A Deep Autoencoder System for Differentiation of Cancer Types Based on DNA Methylation State
This work addresses cancer diagnosis by enabling type differentiation based on epigenetic patterns, but it is incremental as it builds on existing deep learning and data processing methods.
The researchers tackled the problem of predicting and differentiating cancer types using DNA methylation patterns, achieving an overall accuracy of 84.75% with sensitivity of 88.24% and specificity of 83.33% on data from four cancer cell lines.
A Deep Autoencoder based content retrieval algorithm is proposed for prediction and differentiation of cancer types based on the presence of epigenetic patterns of DNA methylation identified in genetic regions known as CpG islands. The developed deep learning system uses a CpG island state classification sub-system to complete sets of missing/incomplete island data in given human cell lines, and is then pipelined with an intricate set of statistical and signal processing methods to accurately predict the presence of cancer and further differentiate the type and cell of origin in the event of a positive result. The proposed system was trained with previously reported data derived from four case groups of cancer cell lines, achieving overall Sensitivity of 88.24%, Specificity of 83.33%, Accuracy of 84.75% and Matthews Correlation Coefficient of 0.687. The ability to predict and differentiate cancer types using epigenetic events as the identifying patterns was demonstrated in previously reported data sets from breast, lung, lymphoblastic leukemia and urological cancer cell lines, allowing the pipelined system to be robust and adjustable to other cancer cell lines or epigenetic events.