deep learning、genomics、convolutional neural networkIn recent years,deep learning has been widely used in diverse fields of research,such as speech recognition,image classification,autonomous driving and natura
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...
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...
Genome Biology is calling for submissions to our Collection on the employment of LLMs in biology in areas such as genomics, transcriptomics, and proteomics, to decode complex biological data and predict genetic variations, and advance our understanding of disease mechanisms.Submit...
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 ...
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...
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...