Bibliographic record
Abstract
Soybean Knowledge Base (SoyKB), is a comprehensive web resource for knowledge about soybean genomics and multi-omics data. It is designed to give researchers easier access and better understanding of soybean traits and molecular breeding. In this thesis we have further expanded the analytics capabilities of SoyKB by developing new informatics tools including eFP Browser, SNPViz 2.0, WGCNA analysis and POP Select. The tools highlighted here provide users information ranging from genomics data to GWAS and its application in molecular breeding. 1) The eFP Browser was originally developed by the University of Toronto to visualize data intuitively. We have done a local standalone implementation in SoyKB with 16 transcriptomics expression datasets. Each dataset is represented by an image that will be recolored based on tissues' expression level. 2) SNPViz is a tool to analyze whole genome sequence SNP datasets for haplotypes of user-defined gene regions. SNPViz 2.0, developed in Javascript, is targeted to resolve the Java related security issues in SNPViz 1.0. It also includes several new features such as gene version control, neighbor joining cluster method and RGBY color scheme. At the same time, the cluster tree constructed in SNPViz 2.0 is dynamic which users can click a node to collapse or expand the sub-tree instead of just a static image. 3) WGCNA, is an open source R package for weighted gene co-expression network analysis and gene module detection, which we have incorporated in SoyKB as a new analysis feature in our Differential Expression Browser suite of tools. 4) Pop Select is a tool to help breeders analyze SNP population datasets and identify top scoring offspring with desired genomic information. It scores all offspring based on user specified region and parent type and then output top offspring information in charts and tables. These newly incorporated tools enriched SoyKB data visualization and analysis functionalities tremendously. In the future we will maintain these tools to make them more robust while exploring new application areas and developing new tools for the soybean research community.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".