Browse
Submit Data
Databases
API
Help

Dataset Information

25 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Prediction of lipid nanoparticles for mRNA vaccines by the machine learning algorithm.

ABSTRACT: Lipid nanoparticle (LNP) is commonly used to deliver mRNA vaccines. Currently, LNP optimization primarily relies on screening ionizable lipids by traditional experiments which consumes intensive cost and time. Current study attempts to apply computational methods to accelerate the LNP development for mRNA vaccines. Firstly, 325 data samples of mRNA vaccine LNP formulations with IgG titer were collected. The machine learning algorithm, lightGBM, was used to build a prediction model with good performance (R ² > 0.87). More importantly, the critical substructures of ionizable lipids in LNPs were identified by the algorithm, which well agreed with published results. The animal experimental results showed that LNP using DLin-MC3-DMA (MC3) as ionizable lipid with an N/P ratio at 6:1 induced higher efficiency in mice than LNP with SM-102, which was consistent with the model prediction. Molecular dynamic modeling further investigated the molecular mechanism of LNPs used in the experiment. The result showed that the lipid molecules aggregated to form LNPs, and mRNA molecules twined around the LNPs. In summary, the machine learning predictive model for LNP-based mRNA vaccines was first developed, validated by experiments, and further integrated with molecular modeling. The prediction model can be used for virtual screening of LNP formulations in the future.

SUBMITTER: Wang W

PROVIDER: S-EPMC9214321 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Prediction of Breast Cancer Estrogen Receptor Status using Machine Learning

Project description:Gene expression profiles were generated from 199 primary breast cancer patients. Samples 1-176 were used in another study, GEO Series GSE22820, and form the training data set in this study. Sample numbers 200-222 form a validation set. This data is used to model a machine learning classifier for Estrogen Receptor Status. RNA was isolated from 199 primary breast cancer patients. A machine learning classifier was built to predict ER status using only three gene features.

2013-01-01 | E-GEOD-29210 | biostudies-arrayexpress

Optimization of Lipid Nanoparticles for Intramuscular Administration of mRNA Vaccines.

Project description:mRNA vaccines have the potential to tackle many unmet medical needs that are unable to be addressed with conventional vaccine technologies. A potent and well-tolerated delivery technology is integral to fully realizing the potential of mRNA vaccines. Pre-clinical and clinical studies have demonstrated that mRNA delivered intramuscularly (IM) with first-generation lipid nanoparticles (LNPs) generates robust immune responses. Despite progress made over the past several years, there remains significant opportunity for improvement, as the most advanced LNPs were designed for intravenous (IV) delivery of siRNA to the liver. Here, we screened a panel of proprietary biodegradable ionizable lipids for both expression and immunogenicity in a rodent model when administered IM. A subset of compounds was selected and further evaluated for tolerability, immunogenicity, and expression in rodents and non-human primates (NHPs). A lead formulation was identified that yielded a robust immune response with improved tolerability. More importantly for vaccines, increased innate immune stimulation driven by LNPs does not equate to increased immunogenicity, illustrating that mRNA vaccine tolerability can be improved without affecting potency.

| S-EPMC6383180 | biostudies-literature

Early Prediction of Sepsis Based on Machine Learning Algorithm.

Project description:Sepsis is an organ failure disease caused by an infection resulting in extremely high mortality. Machine learning algorithms XGBoost and LightGBM are applied to construct two processing methods: mean processing method and feature generation method, aiming to predict early sepsis 6 hours in advance. The feature generation methods are constructed by combining different features, including statistical strength features, window features, and medical features. Miceforest multiple interpolation method is applied to tackle large missing data problems. Results show that the feature generation method outperforms the mean processing method. XGBoost and LightGBM algorithms are both excellent in prediction performance (AUC: 0.910∼0.979), among which LightGBM boasts a faster running speed and is stronger in generalization ability especially on multidimensional data, with AUC reaching 0.979 in the feature generation method. PTT, WBC, and platelets are the key risk factors to predict early sepsis.

| S-EPMC8526252 | biostudies-literature

Lipid nanoparticles in the development of mRNA vaccines for COVID-19.

Project description:Coronavirus disease (COVID-19) was first reported in December 2019, Hubei Province, China. As on 9th December 2021, severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) has affected 266018810 people worldwide with 5265092 deaths. The outbreak of COVID-19 pandemic has caused severe public health crisis across the world. Nucleic acids have been emerging as potential drugs to treat a variety of diseases. Lipid nanoparticles (LNPs) have great potential to deliver nucleic acids including mRNAs. The two mRNA-based vaccines namely the BNT162b2 (Pfizer-BioNTech) and mRNA-1273 (Moderna) have been given emergency use authorization (EUA) by the US-FDA to prevent SARS-CoV-2 caused COVID-19 and the vaccines were developed using LNPs. This article focuses on the potential application of LNPs in the development and delivery of mRNA vaccines for COVID-19.

| S-EPMC9238147 | biostudies-literature

An evolutionary machine learning algorithm for cardiovascular disease risk prediction.

Project description:IntroductionThis study developed a novel risk assessment model to predict the occurrence of cardiovascular disease (CVD) events. It uses a Genetic Algorithm (GA) to develop an easy-to-use model with high accuracy, calibrated based on the Isfahan Cohort Study (ICS) database.MethodsThe ICS was a population-based prospective cohort study of 6,504 healthy Iranian adults aged ≥ 35 years followed for incident CVD over ten years, from 2001 to 2010. To develop a risk score, the problem of predicting CVD was solved using a well-designed GA, and finally, the results were compared with classic machine learning (ML) and statistical methods.ResultsA number of risk scores such as the WHO, and PARS models were utilized as the baseline for comparison due to their similar chart-based models. The Framingham and PROCAM models were also applied to the dataset, with the area under a Receiver Operating Characteristic curve (AUROC) equal to 0.633 and 0.683, respectively. However, the more complex Deep Learning model using a three-layered Convolutional Neural Network (CNN) performed best among the ML models, with an AUROC of 0.74, and the GA-based eXplanaible Persian Atherosclerotic CVD Risk Stratification (XPARS) showed higher performance compared to the statistical methods. XPARS with eight features showed an AUROC of 0.76, and the XPARS with four features, showed an AUROC of 0.72.ConclusionA risk model that is extracted using GA substantially improves the prediction of CVD compared to conventional methods. It is clear, interpretable and can be a suitable replacement for conventional statistical methods.

| S-EPMC9333440 | biostudies-literature

Lipid Nanoparticles as Promising Carriers for mRNA Vaccines for Viral Lung Infections.

Project description:In recent years, there has been an increase in deaths due to infectious diseases, most notably in the context of viral respiratory pathogens. Consequently, the focus has shifted in the search for new therapies, with attention being drawn to the use of nanoparticles in mRNA vaccines for targeted delivery to improve the efficacy of these vaccines. Notably, mRNA vaccine technologies denote as a new era in vaccination due to their rapid, potentially inexpensive, and scalable development. Although they do not pose a risk of integration into the genome and are not produced from infectious elements, they do pose challenges, including exposing naked mRNAs to extracellular endonucleases. Therefore, with the development of nanotechnology, we can further improve their efficacy. Nanoparticles, with their nanometer dimensions, move more freely in the body and, due to their small size, have unique physical and chemical properties. The best candidates for vaccine mRNA transfer are lipid nanoparticles (LNPs), which are stable and biocompatible and contain four components: cationic lipids, ionizable lipids, polyethylene glycols (PEGs), and cholesterol, which are used to facilitate cytoplasmic mRNA delivery. In this article, the components and delivery system of mRNA-LNP vaccines against viral lung infections such as influenza, coronavirus, and respiratory syncytial virus are reviewed. Moreover, we provide a succinct overview of current challenges and potential future directions in the field.

| S-EPMC10146241 | biostudies-literature

Prediction of Pseudoprogression versus Progression using Machine Learning Algorithm in Glioblastoma.

Project description:We aimed to investigate the feasibility of machine learning (ML) algorithm to distinguish pseudoprogression (PsPD) from progression (PD) in patients with glioblastoma (GBM). We recruited the patients diagnosed as primary GBM who received gross total resection (GTR) and concurrent chemoradiotherapy in two institutions from April 2010 to April 2017 and presented suspicious contrast-enhanced lesion on brain magnetic resonance imaging (MRI) during follow-up. Patients from two institutions were allocated to training (N = 59) and testing (N = 19) datasets, respectively. We developed a convolutional neural network combined with a long short-term memory ML structure. MRI data, which was 9 axial post-contrast T1-weighted images in our study, and clinical features were incorporated (Model 1). In the testing set, the trained Model 1 resulted in AUC of 0.83, AUPRC of 0.87, and F1-score of 0.74 using optimal threshold. The performance was superior to that of Model 2 (CNN-LSTM model with MRI data alone) and Model 3 (random forest model with clinical feature alone). The developed algorithm involving MRI data and clinical features could help making decision during follow-up of patients with GBM treated with GTR and concurrent CCRT.

| S-EPMC6104063 | biostudies-literature

Accurate age prediction from blood using a small set of DNA methylation sites and a cohort-based machine learning algorithm

Project description:Chronological age prediction from DNA methylation sheds light on human aging, indicates poor health and predicts lifespan. Previous studies developed methylation clocks based on linear regression models on methylation array data. While accurate, these models are limited to fixed-rate changes in methylation levels across age. Moreover, the high cost of methylation arrays, compared to targeted-PCR sequencing, hinders widespread utility of such predictors. We present an AI-based alternative termed GP-age, which uses a non-parametric approach based on Gaussian Process Regression of a large cohort of ~12K blood methylomes. Given a new blood sample, methylation levels are compared to the cohort samples, which are then weighted to predict the query age. Using only 30 CpG sites, our approach outperforms state-of-the-art methylation clocks that use hundreds of sites, with a median error of 2.1 years (on held-out data). Our model was also applied to sequencing-based data yielding highly accurate predictions. Overall, we provide an accessible alternative to current array-based methylation clocks, with future applications in aging research, forensic profiling, and monitoring epigenetic processes in transplantation medicine and cancer.

2023-01-20 | GSE207605 | GEO

ANGLOR: a composite machine-learning algorithm for protein backbone torsion angle prediction.

Project description:We developed a composite machine-learning based algorithm, called ANGLOR, to predict real-value protein backbone torsion angles from amino acid sequences. The input features of ANGLOR include sequence profiles, predicted secondary structure and solvent accessibility. In a large-scale benchmarking test, the mean absolute error (MAE) of the phi/psi prediction is 28 degrees/46 degrees , which is approximately 10% lower than that generated by software in literature. The prediction is statistically different from a random predictor (or a purely secondary-structure-based predictor) with p-value <1.0 x 10(-300) (or <1.0 x 10(-148)) by Wilcoxon signed rank test. For some residues (ILE, LEU, PRO and VAL) and especially the residues in helix and buried regions, the MAE of phi angles is much smaller (10-20 degrees ) than that in other environments. Thus, although the average accuracy of the ANGLOR prediction is still low, the portion of the accurately predicted dihedral angles may be useful in assisting protein fold recognition and ab initio 3D structure modeling.

| S-EPMC2559866 | biostudies-literature

Machine Learning Prediction of Foodborne Disease Pathogens: Algorithm Development and Validation Study.

Project description:BackgroundFoodborne diseases, as a type of disease with a high global incidence, place a heavy burden on public health and social economy. Foodborne pathogens, as the main factor of foodborne diseases, play an important role in the treatment and prevention of foodborne diseases; however, foodborne diseases caused by different pathogens lack specificity in clinical features, and there is a low proportion of clinically actual pathogen detection in real life.ObjectiveWe aimed to analyze foodborne disease case data, select appropriate features based on analysis results, and use machine learning methods to classify foodborne disease pathogens to predict foodborne disease pathogens that have not been tested.MethodsWe extracted features such as space, time, and exposed food from foodborne disease case data and analyzed the relationship between these features and the foodborne disease pathogens using a variety of machine learning methods to classify foodborne disease pathogens. We compared the results of 4 models to obtain the pathogen prediction model with the highest accuracy.ResultsThe gradient boost decision tree model obtained the highest accuracy, with accuracy approaching 69% in identifying 4 pathogens including Salmonella, Norovirus, Escherichia coli, and Vibrio parahaemolyticus. By evaluating the importance of features such as time of illness, geographical longitude and latitude, and diarrhea frequency, we found that they play important roles in classifying the foodborne disease pathogens.ConclusionsData analysis can reflect the distribution of some features of foodborne diseases and the relationship among the features. The classification of pathogens based on the analysis results and machine learning methods can provide beneficial support for clinical auxiliary diagnosis and treatment of foodborne diseases.

| S-EPMC7872834 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data