:With the rapid growth of biological information, biological science technology has greatly enriched the biology
and medicine data resources. The latest advantages of deep learning have achieved the state-or-the-art performance on
high dimensional, non-structural and less explanatory biological data. The aim of this paper is to provide an overview of
deep learning techniques and some of the-state-of-art applications in biology and medicine field. Specifically, we
introduce the fundamental of deep learning methods, and then review their successes to bioinformatics, biomedical image,
biomedicine and drug discovery. We also discuss the challenges, limitations and further improvement of this area.
Background:
The emergence of generative adversarial networks (GANs) has provided a new technology and
framework for the application of medical images. Specifically, a GAN requires little to no labeled data to obtain highquality data that can be generated through competition between the generator and discriminator networks. Therefore,
GANs are rapidly proving to be a state-of-the-art foundation, achieving enhanced performances in various medical
applications.
Methods:
In this article, we introduce the principles of GANs and their various variants, deep convolutional GAN,
conditional GAN, Wasserstein GAN, Info-GAN, boundary equilibrium GAN, and cycle-GAN.
Results:
All various GANs have found success in medical imaging tasks, including medical image enhancement,
segmentation, classification, reconstruction, and synthesis. Furthermore, we summarize the data processing methods and
evaluation indicators. Finally, we note the limitations of existing methods and the existing challenges that need to be
addressed in this field.
Conclusion:
Although GANs are in initial stage of development in medical image processing, it will have a great prospect
in the future.
Background
Recently, erdafitinib (Balversa), the first targeted therapy drug for genetic alteration, was approved to metastatic urothelial carcinoma. Cancer genomics research has been greatly encouraged. Currently, a large number of gene regulatory networks between different states have been constructed, which can reveal the difference states of genes. However, they have not been applied to the subtypes of Muscle-invasive bladder cancer (MIBC).
Results
In this paper, we propose a method that construct gene regulatory networks under different molecular subtypes of MIBC, and analyse the regulatory differences between different molecular subtypes. Through differential expression analysis and the differential network analysis of the top 100 differential genes in the network, we find that SERPINI1, NOTUM, FGFR1 and other genes have significant differences in expression and regulatory relationship between MIBC subtypes.
Conclusions
Furthermore, pathway enrichment analysis and differential network analysis demonstrate that Neuroactive ligand-receptor interaction and Cytokine-cytokine receptor interaction are significantly enriched pathways, and the genes contained in them are significant diversity in the subtypes of bladder cancer.
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