AIMC Topic: Neural Networks, Computer

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Disease gene prediction with privileged information and heteroscedastic dropout.

Bioinformatics (Oxford, England)
MOTIVATION: Recently, machine learning models have achieved tremendous success in prioritizing candidate genes for genetic diseases. These models are able to accurately quantify the similarity among disease and genes based on the intuition that simil...

On the feasibility of deep learning applications using raw mass spectrometry data.

Bioinformatics (Oxford, England)
SUMMARY: In recent years, SWATH-MS has become the proteomic method of choice for data-independent-acquisition, as it enables high proteome coverage, accuracy and reproducibility. However, data analysis is convoluted and requires prior information and...

CROTON: an automated and variant-aware deep learning framework for predicting CRISPR/Cas9 editing outcomes.

Bioinformatics (Oxford, England)
MOTIVATION: CRISPR/Cas9 is a revolutionary gene-editing technology that has been widely utilized in biology, biotechnology and medicine. CRISPR/Cas9 editing outcomes depend on local DNA sequences at the target site and are thus predictable. However, ...

DeepGraphGO: graph neural network for large-scale, multispecies protein function prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Automated function prediction (AFP) of proteins is a large-scale multi-label classification problem. Two limitations of most network-based methods for AFP are (i) a single model must be trained for each species and (ii) protein sequence i...

Convolutional neural networks can decode eye movement data: A black box approach to predicting task from eye movements.

Journal of vision
Previous attempts to classify task from eye movement data have relied on model architectures designed to emulate theoretically defined cognitive processes and/or data that have been processed into aggregate (e.g., fixations, saccades) or statistical ...

Neuroevolutionary Learning of Particles and Protocols for Self-Assembly.

Physical review letters
Within simulations of molecules deposited on a surface we show that neuroevolutionary learning can design particles and time-dependent protocols to promote self-assembly, without input from physical concepts such as thermal equilibrium or mechanical ...

ModFOLD8: accurate global and local quality estimates for 3D protein models.

Nucleic acids research
Methods for estimating the quality of 3D models of proteins are vital tools for driving the acceptance and utility of predicted tertiary structures by the wider bioscience community. Here we describe the significant major updates to ModFOLD, which ha...

Using machine learning to automate ultrasound-based classification of butt-fused joints in medium-density polyethylene gas pipes.

The Journal of the Acoustical Society of America
Polyethylene (PE) pipes are widely used in gas distribution. Their joints are prone to various flaws and are the most problematic part of the pipeline, so the infrastructure industry requires an effective inspection technique. Butt-fusion (BF) is the...

Towards real-time diagnosis for pediatric sepsis using graph neural network and ensemble methods.

European review for medical and pharmacological sciences
OBJECTIVE: The rapid onset of pediatric sepsis and the short optimal time for resuscitation pose a severe threat to children's health in the ICU. Timely diagnosis and intervention are essential to curing sepsis, but there is a lack of research on the...

[Identifying Molecular Subtypes of Whole-Slide Image in Colorectal Cancer via Deep Learning].

Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition
OBJECTIVE: To establish an artificial intelligence-assisted diagnosis system for molecular subtyping of colorectal cancer (CRC).