A SYSTEMS BIOLOGY APPROACH TO THE STUDY OF CISPLATIN DRUG RESISTANCE IN OVARIAN CANCERS
Why this work is in the frame
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Bibliographic record
Abstract
Cisplatin-induced drug resistance is known to involve a complex set of cellular changes whose molecular mechanism details remain unclear. In this study, we developed a systems biology approach to examine proteomics- and network-level changes between cisplatin-resistant and cisplatin-sensitive cell lines. This approach involves experimental investigation of differential proteomics profiles and computational study of activated enriched proteins, protein interactions, and protein interaction networks. Our experimental platform is based on a Label-free liquid Chromatography/mass spectrometry proteomics platform. Our computational methods start with an initial list of 119 differentially expressed proteins. We expanded these proteins into a cisplatin-resistant activated sub-network using a database of human protein-protein interactions. An examination of network topology features revealed the activated responses in the network are closely coupled. By examining sub-network proteins using gene ontology categories, we found significant enrichment of proton-transporting ATPase and ATP synthase complexes activities in cisplatin-resistant cells in the form of cooperative down-regulations. Using two-dimensional visualization matrixes, we further found significant cascading of endogenous, abiotic, and stress-related signals. Using a visual representation of activated protein categorical sub-networks, we showed that molecular regulation of cell differentiation and development caused by responses to proteome-wide stress as a key signature to the acquired drug resistance.
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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 | 0.001 | 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 | 0.000 | 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