AIMC Topic: Neural Networks, Computer

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Computational framework for fusing eye movements and spoken narratives for image annotation.

Journal of vision
Despite many recent advances in the field of computer vision, there remains a disconnect between how computers process images and how humans understand them. To begin to bridge this gap, we propose a framework that integrates human-elicited gaze and ...

Automated human mind reading using EEG signals for seizure detection.

Journal of medical engineering & technology
Epilepsy is one of the most occurring neurological disease globally emerged back in 4000 BC. It is affecting around 50 million people of all ages these days. The trait of this disease is recurrent seizures. In the past few decades, the treatments ava...

Fully interpretable deep learning model of transcriptional control.

Bioinformatics (Oxford, England)
MOTIVATION: The universal expressibility assumption of Deep Neural Networks (DNNs) is the key motivation behind recent worksin the systems biology community to employDNNs to solve important problems in functional genomics and moleculargenetics. Typic...

Graph neural representational learning of RNA secondary structures for predicting RNA-protein interactions.

Bioinformatics (Oxford, England)
MOTIVATION: RNA-protein interactions are key effectors of post-transcriptional regulation. Significant experimental and bioinformatics efforts have been expended on characterizing protein binding mechanisms on the molecular level, and on highlighting...

QDeep: distance-based protein model quality estimation by residue-level ensemble error classifications using stacked deep residual neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Protein model quality estimation, in many ways, informs protein structure prediction. Despite their tight coupling, existing model quality estimation methods do not leverage inter-residue distance information or the latest technological b...

Deep multiple instance learning classifies subtissue locations in mass spectrometry images from tissue-level annotations.

Bioinformatics (Oxford, England)
MOTIVATION: Mass spectrometry imaging (MSI) characterizes the molecular composition of tissues at spatial resolution, and has a strong potential for distinguishing tissue types, or disease states. This can be achieved by supervised classification, wh...

EvoLSTM: context-dependent models of sequence evolution using a sequence-to-sequence LSTM.

Bioinformatics (Oxford, England)
MOTIVATION: Accurate probabilistic models of sequence evolution are essential for a wide variety of bioinformatics tasks, including sequence alignment and phylogenetic inference. The ability to realistically simulate sequence evolution is also at the...

Network-principled deep generative models for designing drug combinations as graph sets.

Bioinformatics (Oxford, England)
MOTIVATION: Combination therapy has shown to improve therapeutic efficacy while reducing side effects. Importantly, it has become an indispensable strategy to overcome resistance in antibiotics, antimicrobials and anticancer drugs. Facing enormous ch...

Imputing missing RNA-sequencing data from DNA methylation by using a transfer learning-based neural network.

GigaScience
BACKGROUND: Gene expression plays a key intermediate role in linking molecular features at the DNA level and phenotype. However, owing to various limitations in experiments, the RNA-seq data are missing in many samples while there exist high-quality ...

An Improved Performance of Deep Learning Based on Convolution Neural Network to Classify the Hand Motion by Evaluating Hyper Parameter.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
High accuracy in pattern recognition based on electromyography(EMG) contributes to the effectiveness of prosthetics hand development. This study aimed to improve performance and simplify the deep learning pre-processing based on the convolution neura...