Bioterrorism

A subcomponent-guided deep learning method for interpretable cancer drug response prediction.

Accurate prediction of cancer drug response (CDR) is a longstanding challenge in modern oncology that underpins personalized treatment. Current computational methods implement CDR prediction by modeling responses between entire drugs and cell lines, without the consideration that response outcomes may primarily attribute to a few finer-level 'subcomponents', such as privileged substructures of the...

Non-invasive tumor microenvironment evaluation and treatment response prediction in gastric cancer using deep learning radiomics.

The tumor microenvironment (TME) plays a critical role in disease progression and is a key determinant of therapeutic response in cancer patients. Here, we propose a noninvasive approach to predict the TME status from radiological images by combining radiomics and deep learning analyses. Using multi-institution cohorts of 2,686 patients with gastric cancer, we show that the radiological model accu...

A performance evaluation of drug response prediction models for individual drugs.

Drug response prediction is important to establish personalized medicine for cancer therapy. Model construction for predicting drug response (i.e., ce...

Prediction of lymphoma response to CAR T cells by deep learning-based image analysis.

Clinical prognostic scoring systems have limited utility for predicting treatment outcomes in lymphomas. We therefore tested the feasibility of a deep...

Auto-Segmentation and Classification of Glioma Tumors with the Goals of Treatment Response Assessment Using Deep Learning Based on Magnetic Resonance Imaging.

Glioma is the most common primary intracranial neoplasm in adults. Radiotherapy is a treatment approach in glioma patients, and Magnetic Resonance Ima...

Cancer immunotherapy response prediction from multi-modal clinical and image data using semi-supervised deep learning.

BACKGROUND AND PURPOSE: Immunotherapy is a standard treatment for many tumor types. However, only a small proportion of patients derive clinical benef...

Can People With Higher Versus Lower Scores on Impression Management or Self-Monitoring Be Identified Through Different Traces Under Faking?

According to faking models, personality variables and faking are related. Most prominently, people's tendency to try to make an appropriate impression...

Predicting the antigenic evolution of SARS-COV-2 with deep learning.

The relentless evolution of SARS-CoV-2 poses a significant threat to public health, as it adapts to immune pressure from vaccines and natural infectio...

A deep learning predictive model for public health concerns and hesitancy toward the COVID-19 vaccines.

Throughout the pandemic era, COVID-19 was one of the remarkable unexpected situations over the past few years, but with the decentralization and globa...

Development of a program to determine optimal settings for robot-assisted rehabilitation of the post-stroke paretic upper extremity: a simulation study.

Robot-assisted therapy can effectively treat upper extremity (UE) paralysis in patients who experience a stroke. Presently, UE, as a training item, is...

Patient groups in Rheumatoid arthritis identified by deep learning respond differently to biologic or targeted synthetic DMARDs.

Cycling of biologic or targeted synthetic disease modifying antirheumatic drugs (b/tsDMARDs) in rheumatoid arthritis (RA) patients due to non-response...

Biologically Interpretable Deep Learning To Predict Response to Immunotherapy In Advanced Melanoma Using Mutations and Copy Number Variations.

Only 30-40% of advanced melanoma patients respond effectively to immunotherapy in clinical practice, so it is necessary to accurately identify the res...

Animal disease surveillance: How to represent textual data for classifying epidemiological information.

The value of informal sources in increasing the timeliness of disease outbreak detection and providing detailed epidemiological information in the ear...

Assessment of stress response due to C-Mac D-blade guided videolaryngoscopic endotracheal intubation and docking of da Vinci surgical robot using perfusion index in patients undergoing transoral robotic oncosurgery.

Clinical utility of perfusion index (PI) has entered a new realm as a non-invasive, quantitative index of stress response to endotracheal intubation. ...

Neural network based integration of assays to assess pathogenic potential.

Limited data significantly hinders our capability of biothreat assessment of novel bacterial strains. Integration of data from additional sources that...

Precision engineering of biological function with large-scale measurements and machine learning.

As synthetic biology expands and accelerates into real-world applications, methods for quantitatively and precisely engineering biological function be...

Molecular and functional imaging in cancer-targeted therapy: current applications and future directions.

Targeted anticancer drugs block cancer cell growth by interfering with specific signaling pathways vital to carcinogenesis and tumor growth rather tha...

Using Deep Learning to Predict Treatment Response in Patients with Hepatocellular Carcinoma Treated with Y90 Radiation Segmentectomy.

Treatment of hepatocellular carcinoma (HCC) with Y90 radioembolization segmentectomy (Y90-RE) demonstrates a tumor dose-response threshold, where dose...

Energy saving strategy of cloud data computing based on convolutional neural network and policy gradient algorithm.

Cloud Data Computing (CDC) is conducive to precise energy-saving management of user data centers based on the real-time energy consumption monitoring ...

Analysis and estimation of cross-flow heat exchanger fouling in phosphoric acid concentration plant using response surface methodology (RSM) and artificial neural network (ANN).

The production of phosphoric acid by dehydrated process leads to the precipitation of unwanted insoluble salts promoting thus the crystallization foul...