detectability.International journal of molecular sciences · 2022Article
- Augmentation of MS/MS Libraries with Spectral Interpolation for Improved Identification.Journal of chemical information and modeling · 2022Article
- Locality-sensitive hashing enables efficient and scalable signal classification in high-throughput mass spectrometry raw data.BMC bioinformatics · 2022Article
- A streamlined platform for analyzing tera-scale DDA and DIA mass spectrometry data enables highly sensitive immunopeptidomics.Nature communications · 2022Article
- Artificial intelligence in clinical and translational science: Successes, challenges and opportunities.Clinical and translational science · 2022Review
- A Checklist for Reproducible Computational Analysis in Clinical Metabolomics Research.Metabolites · 2022Review
- Deep neural network for detecting arbitrary precision peptide features through attention based segmentation.Scientific reports · 2021Article
- Deep learning neural network tools for proteomics.Cell reports methods · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Liquid chromatography with tandem mass spectrometry (LC-MS/MS) based quantitative proteomics provides the relative different protein abundance in healthy and disease-afflicted patients, which offers the information for molecular interactions, signaling pathways, and biomarker identification to serve the drug discovery and clinical research. Typical analysis workflow begins with the peptide feature detection and intensity calculation from LC-MS map. We are the first to propose a deep learning based model, DeepIso, that combines recent advances in Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) to detect peptide features of different charge states, as well as, estimate their intensity. Existing tools are designed with limited engineered features and domain-specific parameters, which are hardly updated despite a huge amount of new coming proteomic data. On the other hand, DeepIso consisting of two separate deep learning based modules, learns multiple levels of representation of high dimensional data itself through many layers of neurons, and adaptable to newly acquired data. The peptide feature list reported by our model matches with 97.43% of high quality MS/MS identifications in a benchmark dataset, which is higher than the matching produced by several widely used tools. Our results demonstrate that novel deep learning tools are desirable to advance the state-of-the-art in protein identification and quantification.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.