RFCRYS: sequence-based protein crystallization propensity prediction by means of random forest
From MaRDI portal
Publication:1784813
DOI10.1016/J.JTBI.2012.04.028zbMath1397.92530OpenAlexW1970247646WikidataQ51351700 ScholiaQ51351700MaRDI QIDQ1784813
Samad Jahandideh, Abbas Mahdavi
Publication date: 27 September 2018
Published in: Journal of Theoretical Biology (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jtbi.2012.04.028
Applications of statistics to biology and medical sciences; meta analysis (62P10) Learning and adaptive systems in artificial intelligence (68T05) Protein sequences, DNA sequences (92D20)
Uses Software
Cites Work
- Using the augmented Chou's pseudo amino acid composition for predicting protein submitochondria locations based on auto covariance approach
- Some remarks on protein attribute prediction and pseudo amino acid composition
- Using the concept of Chou's pseudo amino acid composition for risk type prediction of human papillomaviruses
- Prediction of GABA\(_{\mathrm A}\) receptor proteins using the concept of Chou's pseudo-amino acid composition and support vector machine
- \textbf{iLoc-Virus}: a multi-label learning classifier for identifying the subcellular localization of virus proteins with both single and multiple sites
- The modified Mahalanobis discriminant for predicting outer membrane proteins by using Chou's pseudo amino acid composition
- Using Chou's amphiphilic pseudo-amino acid composition and support vector machine for prediction of enzyme subfamily classes
- Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data
- Random forests
This page was built for publication: RFCRYS: sequence-based protein crystallization propensity prediction by means of random forest
