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ReDeem Logo

ReDeeM: Repository for Deep Learning Models for Mass Spectrometry

ReDeeM is a Rust crate designed for implementing deep learning models specifically tailored for mass spectrometry data. The primary goal of this project is to facilitate the prediction of peptide properties and to develop classifier scoring models (TDA).

Usage

The ReDeeM crates are designed to be used as a library in other projects, i.e. in Sage. To use the ReDeeM crates, add the following to your Cargo.toml file:

[dependencies]
redeem-properties = { git = "https://github.com/singjc/redeem.git", branch = "master" }
redeem-classifiers = { git = "https://github.com/singjc/redeem.git", branch = "master" }

Note: The ReDeeM crates are still under development and are not yet available on crates.io.

Current Crates

The ReDeeM project consists of two primary crates:

  1. redeem-properties:

    • This crate focuses on deep learning models for peptide property prediction. It implements models for predicting retention time (RT), ion mobility (IM), and MS2 fragment intensities using the Candle library.

    • The models can be fine-tuned on new data and can be saved in the safetensor format for later use.

    • Current Models

    Model Name Architecture Implemented
    AlphaPept RT Model redeem_properties::RTCNNLSTMModel CNN-LSTM ✔️
    AlphaPept MS2 Model redeem_properties::MS2BertModel Bert ✔️
    AlphaPept IM Model redeem_properties::CCSCNNLSTMModel CNN-LSTM ✔️

  2. redeem-classifiers:

    • This crate is aimed at developing semi-supervised scoring classifier models. The goal is to create models for separating target peptides from decoys.

    • Current Models

    Model Name Architecture Implemented
    XGBoost Classifier redeem_classifiers::XGBoostClassifier XGBoost ✔️
    GBDT Classifier redeem_classifiers::GBDTClassifier GBDT ✔️
    SVM Classifier redeem_classifiers::SVMClassifier SVM ✔️