Biomolecular Network Controllability With Drug Binding Information
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Complex networks are ubiquitous in nature. In biological systems, biomolecules interact with each other to form so-called biomolecular networks, which determine the cellular behaviors of living organisms. Controlling the cellular behaviors by regulating certain biomolecules in the network is one of the most concerned problems in systems biology. Recently, the connections between biological networks and structural control theory have been explored, uncovering some interesting biological phenomena. Some researchers have paid attentions to the structural controllability of networks in notion of the minimum steering sets (MSSs). However, because the MSSs for complex networks are not unique and the importance of different MSSs is diverse in real applications, MSSs with certain meanings should be studied. In this paper, we investigated the MSSs of biomolecular networks by considering the drug binding information. The biomolecules in the MSSs with binding preference are enriched with known drug targets and are likely to have more chemical-binding opportunities with existing drugs compared with randomly chosen MSSs, suggesting novel applications for drug target identification and drug repositioning.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it