AIMC Topic: Machine Learning

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Biological modelling of a computational spiking neural network with neuronal avalanches.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
In recent years, an increasing number of studies have demonstrated that networks in the brain can self-organize into a critical state where dynamics exhibit a mixture of ordered and disordered patterns. This critical branching phenomenon is termed ne...

Automatic phase aberration compensation for digital holographic microscopy based on deep learning background detection.

Optics express
We propose a fully automatic technique to obtain aberration free quantitative phase imaging in digital holographic microscopy (DHM) based on deep learning. The traditional DHM solves the phase aberration compensation problem by manually detecting the...

DiagnoProt: a tool for discovery of new molecules by mass spectrometry.

Bioinformatics (Oxford, England)
MOTIVATION: Around 75% of all mass spectra remain unidentified by widely adopted proteomic strategies. We present DiagnoProt, an integrated computational environment that can efficiently cluster millions of spectra and use machine learning to shortli...

HIPred: an integrative approach to predicting haploinsufficient genes.

Bioinformatics (Oxford, England)
MOTIVATION: A major cause of autosomal dominant disease is haploinsufficiency, whereby a single copy of a gene is not sufficient to maintain the normal function of the gene. A large proportion of existing methods for predicting haploinsufficiency inc...

Prediction of Anti-VEGF Treatment Requirements in Neovascular AMD Using a Machine Learning Approach.

Investigative ophthalmology & visual science
PURPOSE: The purpose of this study was to predict low and high anti-VEGF injection requirements during a pro re nata (PRN) treatment, based on sets of optical coherence tomography (OCT) images acquired during the initiation phase in neovascular AMD.

Auditory feature representation using convolutional restricted Boltzmann machine and Teager energy operator for speech recognition.

The Journal of the Acoustical Society of America
In this letter, authors propose an auditory feature representation technique with the filterbank learned using an annealing dropout convolutional restricted Boltzmann machine (ConvRBM) and noise-robust energy estimation using the Teager energy operat...