Article in GigaScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it
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 registry
The 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.
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what money
Authors and funding
6 authors.
Cigdem Sevim BayrakDepartment of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1498 New York, NY 10029, USA.ORCID 0000-0002-3883-5535
Qi ZengDepartment of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1498 New York, NY 10029, USA.ORCID 0009-0003-2226-5122
Marjan IlkovDepartment of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1498 New York, NY 10029, USA.ORCID 0000-0002-1464-9252
Scott J RussoFriedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1498 New York, NY 10029, USA.ORCID 0000-0002-6470-1805
Minghui WangDepartment of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1498 New York, NY 10029, USA.ORCID 0000-0001-9171-4962
Bin ZhangDepartment of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1498 New York, NY 10029, USA.ORCID 0000-0002-9549-5653
Funding
Integrative Network Biology Approaches to Identify, Characterize and Validate Molecular Subtypes in Alzheimer's DiseaseU01AG046170 · NIA · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI WANG, MINGHUI, ZHANG, BIN · 2013 to 2022
$26.0M
Novel Network Biology Approaches to Reposition FDA-approved Drugs for Alzheimer's DiseaseR01AG068030 · NIA · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI BRENNAND, KRISTEN JENNIFER, CAI, DONGMING · 2020 to 2024
$4.2M
Molecular Characterization and Validation of Gender Differences in Alzheimers Disease PathogenesisRF1AG054014 · NIA · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI CAI, DONGMING, ZHANG, BIN · 2016 to 2016
$4.2M
Dissect the interplay between sex and APOE at the single cell level to uncover novel pathways, targets and therapeutics for Alzheimer's diseaseRF1AG074010 · NIA · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI CAI, DONGMING, ZHANG, BIN · 2021 to 2021
$3.6M
Identification and Characterization of Senescent Cells in Molecular Subtypes of Alzheimer's DiseaseR01AG085182 · NIA · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Miranda Ethel Orr, Bin Zhang · 2024 to 2026
$2.5M
COVID and Translational Science supercomputer (CATS)S10OD030463 · OD · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI KOVATCH, PATRICIA · 2021 to 2021
$2.0M
Big Omics Data Engine 2 SupercomputerS10OD026880 · OD · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI KOVATCH, PATRICIA · 2019 to 2019
$2.0M
Research Infrastructure for the study of Alzheimer's Disease and Alzheimer's Disease-related dementias in older Asian AmericansUH2AG083258 · NIA · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI CAI, DONGMING, LI, CLARA · 2023 to 2024
$1.2M
Icahn School of Medicine at Mount Sinai UL1TR004419NCATS NIH HHSNIA NIH HHSNIH HHS R01AG068030NIH HHS R01AG085182NIH HHS RF1AG054014NIH HHS RF1AG074010NIH HHS S10OD026880NIH HHS S10OD030463NIH HHS U01AG046170NIH HHS UH2AG083258
6 · The paper itself
Abstract
backgroundBlood-based biomarkers offer a promising non-invasive strategy for detecting disease-related changes and monitoring tissue and organ health, including brain function. While recent studies have leveraged blood transcriptomic data to predict gene expression in the brain, existing models generally suffer from poor accuracy, limiting their translational utility.
findingsWe present an integrative prediction system (IPS) that combines machine learning with network biology to predict region-specific brain gene expression from blood transcriptomic data. Our framework integrates global blood transcriptomic signals, co-expression network features, and inter-tissue gene-gene interaction data linking blood genes to their target genes in the brain. Applied to the Genotype-Tissue Expression cohort, IPS substantially outperforms existing approaches in both the number and accuracy of brain genes that can be reliably predicted from blood. Notably, immune-related blood genes emerged as key contributors to model performance, underscoring the systematic interplay between peripheral immune signaling and central nervous system.
conclusionsThese findings highlight the potential of blood-based transcriptomic models as scalable, non-invasive tools for studying brain function and developing diagnostic and prognostic biomarkers for neurological and psychiatric disorders.
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.
Leveraging machine learning and network biology approaches to predict brain gene expression from blood transcriptomes. · full record | OpenQuestion