Data management/Annotation software tools | Cryo-electron microscopy image analysis
Find and compare the best bioinformatics software tools for navigating, sharing and collaboratively annotating massive image data sets of biological specimens acquired by cryo-electron microscopy. Tools are ranked by the biomedical research community.
Hosts heterogeneous tools dedicated to neuroimaging research. BrainVISA aims to help researchers in developing new neuroimaging tools, sharing data and distributing software. It offers a way to define viewers which may use any visualization software. Thanks to its data management functions, the tool can define the data types handled by the software, associate key attributes for indexation, and filename patterns to make the link between the filesystem and the database schema.
Assists users in the fitting and building of atomic models. CCP-EM was developed to provides support for individual scientists to a coherent cryoEM community. It can aid scientists in their use of cryoEM software. It also supports software developers in producing and disseminating robust and user-friendly programs. This application provides generic tools for manipulating and visualizing image and volume data.
An open-source, rich web environment to enable highly collaborative analysis of multi-gigapixel imaging data. Cytomine (i) provides remote and collaborative principles, (ii) relies on data models that allow to easily organize and semantically annotate imaging datasets in a standardized way, (iii) efficiently supports high-resolution multi-gigapixel images, (iv) provides mechanisms to readily proofread and share image quantifications produced by machine learning-based image recognition algorithms.
Imputes missing values in large-scale high-dimensional phenome data. phenomeImpute contains four variations of K-nearest-neighbor (KNN) methods and was compared with two existing methods, multivariate imputation by chained equations and missForest. The four variations are imputation by variables (KNN-V), by subjects (KNN-S), their weighted hybrid (KNN-H) and an adaptively weighted hybrid (KNN-A). The results show that Imputation of missing values with low imputability measures increased imputation errors greatly and could potentially deteriorate downstream analyses.
You can access more results by creating a free plan account or unlimited content via a premium account.