Micro-RNA and epigenetic factors regulatory network during Myelopoiesis
Bibliographic record
Abstract
Mammalian hematopoiesis is a hierarchal developmental process, which starts with Hematopoietic Stem Cells (HSCs) in bone marrow and through progressive stages of lineage commitment and differentiation gives rise to different mature blood cell types. These blood types can be classified into myeloid and lymphoid lineages. Epigenetic factors (EFs) are a class of gene regulators that modulate chromatin signatures and also work in concert with other classes of regulators like transcription factors (TFs) and non-coding RNAs (ncRNAs), in regulating normal development process. Perturbations in the expression of these regulators have been linked to various developmental defects and diseases progression including hematopoiesis. We are interested in understanding the cross-talk among these crucial regulators during myeloid cell development and acute myeloid leukemia. Towards this, we have analyzed the mRNA expression profile during each stage of myeloid cell development. The differentially expressed EFs during each stage of development were integrated with a network formed using curated gene regulations from different databases and a potentially active EF sub-network for each stage of development was extracted. These active stage specific sub-networks were further analyzed to obtain the unexplored potential regulatory relationship between EFs and miRNAs in a stage specific manner. The stage specific regulatory network generation will provide a framework for further understanding and targeting crucial EFs in hematopoiesis or blood cell development, perturbation in which leads to leukemia.
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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) | 0.000 | 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 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".