P4‐031: Integrative Network Analysis of Multiple Alzheimer's Disease Rnaseq Studies From the Accelerating Medicine Partnership‐Alzheimer's Disease Consortium
Bibliographic record
Abstract
Alzheimer's disease is a devastating illness, with enormous personal, familial, and societal burdens. There is a desperate need to identify new targets for drug therapy to slow or halt progression of Alzheimer's disease in aging populations. The AMP-AD consortium is applying systems biology tools to develop network models of Alzheimer's disease for target discovery. However, it is unknown to what degree these networks are stable across data source or network generating algorithm. Here we test the robustness of the inferred transcriptional and coexpression networks of groups of genes across multiple data-sets. Transcriptomic data from the Religious Order Study and Memory and Aging Project (ROSMAP - a prospective, longitudinal cohort), the Mayo Clinic RNAseq study (post-mortem cohort with samples from the Mayo Clinic and Banner Sun Health Brain Banks), and the Mount Sinai Brain Bank RNAseq study (post-mortem cohort) were analyzed. Brain regions include dorsolateral prefrontal cortex (ROSMAP), temporal cortex and cerebellum (Mayo Clinic), and frontal pole (FP), superior temporal gyrus (STG), and parahippocampal gyrus (PHG) (Mount Sinai). In total, 1596 (592, 448, 556) samples derived from 1103 (592, 233, 314) patients were analyzed (ROSMAP, Mount Sinai, and Mayo Clinic respectively). We employ multiple network algorithms to infer gene coexpression and regulatory networks among samples within each study by brain region, and these were combined to identify consensus gene modules. A core set of biologically coherent gene modules were identified in a consistent manner across methods, brain region, and study cohort. These included gene modules that were enriched for mitochondrial function, synaptic transmission, immune response, and myelination. Additionally, we replicate previously published findings from AD transcriptome coexpression analyses, including the importance of microglia and innate immunity pathways. Transcriptional network inference holds great promise for elucidating molecular pathways that cause or modulate Alzheimer's disease pathophysiology. The results demonstrate the ability to identify transcriptional networks that are consistent across studies and may represent potential AD pathophysiologic pathways. These computational models of Alzheimer's disease have the potential to provide new hypotheses concerning drivers of AD development and progression for target discovery.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".