Granular support vector machine based method for prediction of solubility of proteins on overexpression in escherichia coli
Pankaj Kumar,Valadi K. Jayaraman,Bhaskar D. Kulkarni +2 more
- pp 406-415
TLDR
The results indicate that a difficult imbalanced classification problem can be successfully solved by employing a granular support vector Machines for prediction of soluble proteins on over expression in Escherichia coli.Abstract:
We employed a granular support vector Machines(GSVM) for prediction of soluble proteins on over expression in Escherichia coli. Granular computing splits the feature space into a set of subspaces (or information granules) such as classes, subsets, clusters and intervals [14]. By the principle of divide and conquer it decomposes a bigger complex problem into smaller and computationally simpler problems. Each of the granules is then solved independently and all the results are aggregated to form the final solution. For the purpose of granulation association rules was employed. The results indicate that a difficult imbalanced classification problem can be successfully solved by employing GSVM.read more
Citations
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Journal ArticleDOI
A review of machine learning methods to predict the solubility of overexpressed recombinant proteins in Escherichia coli.
TL;DR: This paper presents an extensive review of the existing models to predict protein solubility in Escherichia coli recombinant protein overexpression system and concludes that some of the models present acceptable prediction performances and convenient user interfaces can be considered as valuable tools to predict recombinant Protein Solubility results before performing real laboratory experiments, thus saving labour, time and cost.
Journal ArticleDOI
Granular support vector machine: a review
Husheng Guo,Wenjian Wang +1 more
TL;DR: Granular support vector machine (GSVM) is a novel machine learning model based on granular computing and statistical learning theory, and it can solve the low efficiency learning problem that exists in the traditional SVM and obtain satisfactory generalization performance, as well.
Proceedings ArticleDOI
Prediction of protein solubility in E. coli
TL;DR: This work presents a framework that creates models of solubility from sequence information from the primary protein sequences of the genes to be synthesized, and provides the biologist with a comprehensive comparison between different learning algorithms, and insightful feature analysis.
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Anand V. Sastry,Jonathan M. Monk,Hanna Tegel,Mathias Uhlén,Bernhard O. Palsson,Johan Rockberg,Elizabeth Brunk +6 more
TL;DR: This work identifies protein properties that hinder the HPA high‐throughput antibody production pipeline based on a subset of key properties (aromaticity, hydropathy and isoelectric point) and guides the selection of protein fragments based on these characteristics to optimize high-throughput experimentation.
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Granular Computing Concept based long-term prediction of Gas Tank Levels in Steel Industry
TL;DR: In this article, a regression model based on the Granular Computing (GrC) is proposed to provide a long-term prediction for the LDG tank levels, in which the data segments are entirely considered for the prediction horizon extension rather than the generic data point-oriented modeling.
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