Application
- Name
- KISSE
- Type
- ShinyProxy App
- URL
- https://kisse.serve.scilifelab.se
- Source Code
- https://github.com/hassanakthv/SIPMS
- Image
- ghcr.io/hassanakthv/sipms:20250320-154543
- Created
- 17 Feb, 2025
- Updated
- 25 Mar, 2025
- Tags
- lc-ms/ms, proteomics, species-identification
KISSE is a species search engine that utilizes collagen sequences from eight different species to identify unknown samples.
Software
- Type
- Cloud Application
- Operating System
- Kubernetes
- Version
- ghcr.io/scilifelabdatacentre/serve-charts/shinyproxy:1.4.4
Resource
Project
- Name
- KISSE
- Created
- 17 May, 2024
Species Identification and Prediction by Mass Spectrometry (SIP-MS) everages shotgun proteomics techniques to offer collagenous peptide-based species identification. It stands on two foundational pillars: a machine learning method classifier (a Random Forest classifier) with species-specific peptide sequences and abundances, and a correlation classifier that considers all informative peptides in a dataset.
Owner
Hassan Gharibi
hassan.gharibi@ki.se
Department of Medical Biochemistry and Biophysics
Karolinska Institutet (Karolinska Institute)
