  Mutations in proto-oncogenes (ONGO) and the loss of regulatory function of
tumor suppression genes (TSG) are the common underlying mechanism for
uncontrolled tumor growth. While cancer is a heterogeneous complex of distinct
diseases, finding the potentiality of the genes related functionality to ONGO
or TSG through computational studies can help develop drugs that target the
disease. This paper proposes a classification method that starts with a
preprocessing stage to extract the feature map sets from the input 3D protein
structural information. The next stage is a deep convolutional neural network
stage (DCNN) that outputs the probability of functional classification of
genes. We explored and tested two approaches: in Approach 1, all filtered and
cleaned 3D-protein-structures (PDB) are pooled together, whereas in Approach 2,
the primary structures and their corresponding PDBs are separated according to
the genes' primary structural information. Following the DCNN stage, a dynamic
programming-based method is used to determine the final prediction of the
primary structures' functionality. We validated our proposed method using the
COSMIC online database. For the ONGO vs TSG classification problem, the AUROC
of the DCNN stage for Approach 1 and Approach 2 DCNN are 0.978 and 0.765,
respectively. The AUROCs of the final genes' primary structure functionality
classification for Approach 1 and Approach 2 are 0.989, and 0.879,
respectively. For comparison, the current state-of-the-art reported AUROC is
0.924.
