AIMC Topic: Neural Networks, Computer

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Deep Neural Network and Extreme Gradient Boosting Based Hybrid Classifier for Improved Prediction of Protein-Protein Interaction.

IEEE/ACM transactions on computational biology and bioinformatics
Understanding the behavioral process of life and disease-causing mechanism, knowledge regarding protein-protein interactions (PPI) is essential. In this paper, a novel hybrid approach combining deep neural network (DNN) and extreme gradient boosting ...

Graph Convolutional Networks for Drug Response Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
BACKGROUND: Drug response prediction is an important problem in computational personalized medicine. Many machine-learning-based methods, especially deep learning-based ones, have been proposed for this task. However, these methods often represent th...

Effective Cancer Subtype and Stage Prediction via Dropfeature-DNNs.

IEEE/ACM transactions on computational biology and bioinformatics
Precise cancer subtype and/or stage prediction is instrumental for cancer diagnosis, treatment and management. However, most of the existing methods based on genomic profiles suffer from issues such as overfitting, high computational complexity and s...

A Novel Encoding and Decoding Calibration Guiding Pathway for Pathological Image Analysis.

IEEE/ACM transactions on computational biology and bioinformatics
Diagnostic pathology is the foundation and gold standard for identifying carcinomas, and the accurate quantification of pathological images can provide objective clues for pathologists to make more convincing diagnosis. Recently, the encoder-decoder ...

Predicting Local Protein 3D Structures Using Clustering Deep Recurrent Neural Network.

IEEE/ACM transactions on computational biology and bioinformatics
Since protein 3D structure prediction is very important for biochemical study and drug design, researchers have developed many machine learning algorithms to predict protein 3D structures using the sequence information only. Understanding the sequenc...

An Improved Topology Prediction of Alpha-Helical Transmembrane Protein Based on Deep Multi-Scale Convolutional Neural Network.

IEEE/ACM transactions on computational biology and bioinformatics
Alpha-helical proteins ( αTMPs) are essential in various biological processes. Despite their tertiary structures are crucial for revealing complex functions, experimental structure determination remains challenging and costly. In the past decades, va...

PDGNet: Predicting Disease Genes Using a Deep Neural Network With Multi-View Features.

IEEE/ACM transactions on computational biology and bioinformatics
The knowledge of phenotype-genotype associations is crucial for the understanding of disease mechanisms. Numerous studies have focused on developing efficient and accurate computing approaches to predict disease genes. However, owing to the sparsenes...

Graph Convolutional Autoencoder and Generative Adversarial Network-Based Method for Predicting Drug-Target Interactions.

IEEE/ACM transactions on computational biology and bioinformatics
The computational prediction of novel drug-target interactions (DTIs) may effectively speed up the process of drug repositioning and reduce its costs. Most previous methods integrated multiple kinds of connections about drugs and targets by construct...

Convolutional Embedded Networks for Population Scale Clustering and Bio-Ancestry Inferencing.

IEEE/ACM transactions on computational biology and bioinformatics
The study of genetic variants (GVs) can help find correlating population groups and to identify cohorts that are predisposed to common diseases and explain differences in disease susceptibility and how patients react to drugs. Machine learning techni...

Hybrid Deep Learning Approach for Traffic Speed Prediction.

Big data
Traffic speed prediction plays a fundamental role in traffic management and driving route planning. However, timely accurate traffic speed prediction is challenging as it is affected by complex spatial and temporal correlations. Most existing works c...