https://github.com/aalto-ics-kepaco/comboltr
Science Score: 13.0%
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○CITATION.cff file
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○codemeta.json file
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○.zenodo.json file
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✓DOI references
Found 2 DOI reference(s) in README -
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○Scientific vocabulary similarity
Low similarity (6.4%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: aalto-ics-kepaco
- License: mit
- Language: Python
- Default Branch: main
- Size: 218 KB
Statistics
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
comboLTR: Modeling drug combination effects via latent tensor reconstruction
Overview
comboLTR is a new polynomial regression-based framework for modeling anti-cancer effects of drug combinations in various doses and across different cancer cell lines. It is implemented in Python.
The data used in the experiments is available on: https://doi.org/10.5281/zenodo.4625084.
Instructions
ltrtensorsolveractxuvcls010.py This file contains the code for polynomial regression via latent tensor reconstruction.
The description of the interface of the solver is given in readme.pdf file.
comboLTR_CV.py This file contains the code for cross validations on the full dataset used in the paper.
Dependencies
- numpy
- scikit-learn
- scipy
Citing comboLTR
Owner
- Name: KEPACO
- Login: aalto-ics-kepaco
- Kind: organization
- Location: Espoo, Finland
- Website: http://research.ics.aalto.fi/kepaco/
- Repositories: 29
- Profile: https://github.com/aalto-ics-kepaco
Kernel Machines, Pattern Analysis and Computational Metabolomics - Research group at Aalto University