AIMC Topic: Computer Graphics

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CirGO: an alternative circular way of visualising gene ontology terms.

BMC bioinformatics
BACKGROUND: Prioritisation of gene ontology terms from differential gene expression analyses in a two-dimensional format remains a challenge with exponentially growing data volumes. Typically, gene ontology terms are represented as tree-maps that enc...

FunMappOne: a tool to hierarchically organize and visually navigate functional gene annotations in multiple experiments.

BMC bioinformatics
BACKGROUND: Functional annotation of genes is an essential step in omics data analysis. Multiple databases and methods are currently available to summarize the functions of sets of genes into higher level representations, such as ontologies and molec...

Incorporation of a spectral model in a convolutional neural network for accelerated spectral fitting.

Magnetic resonance in medicine
PURPOSE: MRSI has shown great promise in the detection and monitoring of neurologic pathologies such as tumor. A necessary component of data processing includes the quantitation of each metabolite, typically done through fitting a model of the spectr...

Fast animal pose estimation using deep neural networks.

Nature methods
The need for automated and efficient systems for tracking full animal pose has increased with the complexity of behavioral data and analyses. Here we introduce LEAP (LEAP estimates animal pose), a deep-learning-based method for predicting the positio...

Machine learning in critical care: state-of-the-art and a sepsis case study.

Biomedical engineering online
BACKGROUND: Like other scientific fields, such as cosmology, high-energy physics, or even the life sciences, medicine and healthcare face the challenge of an extremely quick transformation into data-driven sciences. This challenge entails the dauntin...

SVM-RFE: selection and visualization of the most relevant features through non-linear kernels.

BMC bioinformatics
BACKGROUND: Support vector machines (SVM) are a powerful tool to analyze data with a number of predictors approximately equal or larger than the number of observations. However, originally, application of SVM to analyze biomedical data was limited be...

DGCNN: A convolutional neural network over large-scale labeled graphs.

Neural networks : the official journal of the International Neural Network Society
Exploiting graph-structured data has many real applications in domains including natural language semantics, programming language processing, and malware analysis. A variety of methods has been developed to deal with such data. However, learning grap...

Using predicate and provenance information from a knowledge graph for drug efficacy screening.

Journal of biomedical semantics
BACKGROUND: Biomedical knowledge graphs have become important tools to computationally analyse the comprehensive body of biomedical knowledge. They represent knowledge as subject-predicate-object triples, in which the predicate indicates the relation...

The Vapnik-Chervonenkis dimension of graph and recursive neural networks.

Neural networks : the official journal of the International Neural Network Society
The Vapnik-Chervonenkis dimension (VC-dim) characterizes the sample learning complexity of a classification model and it is often used as an indicator for the generalization capability of a learning method. The VC-dim has been studied on common feed-...

Deep Neural Representation Guided Face Sketch Synthesis.

IEEE transactions on visualization and computer graphics
Face sketch synthesis shows great applications in a lot of fields such as online entertainment and suspects identification. Existing face sketch synthesis methods learn the patch-wise sketch style from the training dataset containing photo-sketch pai...