Prescriptions

Robotic Endoscope Control - State of the Art of Voice Control and Other Options for Laparoscopic Camera Robot Guidance.

In video-based surgery (VBS), the surgeon is no longer the master of his or her view. Visualization of the surgical field is mediated by a video camera guided by an assistant. The separation of visualization and surgical activities leads to significant drawbacks. Early in the history of VBS, active camera holders controlled directly by the surgeon were introduced to replace the human camera assist...

SPLDExtraTrees: robust machine learning approach for predicting kinase inhibitor resistance.

Drug resistance is a major threat to the global health and a significant concern throughout the clinical treatment of diseases and drug development. The mutation in proteins that is related to drug binding is a common cause for adaptive drug resistance. Therefore, quantitative estimations of how mutations would affect the interaction between a drug and the target protein would be of vital signific...

RNAI-FRID: novel feature representation method with information enhancement and dimension reduction for RNA-RNA interaction.

Different ribonucleic acids (RNAs) can interact to form regulatory networks that play important role in many life activities. Molecular biology experi...

DTI-HETA: prediction of drug-target interactions based on GCN and GAT on heterogeneous graph.

Drug-target interaction (DTI) prediction plays an important role in drug repositioning, drug discovery and drug design. However, due to the large size...

Attention-based Knowledge Graph Representation Learning for Predicting Drug-drug Interactions.

Drug-drug interactions (DDIs) are known as the main cause of life-threatening adverse events, and their identification is a key task in drug developme...

Integrating specific and common topologies of heterogeneous graphs and pairwise attributes for drug-related side effect prediction.

MOTIVATION: Computerized methods for drug-related side effect identification can help reduce costs and speed up drug development. Multisource data abo...

GraphTGI: an attention-based graph embedding model for predicting TF-target gene interactions.

MOTIVATION: Interaction between transcription factor (TF) and its target genes establishes the knowledge foundation for biological researches in trans...

3DGT-DDI: 3D graph and text based neural network for drug-drug interaction prediction.

MOTIVATION: Drug-drug interactions (DDIs) occur during the combination of drugs. Identifying potential DDI helps us to study the mechanism behind the ...

Boost-RS: boosted embeddings for recommender systems and its application to enzyme-substrate interaction prediction.

MOTIVATION: Despite experimental and curation efforts, the extent of enzyme promiscuity on substrates continues to be largely unexplored and under doc...

SynPathy: Predicting Drug Synergy through Drug-Associated Pathways Using Deep Learning.

UNLABELLED: Drug combination therapy has become a promising therapeutic strategy for cancer treatment. While high-throughput drug combination screenin...

Death by Robots? Automation and Working-Age Mortality in the United States.

The decline of manufacturing employment is frequently invoked as a key cause of worsening U.S. population health trends, including rising mortality du...

Prediction of Subjective Refraction From Anterior Corneal Surface, Eye Lengths, and Age Using Machine Learning Algorithms.

PURPOSE: To develop a machine learning regression model of subjective refractive prescription from minimum ocular biometry and corneal topography feat...

Artificial Intelligence in Oncology: Current Capabilities, Future Opportunities, and Ethical Considerations.

The promise of highly personalized oncology care using artificial intelligence (AI) technologies has been forecasted since the emergence of the field....

Reconstruction of human protein-coding gene functional association network based on machine learning.

Networks consisting of molecular interactions are intrinsically dynamical systems of an organism. These interactions curated in molecular interaction ...

PRODeepSyn: predicting anticancer synergistic drug combinations by embedding cell lines with protein-protein interaction network.

Although drug combinations in cancer treatment appear to be a promising therapeutic strategy with respect to monotherapy, it is arduous to discover ne...

An enhanced cascade-based deep forest model for drug combination prediction.

Combination therapy has shown an obvious curative effect on complex diseases, whereas the search space of drug combinations is too large to be validat...

ALDPI: adaptively learning importance of multi-scale topologies and multi-modality similarities for drug-protein interaction prediction.

MOTIVATION: Effective computational methods to predict drug-protein interactions (DPIs) are vital for drug discovery in reducing the time and cost of ...

Identifying drug-target interactions via heterogeneous graph attention networks combined with cross-modal similarities.

Accurate identification of drug-target interactions (DTIs) plays a crucial role in drug discovery. Compared with traditional experimental methods that...

Big data and artificial intelligence (AI) methodologies for computer-aided drug design (CADD).

There have been numerous advances in the development of computational and statistical methods and applications of big data and artificial intelligence...

Explainable multimodal machine learning model for classifying pregnancy drug safety.

MOTIVATION: Teratogenic drugs can cause severe fetal malformation and therefore have critical impact on the health of the fetus, yet the teratogenic r...