Bioterrorism

Modelling of bioprocess non-linear fluorescence data for at-line prediction of etanercept based on artificial neural networks optimized by response surface methodology.

In the last years, regulatory agencies in biopharmaceutical industry have promoted the design and im...

Ensemble Learning for Early-Response Prediction of Antidepressant Treatment in Major Depressive Disorder.

BACKGROUND: In order to reduce unsuccessful treatment trials for depression, neuroimaging and geneti...

Machine learning for the detection of early immunological markers as predictors of multi-organ dysfunction.

The immune response to major trauma has been analysed mainly within post-hospital admission settings...

Unsupervised Machine Learning of Topics Documented by Nurses about Hospitalized Patients Prior to a Rapid-Response Event.

BACKGROUND: In the hospital setting, it is crucial to identify patients at risk for deterioration be...

Propagation of uncertainty in the mechanical and biological response of growing tissues using multi-fidelity Gaussian process regression.

A key feature of living tissues is their capacity to remodel and grow in response to environmental c...

Radical systems thinking and the future role of computational modelling in ergonomics: an exploration of agent-based modelling.

We are teetering on the precipice of the imminent Fourth Industrial Revolution. In this new age, sys...

Machine learning with multiparametric magnetic resonance imaging of the breast for early prediction of response to neoadjuvant chemotherapy.

In patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy (NAC), some pati...

Prediction of lithium response using clinical data.

OBJECTIVE: Promptly establishing maintenance therapy could reduce morbidity and mortality in patient...

Response surface methodology and artificial neural network approach for the optimization of ultrasound-assisted extraction of polyphenols from garlic.

This paper aimed to establish the optimal conditions for ultrasound-assisted extraction of polypheno...

ARPNet: Antidepressant Response Prediction Network for Major Depressive Disorder.

Treating patients with major depressive disorder is challenging because it takes several months for ...

Enhancement of multianalyte mass spectrometry detection through response surface optimization by least squares and artificial neural network modelling.

In this work, the use of design of experiments and posterior data modelling by artificial neural net...

[Prediction of chemotherapy response in primary osteosarcoma using the machine learning technique on radiomic data].

INTRODUCTION: Osteosarcoma is the most common malignant bone tumor before 25 years of age. Response ...

A machine learning algorithm predicts molecular subtypes in pancreatic ductal adenocarcinoma with differential response to gemcitabine-based versus FOLFIRINOX chemotherapy.

PURPOSE: Development of a supervised machine-learning model capable of predicting clinically relevan...

Predicting short- and long-term glycated haemoglobin response after insulin initiation in patients with type 2 diabetes mellitus using machine-learning algorithms.

AIM: To assess the potential of supervised machine-learning techniques to identify clinical variable...

A comparison of machine learning algorithms for the surveillance of autism spectrum disorder.

OBJECTIVE: The Centers for Disease Control and Prevention (CDC) coordinates a labor-intensive proces...

Response to: Comment on "Assessing Effectiveness and Costs in Robot-Mediated Lower Limbs Rehabilitation: A Meta-Analysis and State of the Art".

Operation-aware Neural Networks for user response prediction.

User response prediction makes a crucial contribution to the rapid development of online advertising...

Visual Evoked Response Modulation Occurs in a Complementary Manner Under Dynamic Circuit Framework.

The steady-state visual-evoked potential (SSVEP) induced by the periodic visual stimulus plays an im...

Response to: 'On using machine learning algorithms to define clinically meaningful patient subgroups' by Knevel and Huizinga.

Lessons learned from the application of machine learning to studies on plant response to radio-frequency.

This paper applies Machine Learning (ML) algorithms to peer-reviewed publications in order to discer...

Network as a Biomarker: A Novel Network-Based Sparse Bayesian Machine for Pathway-Driven Drug Response Prediction.

With the advances in different biological networks including gene regulation, gene co-expression, pr...