FishCODE: a web-based information platform for comprehensive omics data exploration in fish research
Bibliographic record
Abstract
Abstract In terms of the utilization of omics data, the current fish database analysis functions are primarily relatively simple tools at the transcriptional level, aimed at obtaining the co-expression levels of specified genes or the data visualization of multiple genes, and do not enable users to perform comprehensive omics data analysis. Furthermore, the gene-level information currently provided by these multispecies fish genomics databases is incomplete, and there is a lack of a comprehensive portal that can offer multidimensional genetic information. To address these challenges, we collected extensive multi-omics information on 35 fishes and established the primary comprehensive multi-omics data information platform for fish, FishCODE ( http://bioinfo.ihb.ac.cn/fishcode ). We have collected experimental background of dataset which pertaining to the target fishes, selected a range of datasets that encompass a broad spectrum of research areas, and downloaded the corresponding raw omics data from public repositories such as the Sequence Read Archive (SRA). Through a unified pipeline analysis, FishCODE contains 11,216 samples from 540 sets of genomic, transcriptomic, and methylomic datasets. These data encompass transcript structure and expression, gene methylation levels, protein domains, protein subcellular localization, protein interactions, best matched protein (Swiss-Prot), associated SNP site information (47,111,018), orthologous genes, phylogenetic tree and GO/KEGG annotations. To facilitate comparison, we annotated the experimental background data sets of the FishCODE, FishGET, PhyloFish, FishSED and FishSCT databases using the Fish Experimental Condition Ontology. Currently, the FishCODE database omics dataset includes 146 unique experimental condition words, 654 cumulative experimental condition words, and 13 species with rich experimental background (more than 20 unique FECO words). These data are 3.5 times (42), 8.3 times (74), and 6.5 times (2) those of the second-ranked databases respectively. We generated word cloud maps for the experimental condition vocabularies of FishCODE and FishGET, illustrating the superior richness of FishCODE’s experimental background.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.019 |
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 source (direct Gemma or distilled Codex), 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".