AIMC Topic: Neural Networks, Computer

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CellProfiler Analyst 3.0: accessible data exploration and machine learning for image analysis.

Bioinformatics (Oxford, England)
SUMMARY: Image-based experiments can yield many thousands of individual measurements describing each object of interest, such as cells in microscopy screens. CellProfiler Analyst is a free, open-source software package designed for the exploration of...

RefRGim: an intelligent reference panel reconstruction method for genotype imputation with convolutional neural networks.

Briefings in bioinformatics
Genotype imputation is a statistical method for estimating missing genotypes from a denser haplotype reference panel. Existing methods usually performed well on common variants, but they may not be ideal for low-frequency and rare variants. Previous ...

DeepFeature: feature selection in nonimage data using convolutional neural network.

Briefings in bioinformatics
Artificial intelligence methods offer exciting new capabilities for the discovery of biological mechanisms from raw data because they are able to detect vastly more complex patterns of association that cannot be captured by classical statistical test...

iDeepSubMito: identification of protein submitochondrial localization with deep learning.

Briefings in bioinformatics
Mitochondria are membrane-bound organelles containing over 1000 different proteins involved in mitochondrial function, gene expression and metabolic processes. Accurate localization of those proteins in the mitochondrial compartments is critical to t...

Addressing data imbalance problems in ligand-binding site prediction using a variational autoencoder and a convolutional neural network.

Briefings in bioinformatics
Since 2015, a fast growing number of deep learning-based methods have been proposed for protein-ligand binding site prediction and many have achieved promising performance. These methods, however, neglect the imbalanced nature of binding site predict...

Deep learning methods for biomedical named entity recognition: a survey and qualitative comparison.

Briefings in bioinformatics
The biomedical literature is growing rapidly, and the extraction of meaningful information from the large amount of literature is increasingly important. Biomedical named entity (BioNE) identification is one of the critical and fundamental tasks in b...

Improved prediction of protein-protein interaction using a hybrid of functional-link Siamese neural network and gradient boosting machines.

Briefings in bioinformatics
In this paper, for accurate prediction of protein-protein interaction (PPI), a novel hybrid classifier is developed by combining the functional-link Siamese neural network (FSNN) with the light gradient boosting machine (LGBM) classifier. The hybrid ...

Improving protein fold recognition using triplet network and ensemble deep learning.

Briefings in bioinformatics
Protein fold recognition is a critical step toward protein structure and function prediction, aiming at providing the most likely fold type of the query protein. In recent years, the development of deep learning (DL) technique has led to massive adva...

Identifying complex motifs in massive omics data with a variable-convolutional layer in deep neural network.

Briefings in bioinformatics
Motif identification is among the most common and essential computational tasks for bioinformatics and genomics. Here we proposed a novel convolutional layer for deep neural network, named variable convolutional (vConv) layer, for effective motif ide...

phyLoSTM: a novel deep learning model on disease prediction from longitudinal microbiome data.

Bioinformatics (Oxford, England)
MOTIVATION: Research shows that human microbiome is highly dynamic on longitudinal timescales, changing dynamically with diet, or due to medical interventions. In this article, we propose a novel deep learning framework 'phyLoSTM', using a combinatio...