https://github.com/cleliacort/build-hmm-
Main steps to make a HMM profile.
Science Score: 13.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
○CITATION.cff file
-
✓codemeta.json file
Found codemeta.json file -
○.zenodo.json file
-
○DOI references
-
○Academic publication links
-
○Academic email domains
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (4.0%) to scientific vocabulary
Repository
Main steps to make a HMM profile.
Basic Info
Statistics
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
build-HMM-
The project describe the procedure to build a Profile Hidden Markov Model able to predict the presence of the Kunitz domain in a sequence. The predictor is trained using a set of Kunitz proteins (Pfam ID:PF0014) which are structurally similar. We tested the predictor using two benchmark sets from which we derived statical informations to evaluate in which E-value range it is able to do its optimal performance. The final results are supported by two statical coefficients: the Accuracy Coefficient and the Matthews Correlation Coefficient.
You can follow all the single steps on the file called Profile-HMM.sh
Owner
- Name: Clelia
- Login: cleliacort
- Kind: user
- Twitter: cleliac93
- Repositories: 1
- Profile: https://github.com/cleliacort
Research fellow in Bioinformatics at IRCCS Regina Elena National Cancer Institute. Working in the field of epigentics!