Machine intelligence for precision oncology

Figure 1 Artificial intelligence, machine learning, and deep learning. Artificial intelligence is a field of applied computer science that mimics human cognition to complete a task. Machine learning is a branch of artificial intelligence that manually extract features from input data to create a model that categorizes the object. Neural network is a set of machine-based learning algorithms to learn labeled datasets and perform classification tasks, and it comprises an input layer, a hidden layer of interconnected nodes, and an output layer. Deep learning is a machine learning technique that uses neural network architectures, and the term “deep” refers to the number of hidden layers (more than three) in the neural network.

Figure 2 A neural network in machine learning. Neural network is a set of machine-based learning algorithms, and it comprises an input layer (red circles), a hidden layer of interconnected nodes (blue circles), and output layer (green circle). The function of a neural network is to extract and process features from labeled datasets and perform classification tasks. A representative hidden layer of interconnected nodes (blue circles) is shown. A deep learning node (blue circle) is a computational unit that combines input data with weights (assigned significance) and generate an output layer. For deep learning, a neural network contains more than three hidden layers.

Figure 3 A neural network in deep learning of radiological images. The input consists of data derived from radiological images from individuals with cancer or without cancer. The output includes detection and classification of tumors.

Figure 4 A neural network in deep learning of digital histopathology. The input consists of digital histopathological data derived from individuals with or without cancer. The output includes classification and diagnosis of cancer as well as predicting genetic mutations and prognosis of patients with cancer.

Figure 5 A neural network in deep learning of cancer-derived multi-omics. The input consists of multi-omics data derived from various cancers. The output includes classification and risk stratification of cancer, predicting prognosis, and investigating gene regulation/biomarkers/biological mechanism, as well as drug discovery and development.

Figure 6 Multi-modal deep learning for precision oncology. The input comprises clinicopathological datasets from healthy individuals and patients with cancers. The output includes a variety of predicted outcomes that form the basis of precision oncology.

Figure 7 Machine intelligence in translational cancer research for precision oncology.