Deep learning-driven multi-omics analysis: enhancing cancer diagnostics and therapeutics

Abstract

Artificial intelligence (AI) excels at efficiently processing large volumes of data and extracting valuable insights. Deep Learning (DL), a subfield of AI, utilizes multi-layer neural network algorithms to analyze various types of data, mimicking the neural network architecture of the human brain. One of the most prominent features of DL is its end-to-end learning mechanism, which excels at automatic feature extraction and pattern recognition in data. As multi-omics technologies rapidly evolve, the volume of omics data from cancer samples has surged, presenting a significant challenge in managing this vast amount of information. Due to its strong data processing capabilities, DL is increasingly applied across a range of cancer research areas, such as early detection and screening, diagnosis, molecular subtype classification, discovery of biomarkers, and predicting patient prognosis and treatment responses. DL integrates high-dimensional data from fields such as genomics, epigenomics, transcriptomics, proteomics, radiomics, and single-cell omics, enhancing our understanding of cancer development and advancing personalized treatment approaches. This paper reviews various DL models and their roles in analyzing complex data patterns, providing a review of DL applications in cancer multi-omics analysis research and emphasizing its potential in early detection, diagnosis, classification, and prognosis prediction. As DL models are introduced continuously, we expect their application in cancer research to become more extensive, thus propelling the advancement of cancer medicine.

Keywords: artificial intelligence; cancer early detection; deep learning; multi-omics; tumor biomarker.

Figure 1

The connections among artificial intelligence (AI), machine learning (ML), and deep learning (DL), along with several types of neural network models in deep learning (by BioRender). AI is capable of simulating the informational processes of human consciousness and cognition, constructing models by learning and identifying patterns and relationships between inputs and outputs, and then computing and outputting results for new input data. AI excels at processing large datasets, efficiently identifying valuable information, patterns, and structures. Machine learning is a subfield of AI, typically divided into supervised learning, unsupervised learning, and reinforcement learning. ML allows machines to automatically learn from data and identify patterns through algorithms, enabling them to make decisions or predictions. DL, a subfield of ML, uses multi-layer neural network architectures to analyze various data types and generate prediction outcomes through an end-to-end learning process. The main deep learning models include multilayer perceptron, convolutional neural network, recurrent neural network and long short-term memory network, autoencoder, graph convolutional network, and transformer architecture (by BioRender).

Figure 2

Workflow for integrating multi-omics data through DL. The complete workflow consists of six major steps: data preprocessing, feature selection or dimensionality reduction, data integration, deep learning model development, data analysis, and result validation. Initially, the multi-omics data undergoes cleaning, followed by feature selection and dimensionality reduction techniques to refine the dataset. Subsequently, the data variables from various omics need to be consolidated into a comprehensive dataset for further analysis. Following that, a suitable deep learning model is chosen according to the data type and task goals, and model training and parameter optimization are conducted. The trained model is utilized to conduct analysis tasks, including classification, regression, and clustering, on the integrated data. Lastly, relevant metrics are chosen to evaluate the model’s performance (by BioRender).

Figure 3

An illustration of multi-omics data encompasses various techniques such as genomics, epigenomics, transcriptomics, proteomics, metabolomics, radiomics, and single-cell omics, each contributing distinct layers of biological information for comprehensive analysis (by BioRender).