BIAS : bioinformatics integrated application software and discovering relationships between transcription factors
Bibliographic record
Abstract
In the first part of this thesis, we present a new development platform especially tailored to Bioinformatics research and software development called Bias (Bioinformatics Integrated Application Software) designed to provide the tools necessary for carrying out integrative Bioinformatics research. Bias follows an object-relational strategy for providing persistent objects, allows third-party tools to be easily incorporated within the system, and it supports standards and data-exchange protocols common to Bioinformatics. The second part of this thesis is on the design and implementation of modules and libraries within Bias related to transcription factors. We present a module in Bias that focuses on discovering competitive relationships between mouse and yeast transcription factors. By competitive relationships we mean the competitive binding of two transcription factors for a given binding site. We also present a method that divides a transcription factor's set of binding sites into two or more different sets when constructing PSSMs.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".