Prescriptions

LDAPred: A Method Based on Information Flow Propagation and a Convolutional Neural Network for the Prediction of Disease-Associated lncRNAs.

Long non-coding RNAs (lncRNAs) play a crucial role in the pathogenesis and development of complex diseases. Predicting potential lncRNA-disease associations can improve our understanding of the molecular mechanisms of human diseases and help identify biomarkers for disease diagnosis, treatment, and prevention. Previous research methods have mostly integrated the similarity and association informat...

Modeling place cells and grid cells in multi-compartment environments: Entorhinal-hippocampal loop as a multisensory integration circuit.

Hippocampal place cells and entorhinal grid cells are thought to form a representation of space by integrating internal and external sensory cues. Experimental data show that different subsets of place cells are controlled by vision, self-motion or a combination of both. Moreover, recent studies in environments with a high degree of visual aliasing suggest that a continuous interaction between pla...

Predicting Drug-Target Interaction Using a Novel Graph Neural Network with 3D Structure-Embedded Graph Representation.

We propose a novel deep learning approach for predicting drug-target interaction using a graph neural network. We introduce a distance-aware graph att...

EK-DRD: A Comprehensive Database for Drug Repositioning Inspired by Experimental Knowledge.

Drug repositioning, or the identification of new indications for approved therapeutic drugs, has gained substantial traction with both academics and p...

A novel approach recommendation for hazard analysis.

Hazard evaluation generally defines the ranking of hazards in the work environment and ignores the interaction of hazards. This article aims to overco...

Predicting Drug-Disease Associations via Using Gaussian Interaction Profile and Kernel-Based Autoencoder.

Computational drug repositioning, designed to identify new indications for existing drugs, significantly reduced the cost and time involved in drug de...

Prediction of Potential Drug-Disease Associations through Deep Integration of Diversity and Projections of Various Drug Features.

Identifying new indications for existing drugs may reduce costs and expedites drug development. Drug-related disease predictions typically combined he...

Delfos: deep learning model for prediction of solvation free energies in generic organic solvents.

Prediction of aqueous solubilities or hydration free energies is an extensively studied area in machine learning applications in chemistry since water...

Visual Interaction with Deep Learning Models through Collaborative Semantic Inference.

Automation of tasks can have critical consequences when humans lose agency over decision processes. Deep learning models are particularly susceptible ...

Stochasticity from function - Why the Bayesian brain may need no noise.

An increasing body of evidence suggests that the trial-to-trial variability of spiking activity in the brain is not mere noise, but rather the reflect...

Using scalp EEG and intracranial EEG signals for predicting epileptic seizures: Review of available methodologies.

Patients suffering from epileptic seizures are usually treated with medication and/or surgical procedures. However, in more than 30% of cases, medicat...

A parallel method for accelerating visualization and interactivity for vector tiles.

Vector tile technology is developing rapidly and has received increasing attention in recent years. Compared to the raster tile, the vector tile has s...

Assisted therapeutic system based on reinforcement learning for children with autism.

Assisted therapy is increasingly used in autism spectrum disorders (ASD) for improving social interaction and communication skills in recent years. A ...

Network as a Biomarker: A Novel Network-Based Sparse Bayesian Machine for Pathway-Driven Drug Response Prediction.

With the advances in different biological networks including gene regulation, gene co-expression, protein-protein interaction networks, and advanced a...

Novel deep learning model for more accurate prediction of drug-drug interaction effects.

BACKGROUND: Predicting the effect of drug-drug interactions (DDIs) precisely is important for safer and more effective drug co-prescription. Many comp...

Episodic Memory in Minicolumn Associative Knowledge Graphs.

A generalization of active neural associative knowledge graphs (ANAKGs) to their minicolumn form is presented in this paper. Each minicolumn represent...

Semi-Supervised Learning Algorithm for Identifying High-Priority Drug-Drug Interactions Through Adverse Event Reports.

Identifying drug-drug interactions (DDIs) is a critical enabler for reducing adverse drug events and improving patient safety. Generating proper DDI a...

Artificial Intelligence for Drug Toxicity and Safety.

Interventional pharmacology is one of medicine's most potent weapons against disease. These drugs, however, can result in damaging side effects and mu...

Detecting drug-drug interactions using artificial neural networks and classic graph similarity measures.

Drug-drug interactions are preventable causes of medical injuries and often result in doctor and emergency room visits. Computational techniques can b...

Probabilistic associative learning suffices for learning the temporal structure of multiple sequences.

From memorizing a musical tune to navigating a well known route, many of our underlying behaviors have a strong temporal component. While the mechanis...