CNN-LSTM based classification of polo like kinase family of Proteins: An emerging cancer drug target

• Main Authors: John, Chinju, Mathew, Oommen K., Sahoo, Jayakrushna
• Format: Conference Proceeding
• Language: English
• Subjects: Cancer drug target; Convolutional Neural Network; Deep Learning; Recurrent Neural Network; Sequence Classification
• ISSN: 2214-7853; 2214-7853
• DOI: 10.1016/j.matpr.2022.02.395

Artificial intelligence and deep learning are becoming an inevitable part of our life. Deep learning models are giving contributions to the identification of genetic causes behind various diseases affecting the human community. The prognosis and diagnosis of such diseases can be recommended with the help of artificial intelligence. Here we are proposing a novel deep learning model that employs the legendary deep learning architectures such as convolutional neural network and the variant of recurrent neural network known as long short-term memory network to classify the protein sequences belonging to the five-member polo like kinase family, a subclass of Serine-Threonine kinases, which is considered as an active anti-cancer drug target these days. The proposed deep learning model was trained and tested on sequences collected from biological sequence databases and classified the new sequences to their corresponding classes with an accuracy of 97.6%. Furthermore, this model could untangle the efforts associated with sequence annotation and classification, which is always a tedious and exigent task.