Table 1.

A novel research publication type utilizing big-omics experimental database mining analyses leads to original new findings and generates new hypotheses.

Category	Big-omics database mining	Traditional literature review
Analysis of experimental data (NIH Geo DataSets with microarray experimental data, etc.)	Yes	No
Original new findings	Yes	No
Association research (gene co-expression patterns at the same pathology or stimuli)	Yes	No
Causative research (upstream regulator gene-deficient microarrays, …)	Yes	No
Panoramic view at multiple mechanisms and pathways	Yes	Yes
Improvement of our understanding	Yes	Yes
Searchable database requirements and tools	Yes	No
New publication types after–omics and high throughput experimental data generation	Yes	No
Different focuses from original papers	Yes	No
Use of Ingenuity Pathway Analysis (IPA) to analyze experimental data	Yes	No
Bioinformatic prediction	No	No
Future experimental verification	Yes	Yes
Face the low-throughput problems in verifying high-throughput–omics data (also see Yao et al. Nature Immunology, PMID: 31209400)	Yes	No
Summary of previous reports	No	Yes
Example for our database mining paper on IL-35 (highly cited by 173 papers)		PMID: 22438968
Example for traditional literature review: a Nature Immunology review that cited our database mining paper on IL-35		PMID: 22990890
Our experimental papers verifying the findings originated from our database mining paper on IL-35		PMIDs: 26085094; 29371247
Use of multiple NIH databases including PubMed database (https://www.ncbi.nlm.nih.gov/books/NBK143764/)	Yes	No PubMed database only

Comparisons were made regarding various aspect between this study, with a big-omics experimental database mining approach, and traditional literature reviews.