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A Deep Autoencoder based content retrieval algorithm is proposed forprediction and differentiation of cancer types based on the presence ofepigenetic patterns of DNA methylation identified in genetic regions known asCpG islands. The developed deep learning system uses a CpG island stateclassification sub-system to complete sets of missing/incomplete island data ingiven human cell lines, and is then pipelined with an intricate set ofstatistical and signal processing methods to accurately predict the presence ofcancer and further differentiate the type and cell of origin in the event of apositive result. The proposed system was trained with previously reported dataderived from four case groups of cancer cell lines, achieving overallSensitivity of 88.24%, Specificity of 83.33%, Accuracy of 84.75% and MatthewsCorrelation Coefficient of 0.687. The ability to predict and differentiatecancer types using epigenetic events as the identifying patterns wasdemonstrated in previously reported data sets from breast, lung, lymphoblasticleukemia and urological cancer cell lines, allowing the pipelined system to berobust and adjustable to other cancer cell lines or epigenetic events.

A Deep Autoencoder System for Differentiation of Cancer Types Based on DNA Methylation State