In one paragraphArticle in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registryThe trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
3 · Its place in the literatureWho cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what moneyAuthors and funding
6 authors.
Xiaohe TianComputer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.ORCID 0009-0005-5795-087X Tabassum FabihaSloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA.ORCID 0009-0008-9046-4525 William F LiComputer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.ORCID 0000-0001-9915-2482 Manolis KellisComputer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.ORCID 0000-0001-7113-9630 Yosuke TanigawaComputer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.ORCID 0000-0001-9759-157X Funding
Single Cell Transcriptomic and Epigenomic Dissection of Opioid and Cocaine Responses in HIVU01DA053631 · NIDA · BROAD INSTITUTE, INC. · PI HEIMAN, MYRIAM, KELLIS, MANOLIS · 2021 to 2025
$12.6MMapping the vulnerable locus coeruleus pathways in aging and ADU01AG077227 · NIA · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI Kwanghun Chung, Li-Huei Tsai · 2022 to 2026
$9.6MIdentification of TDP-43 Modifiers Through Single-Cell Transcriptional and Epigenomic Dissection of ALS and FTLD-MNDR01NS127187 · NINDS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI BELZIL, VERONIQUE, DONNELLY, CHRISTOPHER JAMES · 2021 to 2025
$9.1MEpigenomic, transcriptional and cellular dissection of Alzheimer's variantsR01AG058002 · NIA · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI HYMAN, BRADLEY T., JAENISCH, RUDOLF · 2017 to 2021
$7.9MElucidating the Molecular Mechanisms of Neuropsychiatric Symptoms in Alzheimer's DiseaseR01AG062335 · NIA · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI KELLIS, MANOLIS, TSAI, LI-HUEI · 2018 to 2022
$6.5MSingle-cell epigenomic and trancriptional dissection of sex-specific differences in Alzheimer’s DiseaseR01AG074003 · NIA · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI KELLIS, MANOLIS, TSAI, LI-HUEI · 2021 to 2025
$5.4MSingle-cell transcriptional and epigenomic dissection of Alzheimer's Disease and Related DementiasU01NS110453 · NINDS · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI KELLIS, MANOLIS, TSAI, LI-HUEI · 2018 to 2020
$4.0MCell type specific epigenetic analysis to understand complex mechanisms underlying Alzheimer's disease phenotypesRF1AG054012 · NIA · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI KELLIS, MANOLIS, TSAI, LI-HUEI · 2016 to 2016
$3.9MSingle-Cell Transcriptional and Epigenomic Dissection to Identify Therapeutic Targets for ALS and FTDR01AG067151 · NIA · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI BELZIL, VERONIQUE, KELLIS, MANOLIS · 2021 to 2025
$3.7MDissection of endosomal trafficking mechanisms in Alzheimer's DiseaseRF1AG062377 · NIA · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI TSAI, LI-HUEI · 2018 to 2018
$3.6MConstruction of an Integrated Immune-Vascular Brain - Chip as a Platform for the Study, Drug Screening, and Treatments of Alzheimer's DiseaseUH3NS115064 · NINDS · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI BLANCHARD, JOEL WILLIAM, KELLIS, MANOLIS · 2021 to 2023
$3.5MSingle-cell multi-region transcriptional and epigenomic dissection of VCID.RF1NS129032 · NINDS · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI HEIMAN, MYRIAM, KELLIS, MANOLIS · 2022 to 2022
$3.1MNHGRI NIH HHS R01 HG008155NIA NIH HHS R01 AG058002NIA NIH HHS R01 AG062335NIA NIH HHS R01 AG067151NIA NIH HHS R01 AG074003NIA NIH HHS R01 AG081017NIA NIH HHS R56 AG067151NIA NIH HHS RF1 AG054012NIA NIH HHS RF1 AG062377NIA NIH HHS U01 AG077227NIDA NIH HHS U01 DA053631NIMH NIH HHS R01 MH109978NIMH NIH HHS U01 MH119509NINDS NIH HHS R01 NS127187NINDS NIH HHS R01 NS129032NINDS NIH HHS RF1 NS129032NINDS NIH HHS U01 NS110453NINDS NIH HHS UG3 NS115064NINDS NIH HHS UH3 NS115064
6 · The paper itselfAbstract
The limited transferability of polygenic scores (PGS) across populations constrains their clinical utility and risks exacerbating health disparities, given challenges in multi-ancestry training, fine-mapping, and variant prioritization using genomic annotations, particularly when biologically relevant reference resources are sparse or unavailable for the target population. Here, we introduce PRISM, a transfer learning approach that jointly addresses these challenges to enhance PGS transferability. Applying PRISM to 7352 fine-mapped variants, 414 ENCODE annotations, and 406,659 individuals from the UK Biobank, we demonstrate that ancestry-aware integration of tissue-specific annotations yields the largest gains in predictive performance for African ancestry, with an average improvement of 13.10% (p=1.6×10
Identifiers
PMID41292742
PMCPMC12642535
What OpenQuestion holds
Textmetadata
LicenceCC BY-NC
Read underepoch 390