AIMC Topic: Machine Learning

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Comparative study of lipid nanoparticle-based mRNA vaccine bioprocess with machine learning and combinatorial artificial neural network-design of experiment approach.

International journal of pharmaceutics
To develop a combinatorial artificial-neural-network design-of-experiment (ANN-DOE) model, the effect of ionizable lipid, an ionizable lipid-to-cholesterol ratio, N/P ratio, flow rate ratio (FRR), and total flow rate (TFR) on the outcome responses of...

On the Early and Affordable Diagnosis of Joint Pathologies Using Acoustic Emissions, Deep Learning Decompositions and Prediction Machines.

Sensors (Basel, Switzerland)
The condition of a joint in a human being is prone to wear and several pathologies, particularly in the elderly and athletes. Current means towards assessing the overall condition of a joint to assess for a pathology involve using tools such as X-ray...

Pharmacophenotype identification of intensive care unit medications using unsupervised cluster analysis of the ICURx common data model.

Critical care (London, England)
BACKGROUND: Identifying patterns within ICU medication regimens may help artificial intelligence algorithms to better predict patient outcomes; however, machine learning methods incorporating medications require further development, including standar...

Enabling personalized perioperative risk prediction by using a machine-learning model based on preoperative data.

Scientific reports
Preoperative risk assessment is essential for shared decision-making and adequate perioperative care. Common scores provide limited predictive quality and lack personalized information. The aim of this study was to create an interpretable machine-lea...

Uncovering expression signatures of synergistic drug responses via ensembles of explainable machine-learning models.

Nature biomedical engineering
Machine learning may aid the choice of optimal combinations of anticancer drugs by explaining the molecular basis of their synergy. By combining accurate models with interpretable insights, explainable machine learning promises to accelerate data-dri...

Building Machine Learning Small Molecule Melting Points and Solubility Models Using CCDC Melting Points Dataset.

Journal of chemical information and modeling
Predicting solubility of small molecules is a very difficult undertaking due to the lack of reliable and consistent experimental solubility data. It is well known that for a molecule in a crystal lattice to be dissolved, it must, first, dissociate fr...

Machine Learning-Based Prognostic Model for Patients After Lung Transplantation.

JAMA network open
IMPORTANCE: Although numerous prognostic factors have been found for patients after lung transplantation (LTx) over the years, an accurate prognostic tool for LTx recipients remains unavailable.

Genomic benchmarks: a collection of datasets for genomic sequence classification.

BMC genomic data
BACKGROUND: Recently, deep neural networks have been successfully applied in many biological fields. In 2020, a deep learning model AlphaFold won the protein folding competition with predicted structures within the error tolerance of experimental met...

Measuring the crop water demand and satisfied degree using remote sensing data and machine learning method in monsoon climatic region, India.

Environmental science and pollution research international
Supply of water is one of the most significant determinants of regional crop production and human food security. To promote sustainable management of agricultural water, the crop water requirement assessment (CropWRA) model was introduced as a tool f...

Survey and Evaluation of Hypertension Machine Learning Research.

Journal of the American Heart Association
Background Machine learning (ML) is pervasive in all fields of research, from automating tasks to complex decision-making. However, applications in different specialities are variable and generally limited. Like other conditions, the number of studie...