Sequence Based DNA-Binding Protein Prediction

Protein and DNA have vital role in our biological processes. For accurately predicting binding protein, develop a new sequence based prediction method from the protein sequence. Sequence only considers information as input. DBP, first reliable benchmark data set bank. Second, using Amino Acid Composition (AAC), Position Specific Scoring Matrix (PSSM), Predicted Solvent Accessibility (PSA), Probabilities of DNA-Binding Sites (PDBS) to produce four specific baselines. Using differential evolution algorithm, weights properties are taught. Based on those attained properties, merge characteristics with create an original super feature. And tensor-flow is used paralyze weights. A suitable feature selection algorithm tensor flow’s binary classifier extract excellent subset weighted vector. The training sample obtained process, after generating final features. classification learned through support vector machine flow. output measured surface. choice done basis threshold likelihood above-threshold chance considered be DBP others non-DBP.