AIMC Topic: Neural Networks, Computer

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Utility of deep neural networks in predicting gross-total resection after transsphenoidal surgery for pituitary adenoma: a pilot study.

Neurosurgical focus
OBJECTIVEGross-total resection (GTR) is often the primary surgical goal in transsphenoidal surgery for pituitary adenoma. Existing classifications are effective at predicting GTR but are often hampered by limited discriminatory ability in moderate ca...

A graph-embedded deep feedforward network for disease outcome classification and feature selection using gene expression data.

Bioinformatics (Oxford, England)
MOTIVATION: Gene expression data represents a unique challenge in predictive model building, because of the small number of samples (n) compared with the huge amount of features (p). This 'n≪p' property has hampered application of deep learning techn...

Automatic method to monitor floating macroalgae blooms based on multilayer perceptron: case study of Yellow Sea using GOCI images.

Optics express
Timely and accurate information about floating macroalgae blooms (MAB), including their distribution, movement, and duration, is crucial in order for local government and residents to grasp the whole picture, and then plan effectively to restrain eco...

Protein secondary structure prediction improved by recurrent neural networks integrated with two-dimensional convolutional neural networks.

Journal of bioinformatics and computational biology
Protein secondary structure prediction (PSSP) is an important research field in bioinformatics. The representation of protein sequence features could be treated as a matrix, which includes the amino-acid residue (time-step) dimension and the feature ...

A systematic exploration of [Formula: see text] cutoff ranges in machine learning models for protein mutation stability prediction.

Journal of bioinformatics and computational biology
Discerning how a mutation affects the stability of a protein is central to the study of a wide range of diseases. Mutagenesis experiments on physical proteins provide precise insights about the effects of amino acid substitutions, but such studies ar...

Attractor dynamics of a Boolean model of a brain circuit controlled by multiple parameters.

Chaos (Woodbury, N.Y.)
Studies of Boolean recurrent neural networks are briefly introduced with an emphasis on the attractor dynamics determined by the sequence of distinct attractors observed in the limit cycles. We apply this framework to a simplified model of the basal ...

Propagation delays determine neuronal activity and synaptic connectivity patterns emerging in plastic neuronal networks.

Chaos (Woodbury, N.Y.)
In plastic neuronal networks, the synaptic strengths are adapted to the neuronal activity. Specifically, spike-timing-dependent plasticity (STDP) is a fundamental mechanism that modifies the synaptic strengths based on the relative timing of pre- and...

Global firing rate contrast enhancement in E/I neuronal networks by recurrent synchronized inhibition.

Chaos (Woodbury, N.Y.)
Inhibitory synchronization is commonly observed and may play some important functional roles in excitatory/inhibitory (E/I) neuronal networks. The firing rate contrast enhancement is a general feature of information processing in sensory pathways, an...