Feb 26, 2018 · First of all, we extract amino acid components, dipeptide component and entropy density from Position Specific Scoring Matrix to construct the ...
Since the SVM-RFE algorithm can select the optimal feature subset according to the correlation between each feature and protein subcellular localization, and it ...
The research of algorithm for protein subcellular localization prediction based on SVM-RFE ; Springer Nature · Chou K., Shen H. · Nature Protocols · 2008 ; Elsevier.
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An SVM-based system for predicting protein subnuclear localizations
www.ncbi.nlm.nih.gov › PMC1325059
The overall accuracy of prediction for 6 localizations is about 50% (vs. random prediction 16.7%) for single localization proteins in the leave-one-out cross- ...
Missing: RFE. | Show results with:RFE.
We developed a novel computational approach based on human protein atlas (HPA) data, referred to as PScL-HDeep, for accurate and efficient image-based ...
Jul 30, 2021 · We developed a novel computational approach based on human protein atlas (HPA) data, referred to as PScL-HDeep, for accurate and efficient image-based ...
Oct 22, 2024 · In this paper we present a new method for extracting appropriate features from the sequence data by computing pairwise sequence alignment scores ...
Jun 15, 2020 · In this study, a Deep learning (DL) technique is proposed to enhance the precision of the analytical engine of one of these tools called PSORTb v3.0.
A novel method (StackPDB) to predict DNA-binding proteins. Fusing the PSSM-TPC, PsePSSM, EDT, RPT and PseAAC methods to extract feature information.
The article proposes a novel method named ML-locMLFE. First of all, six feature extraction methods are adopted to obtain protein effective information.