Questions On Random Forest Method Essay

Questions On Random Forest Method Essay

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2.4.2 Random Forest Method
In the RSA prediction done by Pugalenthi et al. where Random Forest Method (RFM) approach was applied, residues of 120 proteins were categorized on either a positive class (buried) or a negative class (exposed) given the threshold limits of 0%, 5%, 10%, 25% and 50%. The proteins used share less than 25% similarity among each other to avoid redundancy of data. Multiple sequence alignment was also employed through PSI-BLAST to gather data concerning hydrophobicity and frequency of occurrence of functional groups phenyl, carboxyl, imidazole, primary amine, guanidine, thiol, sulfur, amido, hydroxyl and non-polar. Results showed that buried residues are mostly hydrophobic whereas the results achieved >70% accuracy for all the thresholds. Using the same model, surface accessibilities of residues from human thioredoxin (pdb code 1erv) were also predicted to compare with results of previously used neural network method on the same sample. Result from RFM yielded higher accuracy. Furthermore, performance measures in the study were defined by the equations below [21].
Sensitivity=TP/(TP+FN) Equation 1
Specificity=TN/(TN+FP) Equation 2
Accuracy=(TP+TN)/(TP+FP+TN+FN) Equation 3
Matthew^ ' s Corr Coef=(TPTN-FPFN)/√((TN+FN)(TP+FN)(TN+FP)(TP+FP)) Equation 4
where true positives (TP) are correctly predicted buried residues, true negatives (TN) are correctly predicted exposed residues, false positives (FP) are exposed residues predicted as buried; and false negatives (FN) are buried residues predicted as exposed [21].
2.4.3 Multiple Linear Regression Method
A novel approach was presented in the Lustig group by Rose et al. [7] and Nepal [9] to predict RSA by multiple linear regression methods using...

... middle of paper ...

...ation, solvent accessibility and statistical pair-wise potentials (PP) to determine the hot spots. Resulting model yielded higher accuracy that experimental methods [29].

2.5.2 Allosteric Regions
Allosteric regions are defined as group of residues relatively far from the interfacial sites of protein-protein interactions which cause conformational changes on the protein quaternary structure. These changes are termed as allosteric mutations or indirect mutations and are dictated by a constant selection for function and stability. Perica et al. [13] investigated the mechanism of this mutation on a protein family of pyrimidine operon attenuators. The study identified key mutations acting from a distance to destabilize the interface. This effect was found to be equivalent to conformational shifts between free and nucleotide-bound conformations of proteins [13].

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