KibioR & Kibio: a new architecture for next-generation data querying and sharing in big biology
Bibliographic record
Abstract
MOTIVATION: The growing production of massive heterogeneous biological data offers opportunities for new discoveries. However, performing multi-omics data analysis is challenging, and researchers are forced to handle the ever-increasing complexity of both data management and evolution of our biological understanding. Substantial efforts have been made to unify biological datasets into integrated systems. Unfortunately, they are not easily scalable, deployable and searchable, locally or globally. RESULTS: This publication presents two tools with a simple structure that can help any data provider, organization or researcher, requiring a reliable data search and analysis base. The first tool is Kibio, a scalable and adaptable data storage based on Elasticsearch search engine. The second tool is KibioR, a R package to pull, push and search Kibio datasets or any accessible Elasticsearch-based databases. These tools apply a uniform data exchange model and minimize the burden of data management by organizing data into a decentralized, versatile, searchable and shareable structure. Several case studies are presented using multiple databases, from drug characterization to miRNAs and pathways identification, emphasizing the ease of use and versatility of the Kibio/KibioR framework. AVAILABILITYAND IMPLEMENTATION: Both KibioR and Elasticsearch are open source. KibioR package source is available at https://github.com/regisoc/kibior and the library on CRAN at https://cran.r-project.org/package=kibior. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".