Prescriptions

GHS-NET a generic hybridized shallow neural network for multi-label biomedical text classification.

Exponential growth of biomedical literature and clinical data demands more robust yet precise computational methodologies to extract useful insights from biomedical literature and to perform accurate assignment of disease-specific codes. Such approaches can largely enhance the effectiveness of diverse biomedicine and bioinformatics applications. State-of-the-art computational biomedical text class...

Sensitivity analysis for interpretation of machine learning based segmentation models in cardiac MRI.

BACKGROUND: Image segmentation is a common task in medical imaging e.g., for volumetry analysis in cardiac MRI. Artificial neural networks are used to automate this task with performance similar to manual operators. However, this performance is only achieved in the narrow tasks networks are trained on. Performance drops dramatically when data characteristics differ from the training set properties...

Artificial intelligence, machine learning, and drug repurposing in cancer.

: Drug repurposing provides a cost-effective strategy to re-use approved drugs for new medical indications. Several machine learning (ML) and artifici...

TranSynergy: Mechanism-driven interpretable deep neural network for the synergistic prediction and pathway deconvolution of drug combinations.

Drug combinations have demonstrated great potential in cancer treatments. They alleviate drug resistance and improve therapeutic efficacy. The fast-gr...

Complementary Pseudo Labels for Unsupervised Domain Adaptation On Person Re-Identification.

In recent years, supervised person re-identification (re-ID) models have received increasing studies. However, these models trained on the source doma...

Modulation of the dynamics of cerebellar Purkinje cells through the interaction of excitatory and inhibitory feedforward pathways.

The dynamics of cerebellar neuronal networks is controlled by the underlying building blocks of neurons and synapses between them. For which, the comp...

Cerebral blood flow measurements with O-water PET using a non-invasive machine-learning-derived arterial input function.

Cerebral blood flow (CBF) can be measured with dynamic positron emission tomography (PET) of O-labeled water by using tracer kinetic modelling. Howeve...

Learning Atomic Interactions through Solvation Free Energy Prediction Using Graph Neural Networks.

Solvation free energy is a fundamental property that influences various chemical and biological processes, such as reaction rates, protein folding, dr...

Hierarchical Long Short-Term Concurrent Memory for Human Interaction Recognition.

In this work, we aim to address the problem of human interaction recognition in videos by exploring the long-term inter-related dynamics among multipl...

Validating Intelligent Automation Systems in Pharmacovigilance: Insights from Good Manufacturing Practices.

Pharmacovigilance is the science of monitoring the effects of medicinal products to identify and evaluate potential adverse reactions and provide nece...

Machine learning model for predicting severity prognosis in patients infected with COVID-19: Study protocol from COVID-AI Brasil.

The new coronavirus, which began to be called SARS-CoV-2, is a single-stranded RNA beta coronavirus, initially identified in Wuhan (Hubei province, Ch...

A computational framework for optimal control of a self-adjustive neural system with activity-dependent and homeostatic plasticity.

The control of the brain system has received increasing attention in the domain of brain science. Most brain control studies have been conducted to ex...

SSnet: A Deep Learning Approach for Protein-Ligand Interaction Prediction.

Computational prediction of Protein-Ligand Interaction (PLI) is an important step in the modern drug discovery pipeline as it mitigates the cost, time...

An eight-camera fall detection system using human fall pattern recognition via machine learning by a low-cost android box.

Falls are a leading cause of unintentional injuries and can result in devastating disabilities and fatalities when left undetected and not treated in ...

Identification of drug combinations on the basis of machine learning to maximize anti-aging effects.

Aging is a multifactorial process that involves numerous genetic changes, so identifying anti-aging agents is quite challenging. Age-associated geneti...

Automatically classifying the evidence type of drug-drug interaction research papers as a step toward computer supported evidence curation.

A longstanding issue with knowledge bases that discuss drug-drug interactions (DDIs) is that they are inconsistent with one another. Computerized supp...

Predicting Tumor Cell Response to Synergistic Drug Combinations Using a Novel Simplified Deep Learning Model.

Drug combinations targeting multiple targets/pathways are believed to be able to reduce drug resistance. Computational models are essential for novel ...

Leveraging digital media data for pharmacovigilance.

The development of novel drugs in response to changing clinical requirements is a complex and costly method with uncertain outcomes. Postmarket pharma...

MaskLayer: Enabling scalable deep learning solutions by training embedded feature sets.

Deep learning-based methods have shown to achieve excellent results in a variety of domains, however, some important assets are absent. Quality scalab...

Use of artificial intelligence to enhance phenotypic drug discovery.

Research and development (R&D) productivity across the pharmaceutical industry has received close scrutiny over the past two decades, especially takin...