BioLake: an RNA expression analysis framework for prostate cancer biomarker powered by data lakehouse
Bibliographic record
Abstract
Biomedical researchers must often deal with large amounts of raw data, and analysis of this data might provide significant insights. However, if the raw data size is large, it might be difficult to uncover these insights. In this paper, a data framework named BioLake is presented that provides minimalist interactive methods to help researchers conduct bioinformatics data analysis. Unlike some existing analytical tools on the market, BioLake supports a wide range of web-based bioinformatics data analysis for public datasets, while allowing researchers to analyze their private datasets instantly. The tool also significantly enhances result interpretability by providing the source code and detailed instructions. In terms of data storage design, BioLake adopts the data lakehouse architecture to provide storage scalability and analysis flexibility. To further enhance the analysis efficiency, BioLake supports online analysis for custom data, allowing researchers to upload their own data via a designed procedure without waiting for server-side approval. BioLake allows a one-time upload of custom data of up to 500 MB to ensure that researchers avoid issues with data being too large for upload. In terms of the built-in dataset, BioLake applies reactive continuous data integration, helping the analysis pipeline to get rid of most preprocessing steps. The only pre-built-in dataset of BioLake in the first public version is TCGA-PRAD mRNA expression data for prostate cancer research, which is the primary focus of the development team of BioLake. In summary, BioLake offers a lightweight online tool to facilitate bioinformatic mRNA data analysis with the support of custom online data processing.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".