AIMC Topic: Animals

Clear Filters Showing 8451 to 8460 of 8866 articles

DeepSF: deep convolutional neural network for mapping protein sequences to folds.

Bioinformatics (Oxford, England)
MOTIVATION: Protein fold recognition is an important problem in structural bioinformatics. Almost all traditional fold recognition methods use sequence (homology) comparison to indirectly predict the fold of a target protein based on the fold of a te...

SANA NetGO: a combinatorial approach to using Gene Ontology (GO) terms to score network alignments.

Bioinformatics (Oxford, England)
MOTIVATION: Gene Ontology (GO) terms are frequently used to score alignments between protein-protein interaction (PPI) networks. Methods exist to measure GO similarity between proteins in isolation, but proteins in a network alignment are not isolate...

BIG DATA ANALYTICS AND PRECISION ANIMAL AGRICULTURE SYMPOSIUM: Machine learning and data mining advance predictive big data analysis in precision animal agriculture.

Journal of animal science
Precision animal agriculture is poised to rise to prominence in the livestock enterprise in the domains of management, production, welfare, sustainability, health surveillance, and environmental footprint. Considerable progress has been made in the u...

Object Recognition: Complexity of Recognition Strategies.

Current biology : CB
Primate brains and state-of-the-art convolutional neural networks can recognize many faces, objects and scenes, though how they do so is often mysterious. New research unveils some of the mystery, revealing unexpected complexity in the recognition st...

A Real-Time Reconfigurable Multichip Architecture for Large-Scale Biophysically Accurate Neuron Simulation.

IEEE transactions on biomedical circuits and systems
Simulation of brain neurons in real-time using biophysically meaningful models is a prerequisite for comprehensive understanding of how neurons process information and communicate with each other, in effect efficiently complementing in-vivo experimen...

Superior arm-movement decoding from cortex with a new, unsupervised-learning algorithm.

Journal of neural engineering
OBJECTIVE: The aim of this work is to improve the state of the art for motor-control with a brain-machine interface (BMI). BMIs use neurological recording devices and decoding algorithms to transform brain activity directly into real-time control of ...

SATORI: a system for ontology-guided visual exploration of biomedical data repositories.

Bioinformatics (Oxford, England)
MOTIVATION: The ever-increasing number of biomedical datasets provides tremendous opportunities for re-use but current data repositories provide limited means of exploration apart from text-based search. Ontological metadata annotations provide conte...

Deep learning for tumor classification in imaging mass spectrometry.

Bioinformatics (Oxford, England)
MOTIVATION: Tumor classification using imaging mass spectrometry (IMS) data has a high potential for future applications in pathology. Due to the complexity and size of the data, automated feature extraction and classification steps are required to f...

A review and experimental study on the application of classifiers and evolutionary algorithms in EEG-based brain-machine interface systems.

Journal of neural engineering
OBJECTIVE: Considering the importance and the near-future development of noninvasive brain-machine interface (BMI) systems, this paper presents a comprehensive theoretical-experimental survey on the classification and evolutionary methods for BMI-bas...

Research progress in machine learning methods for gene-gene interaction detection.

Yi chuan = Hereditas
Complex diseases are results of gene-gene and gene-environment interactions. However, the detection of high-dimensional gene-gene interactions is computationally challenging. In the last two decades, machine-learning approaches have been developed to...