% Bibliography for hypothesis h-f503b337
% Title: TYROBP (DAP12) Conditional Antagonism for Early-Stage Neuroprotection
% Generated: 2026-04-28T19:34:20Z

@article{pmid41139712,
  title = {Genome-wide consensus transcriptional signatures identify synaptic pruning linking Alzheimer's disease and epilepsy.},
  author = {Li H, Xie Z, Tian Y, Zhou R, Yang Y, Lin B, Chen S, Wu J, Deng Z, Li J, Chen M, Liu X, Sun Y, Wei N, Huang B, Ji X},
  journal = {Molecular psychiatry},
  year = {2026},
  pmid = {41139712},
  doi = {10.1038/s41380-025-03318-0},
  url = {https://pubmed.ncbi.nlm.nih.gov/41139712/},
}

@article{pmid41139712,
  title = {Genome-wide consensus transcriptional signatures identify synaptic pruning linking Alzheimer's disease and epilepsy.},
  author = {Li H, Xie Z, Tian Y, Zhou R, Yang Y et al.},
  journal = {Mol Psychiatry},
  year = {2026},
  pmid = {41139712},
  doi = {doi: 10.1038/s41380-025-03318-0},
  url = {https://pubmed.ncbi.nlm.nih.gov/41139712/},
}

@article{pmid41435829,
  title = {The Alzheimer's disease risk genes MS4A4A and MS4A6A cooperate to negatively regulate TREM2 and microglia states.},
  author = {Rosner D, Sun J, Cacace R, Yee A, Wagh C, Rychkova A, Dunlap M, Gulbranson D, Ibrahim A, Wang X, Wang R, Buonfiglioli A, Alhawagri M, Kong P, Roell M, Ho WH, Gong B, Denton H, Muscarnera G, Meese T, El-Khatib M, Bermingham D, Kahn E, Cignarella F, Rhinn H, Khan Z, Schwabe T, Srinivisan K, Mitra A, de Witte L},
  journal = {Neuron},
  year = {2026},
  pmid = {41435829},
  doi = {10.1016/j.neuron.2025.11.022},
  url = {https://pubmed.ncbi.nlm.nih.gov/41435829/},
}

@article{pmid41435829,
  title = {The Alzheimer's disease risk genes MS4A4A and MS4A6A cooperate to negatively regulate TREM2 and microglia states.},
  author = {Rosner D, Sun J, Cacace R, Yee A, Wagh C et al.},
  journal = {Neuron},
  year = {2026},
  pmid = {41435829},
  doi = {doi: 10.1016/j.neuron.2025.11.022},
  url = {https://pubmed.ncbi.nlm.nih.gov/41435829/},
}

@article{pmid41524613,
  title = {Integrative Transcriptomic and Bioinformatics Approaches Combined With Transformer Models Identify Key Gene Networks in Atherosclerosis.},
  author = {Yan D, Li W, Xing E, Wang P, Zhang Z},
  journal = {FASEB J},
  year = {2026},
  pmid = {41524613},
  doi = {doi: 10.1096/fj.202503988R},
  url = {https://pubmed.ncbi.nlm.nih.gov/41524613/},
}

@article{pmid41961800,
  title = {Integrative bioinformatics and machine learning identify shared molecular mechanisms and diagnostic biomarkers between Helicobacter pylori infection and atrial fibrillation.},
  author = {Deng A, Wang W},
  journal = {PLoS One},
  year = {2026},
  pmid = {41961800},
  doi = {doi: 10.1371/journal.pone.0346038},
  url = {https://pubmed.ncbi.nlm.nih.gov/41961800/},
}

@article{pmid20301376,
  title = {Polycystic Lipomembranous Osteodysplasia with Sclerosing Leukoencephalopathy.},
  author = {Adam, Bick, Mirzaa, Pagon, Wallace et al.},
  year = {1993},
  pmid = {20301376},
  url = {https://pubmed.ncbi.nlm.nih.gov/20301376/},
}