AIMC Topic: Neural Networks, Computer

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Using deep learning convolutional neural networks to automatically perform cerebral aqueduct CSF flow analysis.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
Since the development of phase-contrast magnetic resonance imaging (PC-MRI), quantification of cerebrospinal fluid (CSF) flow across the cerebral aqueduct has been utilized for diagnosis of conditions such as normal pressure hydrocephalus (NPH). This...

Spatially Constrained Context-Aware Hierarchical Deep Correlation Filters for Nucleus Detection in Histology Images.

Medical image analysis
Nucleus detection in histology images is a fundamental step for cellular-level analysis in computational pathology. In clinical practice, quantitative nuclear morphology can be used for diagnostic decision making, prognostic stratification, and treat...

Physics-informed deep learning for prediction of CO storage site response.

Journal of contaminant hydrology
Accurate prediction of the CO plume migration and pressure is imperative for safe operation and economic management of carbon storage projects. Numerical reservoir simulations of CO flow could be used for this purpose allowing the operators and stake...

DeepSIRT: A deep neural network for identification of sirtuin targets and their subcellular localizations.

Computational biology and chemistry
Sirtuins are a family of proteins that play a key role in regulating a wide range of cellular processes including DNA regulation, metabolism, aging/longevity, cell survival, apoptosis, and stress resistance. Sirtuins are protein deacetylases and incl...

Convolutional neural networks (CNNs): concepts and applications in pharmacogenomics.

Molecular diversity
Convolutional neural networks (CNNs) have been used to extract information from various datasets of different dimensions. This approach has led to accurate interpretations in several subfields of biological research, like pharmacogenomics, addressing...

Causal relationship extraction from biomedical text using deep neural models: A comprehensive survey.

Journal of biomedical informatics
The identification of causal relationships between events or entities within biomedical texts is of great importance for creating scientific knowledge bases and is also a fundamental natural language processing (NLP) task. A causal (cause-effect) rel...

Can methods of artificial intelligence aid in optimizing patient selection in patients undergoing intrauterine inseminations?

Journal of assisted reproduction and genetics
PURPOSE: AI and its machine learning algorithms have proven useful in several fields of medicine, including medically assisted reproduction. The purpose of the study was to construct several predictive models based on clinical data and select the bes...

Automated Generation of Novel Fragments Using Screening Data, a Dual SMILES Autoencoder, Transfer Learning and Syntax Correction.

Journal of chemical information and modeling
Fragment-based hit identification (FBHI) allows proportionately greater coverage of chemical space using fewer molecules than traditional high-throughput screening approaches. However, effectively exploiting this advantage is highly dependent on the ...

Deep Learning-Based Advances in Protein Structure Prediction.

International journal of molecular sciences
Obtaining an accurate description of protein structure is a fundamental step toward understanding the underpinning of biology. Although recent advances in experimental approaches have greatly enhanced our capabilities to experimentally determine prot...

A continuous-time neurodynamic approach and its discretization for distributed convex optimization over multi-agent systems.

Neural networks : the official journal of the International Neural Network Society
Distributed optimization problem (DOP) over multi-agent systems, which can be described by minimizing the sum of agents' local objective functions, has recently attracted widespread attention owing to its applications in diverse domains. In this pape...