AIMC Topic: Deep Learning

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Deep image reconstruction from human brain activity.

PLoS computational biology
The mental contents of perception and imagery are thought to be encoded in hierarchical representations in the brain, but previous attempts to visualize perceptual contents have failed to capitalize on multiple levels of the hierarchy, leaving it cha...

Exploring Tunable Hyperparameters for Deep Neural Networks with Industrial ADME Data Sets.

Journal of chemical information and modeling
Deep learning has drawn significant attention in different areas including drug discovery. It has been proposed that it could outperform other machine learning algorithms, especially with big data sets. In the field of pharmaceutical industry, machin...

Integration of A Deep Learning Classifier with A Random Forest Approach for Predicting Malonylation Sites.

Genomics, proteomics & bioinformatics
As a newly-identified protein post-translational modification, malonylation is involved in a variety of biological functions. Recognizing malonylation sites in substrates represents an initial but crucial step in elucidating the molecular mechanisms ...

CorNET: Deep Learning Framework for PPG-Based Heart Rate Estimation and Biometric Identification in Ambulant Environment.

IEEE transactions on biomedical circuits and systems
Advancements in wireless sensor network technologies have enabled the proliferation of miniaturized body-worn sensors, capable of long-term pervasive biomedical signal monitoring. Remote cardiovascular monitoring has been one of the beneficiaries of ...

Improving the Quality of Synthetic FLAIR Images with Deep Learning Using a Conditional Generative Adversarial Network for Pixel-by-Pixel Image Translation.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Synthetic FLAIR images are of lower quality than conventional FLAIR images. Here, we aimed to improve the synthetic FLAIR image quality using deep learning with pixel-by-pixel translation through conditional generative adversa...

Deep learning for image analysis: Personalizing medicine closer to the point of care.

Critical reviews in clinical laboratory sciences
The precision-based revolution in medicine continues to demand stratification of patients into smaller and more personalized subgroups. While genomic technologies have largely led this movement, diagnostic results can take days to weeks to generate. ...

Dominant-Current Deep Learning Scheme for Electrical Impedance Tomography.

IEEE transactions on bio-medical engineering
OBJECTIVE: Deep learning has recently been applied to electrical impedance tomography (EIT) imaging. Nevertheless, there are still many challenges that this approach has to face, e.g., targets with sharp corners or edges cannot be well recovered when...

Utilizing dynamic treatment information for MACE prediction of acute coronary syndrome.

BMC medical informatics and decision making
BACKGROUND: Main adverse cardiac events (MACE) are essentially composite endpoints for assessing safety and efficacy of treatment processes of acute coronary syndrome (ACS) patients. Timely prediction of MACE is highly valuable for improving the effe...

Alternative Diagnosis of Epilepsy in Children Without Epileptiform Discharges Using Deep Convolutional Neural Networks.

International journal of neural systems
Numerous nonepileptic paroxysmal events, such as syncope and psychogenic nonepileptic seizures, may imitate seizures and impede diagnosis. Misdiagnosis can lead to mistreatment, affecting patients' lives considerably. Electroencephalography is common...

DeepCDpred: Inter-residue distance and contact prediction for improved prediction of protein structure.

PloS one
Rapid, accurate prediction of protein structure from amino acid sequence would accelerate fields as diverse as drug discovery, synthetic biology and disease diagnosis. Massively improved prediction of protein structures has been driven by improving t...