Leveraging well-annotated databases for deep learning in biomedical research

Transl Cancer Res 2020;9(12):7682-7684 | http://dx.doi.org/10.21037/tcr-20-3163 Buzzwords indicate popular trends in research fields. These terms might last for decades or perish in just a few years (1). Over the last ten years, we have witnessed the rise of a big buzzword-deep learning (DL) (2-7). In brief, DL is a subdomain of artificial intelligence (AI), a type of representation learning, which automatically finds features in a data and transforms them into a higher abstract data based on matrix operations (3). There are various types of DL algorithms such as convolutional neural networks (8), recurrent neural networks (9), long short-term memory networks (10), convolutional deep belief networks (11), generative adversarial networks (12), and deep residual networks (13), just to name a few. Depending on the specific task/problem, one could use these networks individually or combine them into a pipeline. The biggest advantage of DL algorithms is that they can be trained without pre-defined features/ variables, which is especially convenient for complicated data types, such as biomedical images or sequencing data, that are time-consuming and computationally expensive and require a high level of human expertise for feature selection (3). Moreover, high-end facilities such as graphical processing units, central processing units, and randomaccess memory are needed for processing and training such data within a reasonable amount of time. A growing body of research related to neural network applications for solving problems in the biomedical field includes diverse research topics that commonly leverage big data. This includes biomedical images and multi-omics datasets either from public domain or in-house data from different populations (6,14-16). Biomedical images can be in 2-dimensional (2D) format such as pathological images, or 3-dimensional (3D) such as with mammography images, computed tomography scans, and magnetic resonance imaging (17-22). A single scanned image could be split into hundreds to several thousands of smaller images, which easily complies with the data demands of neural network training. The data formats for multi-omics data is even more complicated and are highly dependent on the manufacturing platforms. The omics data, such as genomics (sequencing data) (23), transcriptomics (sequencing and expression data) (24,25), proteomics (mass spectrometry data) (26), and metabolomics (metabolite compounds) (27), can be used for DL models as long as the number of samples and features is suitable for training and can achieve acceptable accuracy. From only a single run, these highthroughput platforms can generate thousands to millions of data points from each sample. Integrating these could provide an unprecedentedly comprehensive data to study the complicated diseases such as cancer (28) or human brain diseases (29). Therefore, this is a golden era for data-driven research, not only due to the huge amount of publicly available datasets, but also because of the rapid development of modern algorithms and giant technology corporations such as Google (TensorFlow and CoLab cloud computing) (30-32), Amazon (Amazon Web Services) (33), and Facebook (PyTorch) (34) and their platforms and cloud computing services. With such favorable conditions and the available open-source environments of the DL Editorial Commentary