ArticleBMC bioinformatics2015
Deep convolutional neural networks for annotating gene expression patterns in the mouse brain.
Article in BMC bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 65 citations in OpenAlex.
- Developmental mouse brain common coordinate framework.Nature communications · 2024Article
- Unsupervised pattern identification in spatial gene expression atlas reveals mouse brain regions beyond established ontology.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- MIGGRI: A multi-instance graph neural network model for inferring gene regulatory networks for Drosophila from spatial expression images.PLoS computational biology · 2023Article
- Developmental Mouse Brain Common Coordinate Framework.bioRxiv : the preprint server for biology · 2023Article
- Transfer learning identifies sequence determinants of cell-type specific regulatory element accessibility.NAR genomics and bioinformatics · 2023Article
- Evaluation of deep convolutional neural networks for in situ hybridization gene expression image representation.PloS one · 2022Article
- On the road to explainable AI in drug-drug interactions prediction: A systematic review.Computational and structural biotechnology journal · 2022Review
- Health Informatics: Engaging Modern Healthcare Units: A Brief Overview.Frontiers in public health · 2022Review
- Predicting environmentally responsive transgenerational differential DNA methylated regions (epimutations) in the genome using a hybrid deep-machine learning approach.BMC bioinformatics · 2021Article
- DeNeRD: high-throughput detection of neurons for brain-wide analysis with deep learning.Scientific reports · 2019Article
- DeepInsight: A methodology to transform a non-image data to an image for convolution neural network architecture.Scientific reports · 2019Article
- Application of Computational Biology to Decode Brain Transcriptomes.Genomics, proteomics & bioinformatics · 2019Review
- Deep learning: new computational modelling techniques for genomics.Nature reviews. Genetics · 2019Review
- Supervised and Unsupervised End-to-End Deep Learning for Gene Ontology Classification of Neural In Situ Hybridization Images.Entropy (Basel, Switzerland) · 2019Article
- Model-Based Control of Soft Actuators Using Learned Non-linear Discrete-Time Models.Frontiers in robotics and AI · 2019Article
- Exploratory Studies Detecting Secondary Structures in Medium Resolution 3D Cryo-EM Images Using Deep Convolutional Neural Networks.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2018Article
- Opportunities and obstacles for deep learning in biology and medicine.Journal of the Royal Society, Interface · 2018Review
- Use of deep neural network ensembles to identify embryonic-fetal transition markers: repression ofOncotarget · 2018Article
- Long short-term memory RNN for biomedical named entity recognition.BMC bioinformatics · 2017Article
- Brain transcriptome atlases: a computational perspective.Brain structure & function · 2017Review
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundProfiling gene expression in brain structures at various spatial and temporal scales is essential to understanding how genes regulate the development of brain structures. The Allen Developing Mouse Brain Atlas provides high-resolution 3-D in situ hybridization (ISH) gene expression patterns in multiple developing stages of the mouse brain. Currently, the ISH images are annotated with anatomical terms manually. In this paper, we propose a computational approach to annotate gene expression pattern images in the mouse brain at various structural levels over the course of development.
resultsWe applied deep convolutional neural network that was trained on a large set of natural images to extract features from the ISH images of developing mouse brain. As a baseline representation, we applied invariant image feature descriptors to capture local statistics from ISH images and used the bag-of-words approach to build image-level representations. Both types of features from multiple ISH image sections of the entire brain were then combined to build 3-D, brain-wide gene expression representations. We employed regularized learning methods for discriminating gene expression patterns in different brain structures. Results show that our approach of using convolutional model as feature extractors achieved superior performance in annotating gene expression patterns at multiple levels of brain structures throughout four developing ages. Overall, we achieved average AUC of 0.894 ± 0.014, as compared with 0.820 ± 0.046 yielded by the bag-of-words approach.
conclusionsDeep convolutional neural network model trained on natural image sets and applied to gene expression pattern annotation tasks yielded superior performance, demonstrating its transfer learning property is applicable to such biological image sets.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.