Additional file 2. Additional details for the external dataset.

Additional file 4: Description of the gamma and c parameters. Fig. S2. This figure shows the decision boundaries and support vectors for different settings of the regularization parameter (C) and Kernel coefficient (gamma). We used the mglearn package to create this figure [27].

In order to overcome the pandemic of COVID-19, messenger RNA (mRNA)-based vaccine has been extensively researched as a rapid and versatile strategy. Herein, we described the immunogenicity of mRNA-based vaccines for Beta and the most recent Omicron variants. The homologous mRNA-Beta and mRNA-Omicron and heterologous Ad5-nCoV plus mRNA vaccine exhibited high-level cross-reactive neutralization for Beta, original, Delta, and Omicron variants. It indicated that the COVID-19 mRNA vaccines have great potential in the clinical use against...

Additional file 10. Examples of titles and abstracts that were classified incorrectly by our algortihms.

Additional file 1. Justification for using a CRT search filter.

Additional file 5. Continuous skip-gram architecture.

Additional file 9. Details and results for applying our model on a dialysis-related dataset. Table S1. Number of relevant articles retrieved with and without machine learning algorithm using the demonstration dataset (n=882 records). The research objective was to review CRTs in the hemodialysis setting to report key methodological and ethical issues.

Supplementary Material 2

Additional file 1: Table S1. Search strategy for EMBASE and MEDLINE. Table S2. The journals searched for dental speciality.

In order to overcome the pandemic of COVID-19, messenger RNA (mRNA)-based vaccine has been extensively researched as a rapid and versatile strategy. Herein, we described the immunogenicity of mRNA-based vaccines for Beta and the most recent Omicron variants. The homologous mRNA-Beta and mRNA-Omicron and heterologous Ad5-nCoV plus mRNA vaccine exhibited high-level cross-reactive neutralization for Beta, original, Delta, and Omicron variants. It indicated that the COVID-19 mRNA vaccines have great potential in the clinical use against...

Supplementary Material 1

Additional file 4: Description of the gamma and c parameters. Fig. S2. This figure shows the decision boundaries and support vectors for different settings of the regularization parameter (C) and Kernel coefficient (gamma). We used the mglearn package to create this figure [27].

Additional file 9. Details and results for applying our model on a dialysis-related dataset. Table S1. Number of relevant articles retrieved with and without machine learning algorithm using the demonstration dataset (n=882 records). The research objective was to review CRTs in the hemodialysis setting to report key methodological and ethical issues.

Additional file 1: Table S1. Search strategy for EMBASE and MEDLINE. Table S2. The journals searched for dental speciality.

Additional file 2. Additional details for the external dataset.

Additional file 3: Fig. S1. A general architecture for a convolutional neural work used for text classification.

Additional file 6. Python libraries used for this project.

Additional file 6. Python libraries used for this project.

Additional file 7: Fig. S3. Probability plot for the CRTs in the first external data classified as a CRT (Figure A, 665 CRTs) and non-CRTs classified as a CRT (Figure B, 1251 non-CRTs). The x-axis depicts the stacked ensemble model's prediction of the article being classified as a CRT. The y-axis represents the proportion of all documents that had the corresponding probability.

Additional file 8. Screenshots of the front-end tool used for our machine learning algorithm.

Supplementary Material 1

Supplementary Material 2

Additional file 10. Examples of titles and abstracts that were classified incorrectly by our algortihms.

Additional file 1. Justification for using a CRT search filter.

Additional file 3: Fig. S1. A general architecture for a convolutional neural work used for text classification.