Nicaragua: diagnóstico de las fuentes de energía nuevas y renovables y del uso racional de la energía

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Additional file 3: Figure. S1. of RRCRank: a fusion method using rank strategy for residue-residue contact prediction

The distributions of protein domains’ length on the CASP11 dataset and CASP12 dataset. (a) CASP11 dataset. (b) CASP12 dataset. (PDF 44 kb

Additional file 2: Table S2. of RRCRank: a fusion method using rank strategy for residue-residue contact prediction

Detailed list of the 55 protein domains of CASP12 dataset. (PDF 30 kb

Additional file 7: Table S4. of RRCRank: a fusion method using rank strategy for residue-residue contact prediction

The comparative results of the proposed method with the state-of-the-art methods on 40 CASP12 targets. (PDF 13 kb

Additional file 8: Table S5. of RRCRank: a fusion method using rank strategy for residue-residue contact prediction

The p-values in Student’s t-test for the difference on L/5 prediction precision between different methods on CASP11 dataset. (PDF 112 kb

Additional file 9: Table S6. of RRCRank: a fusion method using rank strategy for residue-residue contact prediction

The p-values in Student’s t-test for the difference in L/5 prediction precision between different methods on CASP12 dataset. (PDF 258 kb

Using Amino Acid Physicochemical Distance Transformation for Fast Protein Remote Homology Detection

<div><p>Protein remote homology detection is one of the most important problems in bioinformatics. Discriminative methods such as support vector machines (SVM) have shown superior performance. However, the performance of SVM-based methods depends on the vector representations of the protein sequences. Prior works have demonstrated that sequence-order effects are relevant for discrimination, but little work has explored how to incorporate the sequence-order information along with the amino acid physicochemical properties into the prediction. In order to incorporate the sequence-order effects into the protein remote homology detection, the physicochemical distance transformation (PDT) method is proposed. Each protein sequence is converted into a series of numbers by using the physicochemical property scores in the amino acid index (AAIndex), and then the sequence is converted into a fixed length vector by PDT. The sequence-order information can be efficiently included into the feature vector with little computational cost by this approach. Finally, the feature vectors are input into a support vector machine classifier to detect the protein remote homologies. Our experiments on a well-known benchmark show the proposed method SVM-PDT achieves superior or comparable performance with current state-of-the-art methods and its computational cost is considerably superior to those of other methods. When the evolutionary information extracted from the frequency profiles is combined with the PDT method, the profile-based PDT approach can improve the performance by 3.4% and 11.4% in terms of ROC score and ROC50 score respectively. The local sequence-order information of the protein can be efficiently captured by the proposed PDT and the physicochemical properties extracted from the amino acid index are incorporated into the prediction. The physicochemical distance transformation provides a general framework, which would be a valuable tool for protein-level study.</p> </div

The average ROC scores of the sequence-based PDT approach with different <i>β</i> values on SCOP 1.53 dataset.

<p>The average ROC scores of the sequence-based PDT approach with different <i>β</i> values on SCOP 1.53 dataset.</p

Comparison against the profile-based methods.

*<p>The results of HHsearch are obtained by in-house implementation of the hhsuite package.</p

Comparison against the methods based on sequence composition information.

<p>NA refers to the unreported result.</p