Learning the therapeutic targets of acute myeloid leukemia through multiscale human interactome network and community analysis
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
Acute myeloid leukemia (AML) is caused by proliferation of mutated myeloid progenitor cells. The standard chemotherapy regimen does not efficiently cause remission as there is a high relapse rate. Resistance acquired by leukemic stem cells is suggested to be one of the root causes of relapse. Therefore, there is an urgency to develop new drugs for therapy. Repurposing approved drugs for AML can provide a cost-friendly, time-efficient, and affordable alternative. The multiscale interactome network is a computational tool that can identify potential therapeutic candidates by comparing mechanisms of the drug and disease. Communities that could be potentially experimentally validated are detected in the multiscale interactome network using the algorithm CRank. The results are evaluated through literature search and Gene Ontology (GO) enrichment analysis. In this research, we identify therapeutic candidates for AML and their mechanisms from the interactome, and isolate prioritized communities that are dominant in the therapeutic mechanism that could potentially be used as a prompt for pre-clinical/translational research (e.g. bioinformatics, laboratory research) to focus on biological functions and mechanisms that are associated with the disease and drug. This method may allow for an efficient and accelerated discovery of potential candidates for AML, a rapidly progressing disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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