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Articles tagged: machine learning
Tutorial: Selecting the best MS2PIP model
Mascot Server 3.0 can refine database search results using predicted fragment intensities. The predictions are provided by MS2PIP, and Mascot ships with suitable models for common instrument types. This tutorial shows how to select the best model for your instrument and experiment. What is MS2PIP? MS2PIP is a tool for predicting the MS/MS fragmentation spectrum from a peptide sequence, charge [...]
Tutorial: Selecting the best DeepLC model
Mascot Server 3.0 can refine database search results using predicted retention times. The predictions are provided by DeepLC, and Mascot ships with twenty models for different gradient lengths, column types and peptide properties. This tutorial shows how to select the best model for your experiment. What is DeepLC? DeepLC is a retention time predictor that uses a convolutional neural network [...]
Predicted RT and fragment intensity in Mascot Server 3.0
A release candidate of the next version of Mascot Server is running on this website. One of the headline features in the preliminary release notes is refining results with machine learning, which includes integration with MS2Rescore. Below is a preview for you to enjoy while we are beta testing the new release. What is MS2Rescore? Mascot Server has shipped with [...]
Matrix Science at ASMS 2024
The ASMS 2024 conference was held in Anaheim, California, on 2-6 June 2024. It was great to see many of you at our booth as well as the annual Breakfast Meeting! We presented three talks at the meeting. If you were unable to attend, or want to review any of the material that was presented, the talks are summarised below [...]