Hidden Markov model and Chapman Kolmogrov for protein structures prediction from images. (June 2017) - Kamal, Md. Sarwar; Chowdhury, Linkon; Khan, Mohammad Ibrahim; Ashour, Amira S.; Tavares, João Manuel R.S.; Dey, Nilanjan
Graphical abstract: Highlights: Primary, secondary and tertiary protein structure predictions from images is an essential process. Images are converted to the binary values using Otsu Method. Number of 1's and 0's are the key factor for Otsu method analysis. Chapman-Kolmogorv equation is used for predicting the certain proteins patterns. Flood fill algorithm and Warshall algorithm are used for structure classification. Chapman Kolmogrov equation can predict any values from any position. That is the key for this research outcome. Big datasets can be easily managed by Chapman Kolmogrov equation. Abstract: Protein structure prediction and analysis are more significant for living organs to perfect asses the living organ functionalities. Several protein structure prediction methods use neural network (NN). However, the Hidden Markov model is more interpretable and effective for more biological data analysis compared to the NN. It employs statistical data analysis to enhance the prediction accuracy. The current work proposed a protein prediction approach from protein images based on Hidden Markov Model and Chapman Kolmogrov equation. Initially, a preprocessing stage was applied for protein images' binarization using Otsu technique in order to convert the protein image into binary matrix. Subsequently, two counting algorithms, namely the Flood fill and Warshall are employed to classify the protein structures. Finally, Hidden Markov model and Chapman Kolmogrov equation are applied on the classified structures for predicting the protein structure. The execution time and algorithmic performances are measured to evaluate the primary, secondary and tertiary protein structure prediction.