Prescriptions

Deep learning for drug-drug interaction extraction from the literature: a review.

Drug-drug interactions (DDIs) are crucial for drug research and pharmacovigilance. These interactions may cause adverse drug effects that threaten public health and patient safety. Therefore, the DDIs extraction from biomedical literature has been widely studied and emphasized in modern biomedical research. The previous rules-based and machine learning approaches rely on tedious feature engineerin...

Spectrum of deep learning algorithms in drug discovery.

Deep learning (DL) algorithms are a subset of machine learning algorithms with the aim of modeling complex mapping between a set of elements and their classes. In parallel to the advance in revealing the molecular bases of diseases, a notable innovation has been undertaken to apply DL in data/libraries management, reaction optimizations, differentiating uncertainties, molecule constructions, creat...

Artificial intelligence and machine learning for protein toxicity prediction using proteomics data.

Instead of only focusing on the targeted drug delivery system, researchers have a great interest in developing peptide-based therapies for the procure...

[Synergistic drug combination prediction in multi-input neural network].

Synergistic effects of drug combinations are very important in improving drug efficacy or reducing drug toxicity. However, due to the complex mechanis...

Revealing new therapeutic opportunities through drug target prediction: a class imbalance-tolerant machine learning approach.

MOTIVATION: In silico drug target prediction provides valuable information for drug repurposing, understanding of side effects as well as expansion of...

A multimodal deep learning framework for predicting drug-drug interaction events.

MOTIVATION: Drug-drug interactions (DDIs) are one of the major concerns in pharmaceutical research. Many machine learning based methods have been prop...

Efficient prediction of drug-drug interaction using deep learning models.

A drug-drug interaction or drug synergy is extensively utilised for cancer treatment. However, prediction of drug-drug interaction is defined as an il...

Using Supervised Learning Methods to Develop a List of Prescription Medications of Greatest Concern during Pregnancy.

INTRODUCTION: Women and healthcare providers lack adequate information on medication safety during pregnancy. While resources describing fetal risk ar...

A novel rat robot controlled by electrical stimulation of the nigrostriatal pathway.

OBJECTIVE: Artificial manipulation of animal movement could offer interesting advantages and potential applications using the animal's inherited super...

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

MOTIVATION: Combination therapy has shown to improve therapeutic efficacy while reducing side effects. Importantly, it has become an indispensable str...

Generating X-ray Images from Point Clouds Using Conditional Generative Adversarial Networks.

Simulating medical images such as X-rays is of key interest to reduce radiation in non-diagnostic visualization scenarios. Past state of the art metho...

A Machine Learning Approach to Detecting Low Medication State with Wearable Technologies.

Medication adherence is a critical component and implicit assumption of the patient life cycle that is often violated, incurring financial and medical...

Characterizing Limits of Vision-Based Force Feedback in Simulated Surgical Tool-Tissue Interaction.

Haptic feedback can render real-time force interactions with computer simulated objects. In several telerobotic applications, it is desired that a hap...

A Network-Based Embedding Method for Drug-Target Interaction Prediction.

Integration of multi-omics and pharmacological data can help researchers understand the impact of drugs on dynamic biological systems. Network-based a...

A Preliminary Study of Predicting Effectiveness of Anti-VEGF Injection Using OCT Images Based on Deep Learning.

Deep learning based radiomics have made great progress such as CNN based diagnosis and U-Net based segmentation. However, the prediction of drug effec...

Semiautomated Approach for Muscle Weakness Detection in Clinical Texts.

The automated detection of adverse events in medical records might be a cost-effective solution for patient safety management or pharmacovigilance. Ou...

Emerging Concepts and Applied Machine Learning Research in Patients with Drug-Induced Repolarization Disorders.

The paper presents a review of current research to develop predictive models for automated detection of drug-induced repolarization disorders and show...

Optimizing Nondecomposable Data Dependent Regularizers via Lagrangian Reparameterization Offers Significant Performance and Efficiency Gains.

Data dependent regularization is known to benefit a wide variety of problems in machine learning. Often, these regularizers cannot be easily decompose...

VISAR: an interactive tool for dissecting chemical features learned by deep neural network QSAR models.

SUMMARY: Although many quantitative structure-activity relationship (QSAR) models are trained and evaluated for their predictive merits, understanding...

Leveraging artificial intelligence to monitor unhealthy food and brand marketing to children on digital media.