Bioterrorism

SWnet: a deep learning model for drug response prediction from cancer genomic signatures and compound chemical structures.

BACKGROUND: One of the major challenges in precision medicine is accurate prediction of individual p...

Toxicity evaluation and oxidative stress response of fumaronitrile, a persistent organic pollutant (POP) of industrial waste water on tilapia fish (Oreochromis mossambicus).

The study was designed to determine the impact of acute toxicity of fumaronitrile exposure through t...

Response to the letter to the editor "Comments on 'Artificial intelligence applications in restorative dentistry: A systematic review'".

A Bioinspired Stress-Response Strategy for High-Speed Soft Grippers.

The stress-response strategy is one of the nature's greatest developments, enabling animals and plan...

Combining computed tomography and biologically effective dose in radiomics and deep learning improves prediction of tumor response to robotic lung stereotactic body radiation therapy.

PURPOSE: The aim of this study is to improve the performance of machine learning (ML) models in pred...

Redefining β-blocker response in heart failure patients with sinus rhythm and atrial fibrillation: a machine learning cluster analysis.

BACKGROUND: Mortality remains unacceptably high in patients with heart failure and reduced left vent...

Implementation of a standardized robotic assistant surgical training curriculum.

Since 2000, robotic-assisted surgery has rapidly expanded into almost every surgical sub-specialty. ...

Systematic Review of the Effectiveness of Machine Learning Algorithms for Classifying Pain Intensity, Phenotype or Treatment Outcomes Using Electroencephalogram Data.

Recent attempts to utilize machine learning (ML) to predict pain-related outcomes from Electroenceph...

An approach to rapidly assess sepsis through multi-biomarker host response using machine learning algorithm.

Sepsis is a life-threatening condition and understanding the disease pathophysiology through the use...

Deep learning-based predictive biomarker of pathological complete response to neoadjuvant chemotherapy from histological images in breast cancer.

BACKGROUND: Pathological complete response (pCR) is considered a surrogate endpoint for favorable su...

Accounting for symptom heterogeneity can improve neuroimaging models of antidepressant response after electroconvulsive therapy.

Depression symptom heterogeneity limits the identifiability of treatment-response biomarkers. Whethe...

Machine Learning for personalised stress detection: Inter-individual variability of EEG-ECG markers for acute-stress response.

Stress appears as a response for a broad variety of physiological stimuli. It does vary among indivi...

Study becomes insight: Ecological learning from machine learning.

The ecological and environmental science communities have embraced machine learning (ML) for empiric...

Machine learning predicts treatment sensitivity in multiple myeloma based on molecular and clinical information coupled with drug response.

Providing treatment sensitivity stratification at the time of cancer diagnosis allows better allocat...

Graph-theory based degree centrality combined with machine learning algorithms can predict response to treatment with antiepileptic medications in children with epilepsy.

BACKGROUND AND PURPOSE: The purpose of the current study is to detect changes of graph-theory-based ...

Machine learning reveals mesenchymal breast carcinoma cell adaptation in response to matrix stiffness.

Epithelial-mesenchymal transition (EMT) and its reverse process, mesenchymal-epithelial transition (...

Optimization of diosgenin extraction from Dioscorea deltoidea tubers using response surface methodology and artificial neural network modelling.

INTRODUCTION: Dioscorea deltoidea var. deltoidea (Dioscoreaceae) is a valuable endangered plant of g...

[Artificial intelligence, radiomics and pathomics to predict response and survival of patients treated with radiations].

Artificial intelligence approaches in medicine are more and more used and are extremely promising du...

Systems biology informed neural networks (SBINN) predict response and novel combinations for PD-1 checkpoint blockade.

Anti-PD-1 immunotherapy has recently shown tremendous success for the treatment of several aggressiv...

Modeling the response of ecological service value to land use change through deep learning simulation in Lanzhou, China.

Land use (LU) changes caused by urbanization, climate, and anthropogenic activities alter the supply...

Bone mineral density response prediction following osteoporosis treatment using machine learning to aid personalized therapy.

Osteoporosis is a global health problem for ageing populations. The goals of osteoporosis treatment ...