Then we provided a concise introduction of deep learning applications in genomics and synthetic biology at the levels of DNA, RNA and protein. Finally, we discussed the current challenges and future perspectives of deep learning in genomics.
Genomics, Proteomics & Bioinformatics Volume 20, Issue 5, October 2022, Pages 814-835ReviewApplication of Deep Learning on Single-cell RNA Sequencing Data Analysis: A Review Author links open overlay panelMatthew Brendel 1 2 #, Chang Su 3 #, Zilong Bai 1, Hao Zhang 1, Olivier Elemento 2,...
Recently, the application of deep learning (DL) has made great progress in various fields, especially in cancer research. However, to date, the bibliometric analysis of the application of DL in cancer is scarce. Therefore, this study aimed to explore the research status and hotspots of the app...
of Excellence in Advanced Molecular Imaging and The Centre to Impact AMR (Antimicrobial Resistance). Trained as a bioinformatician and data-savvy biomedical scientist, his research interests span AI, bioinformatics, comparative genomics, cancer genomics, bacterial genomics, computational biomedicine, data ...
Deep learning techniques have considerably improved the field of computer vision, speech recognition, natural language processing, drug discovery and genomics, among other domains. Reinforcement learning (RL) is an area of machine learning concerned with teaching an artificially intelligent (AI) agent ...
We present the successful application of deep learning by Mask R-CNN to maize cob segmentation in the context of genebank phenomics by developing a pipeline written in Python for a large-scale image analysis of highly diverse maize cobs. We also developed a post-processing workflow to automaticall...
Exploration of cancer immunotherapy targets Gene expression analysis Genomics Machine learning Omics Precision medicine Prediction and classification algorithms for cellular/molecular data Protein structure prediction Proteomics Statistical tools and data analytics in cellular/molecular data ...
Methods of Single-Cell Transcriptomics Application of scRNA-seq in Plant Systems Biology Application of Single-Cell Genomics in Plant Synthetic Biology Challenges and Potential Solutions for the Application of scRNA-seq in Plants Conclusion and Perspectives Acknowledgments ReferencesShow full outline Cited by...
Looking towards the future, despite its limitations, ST has the potential to address these problems in conjunction with “dynamics, multi-omics, and resolution”. Ultimately, the development of ST technology, improvement of algorithms, utilization of deep learning, and refinement of the analysis ...
Artificial intelligence has made significant contributions to oncology through the availability of high-dimensional datasets and advances in computing and deep learning. Cancer precision medicine aims to optimize therapeutic outcomes and reduce side effects for individual cancer patients. However, a comprehensi...