title: A new hybrid fractal algorithm for predicting thermophilic nucleotide sequences.
abstract: Knowledge of thermophilic mechanisms about some organisms whose optimum growth temperature (OGT) ranges from 50 to 80 degree plays a major role in helping design stable proteins. How to predict a DNA sequence to be thermophilic is a long but not fairly resolved problem. Chaos game representation (CGR) can investigate the patterns hiding in DNA sequences, and can visually reveal previously unknown structure. Fractal dimensions are good tools to measure sizes of complex, highly irregular geometric objects. In this paper, we convert every DNA sequence into a high dimensional vector by CGR algorithm and fractal dimension, and then predict the DNA sequence thermostability by these fractal features and support vector machine (SVM). We have conducted experiments on three groups: 17-dimensional vector, 65-dimensional vector, and 257-dimensional vector. Each group is evaluated by the 10-fold cross-validation test. For the results, the group of 257-dimensional vector gets the best results: the average accuracy is 0.9456 and average MCC is 0.8878. The results are also compared with the previous work with single CGR features. The comparison shows the high effectiveness of the new hybrid fractal algorithm.
authors: College of Science, Huazhong Agricultural University, Wuhan, PR China.
authors: Jin-Long
authors: JL
authors: Lu
authors: Xue-Hai
authors: XH
authors: Hu
authors: Dong-Gang
authors: DG
authors: Hu
mesh_terms: D000465:Algorithms; D001483:Base Sequence; D004269:DNA, Bacterial / Q000235:genetics*; D030561:Databases, Nucleic Acid; D017709:Fractals; D006358:Hot Temperature; D008957:Models, Genetic; D017711:Nonlinear Dynamics; D017422:Sequence Analysis, DNA / Q000379:methods*; D060388:Support Vector Machine
chemical_list: D004269:DNA, Bacterial