Identification of functionally connected multi-omic biomarkers for Alzheimer’s disease using modularity-constrained Lasso
Open accessPublished version of a preprint
Abstract
Large-scale genome wide association studies (GWASs) have led to discovery of many genetic risk factors in Alzheimer's disease (AD), such as APOE, TOMM40 and CLU. Despite the significant progress, it remains a major challenge to functionally validate these genetic findings and translate them into targetable mechanisms. Integration of multiple types of molecular data is increasingly used to address this problem. In this paper, we proposed a modularity-constrained Lasso model to jointly analyze the genotype, gene expression and protein expression data for discovery of functionally connected multi-omic biomarkers in AD. With a prior network capturing the functional relationship between SNPs, genes and proteins, the newly introduced penalty term maximizes the global modularity of the subnetwork involving selected markers and encourages the selection of multi-omic markers with dense functional connectivity, instead of individual markers. We applied this new model to the real data collected in the ROS/MAP cohort where the cognitive performance was used as disease quantitative trait. A functionally connected subnetwork involving 276 multi-omic biomarkers, including SNPs, genes and proteins, were identified to bear predictive power. Within this subnetwork, multiple trans-omic paths from SNPs to genes and then proteins were observed. This suggests that cognitive performance deterioration in AD patients can be potentially a result of genetic variations due to their cascade effect on the downstream transcriptome and proteome level.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Modularity-constrained Lasso has been used to link genetic variants to transcriptomic and proteomic alterations that collectively influence cognitive function.
“For example, modularity-constrained Lasso has been used to link genetic variants to transcriptomic and proteomic alterations that collectively influence cognitive function332.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [332]
The Introduction reports that modularity-constrained Lasso identified a connected subnetwork of SNPs, genes and proteins using cognitive performance as the outcome. It describes a genetic-to-transcriptomic-to-proteomic cascade affecting cognitive deterioration as a possibility, not an established collective influence on cognitive function. (LLM, conf=0.86)
Experimental — accuracy not yet measuredGraph neural networks leverage known biological interactions, including gene-gene and protein-protein relationships, to enhance biomarker discovery, improving predictive performance and elucidating interconnected molecular subnetworks.
“Concurrently, AI methodologies such as graph neural networks leverage known biological interactions, including gene–gene and protein–protein relationships, to enhance biomarker discovery. These models not only improve predictive performance but also elucidate interconnected molecular subnetworks that mediate broader disease processes314,332.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [332]
The Introduction describes using known SNP–gene–protein relationships to discover connected biomarkers, and Results > Performance comparison with competing methods reports better predictive performance and more connected subnetworks for modularity-constrained Lasso. The cited method is not a graph neural network; the paper compares it with GraphNet-constrained Lasso, which it says selected less-connected markers. Page numbers were not supplied in our copy. (LLM, conf=0.88)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975) · Journal of Psychiatric Research · doi:10.1016/0022-3956(75)90026-6 · Cited by 88,282 · see citing works
Cytoscape: A software Environment for integrated models of biomolecular interaction networks
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003) · Genome Research · doi:10.1101/gr.1239303 · Cited by 51,888 · see citing works
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482 · see citing works
KEGG: Kyoto Encyclopedia of Genes and Genomes
Kanehisa, M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.27 · Cited by 37,077 · see citing works
KEGG: Kyoto Encyclopedia of Genes and Genomes
Ogata, H., Goto, S., Sato, K., Fujibuchi, W., Bono, H., & Kanehisa, M. (1999) · Nucleic Acids Research · doi:10.1093/nar/27.1.29 · Cited by 31,232 · see citing works
Hirono, N., Mori, E., Ikejiri, Y., Imamura, T., Shimomura, T., Hashimoto, M., Yamashita, H., & Ikeda, M. (1997) · PubMed · Cited by 21,010 · see citing works
Kok, J. S., Berg, I. J., & Scherder, E. J. A. (2013) · Dementia and Geriatric Cognitive Disorders Extra · doi:10.1159/000353441 · Cited by 20,589 · see citing works
Older Adults' Reasons for Using Technology while Aging in Place
Peek, S. T. M., Luijkx, K. G., Rijnaard, M. D., Nieboer, M. E., van der Voort, C. S., Aarts, S., van Hoof, J., Vrijhoef, H. J., & Wouters, E. (2015) · Gerontology · doi:10.1159/000430949 · Cited by 19,851 · see citing works
Sensitivity and False Alarm Rate of a Fall Sensor in Long-Term Fall Detection in the Elderly
Kangas, M., Korpelainen, R., Vikman, I., Nyberg, L., & Jämsä, T. J. (2014) · Gerontology · doi:10.1159/000362720 · Cited by 19,005 · see citing works
Modularity and community structure in networks
Newman, M. E. J. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0601602103 · Cited by 12,256 · see citing works
Bindea, G., Mlecnik, B., Hackl, H., Charoentong, P., Tosolini, M., Kirilovsky, A., Fridman, W.-H., Pagès, F., Trajanoski, Z., & Galon, J. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp101 · Cited by 6,280 · see citing works
Oxyl radicals, redox-sensitive signalling cascades and antioxidants
Genestra, M. (2007) · Cellular Signalling · doi:10.1016/j.cellsig.2007.04.009 · Cited by 4,930 · see citing works
The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods
Jack, C. R., Bernstein, M. A., Fox, N. C., Thompson, P., Alexander, G., Harvey, D., Borowski, B., Britson, P. J., L. Whitwell, J., Ward, C., et al. (2008) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.21049 · Cited by 4,347 · see citing works
Multi-omics approaches to disease
Hasin, Y., Seldin, M., & Lusis, A. (2017) · Genome Biology · doi:10.1186/s13059-017-1215-1 · Cited by 2,953 · see citing works
The Reactome Pathway Knowledgebase
Fabregat, A., Jupe, S., Matthews, L. R., Sidiropoulos, K., Gillespie, M. E., Garapati, P. V., Haw, R., Jassal, B., Korninger, F., May, B., et al. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx1132 · Cited by 2,798 · see citing works
Visscher, P. M., Brown, M. A., McCarthy, M. I., & Yang, J. (2012) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2011.11.029 · Cited by 2,425 · see citing works
The Mini-Mental State Examination
Folstein, M. F. (1983) · Archives of General Psychiatry · doi:10.1001/archpsyc.1983.01790060110016 · Cited by 1,976 · see citing works
Brain insulin resistance in type 2 diabetes and Alzheimer disease: concepts and conundrums
Arnold, S. E., Arvanitakis, Z., Macauley-Rambach, S. L., Koenig, A. M., Wang, H.-Y., Ahima, R. S., Craft, S., Gandy, S., Buettner, C., Stoeckel, L. E., et al. (2018) · Nature Reviews Neurology · doi:10.1038/nrneurol.2017.185 · Cited by 1,597 · see citing works
The Alzheimer's Disease Neuroimaging Initiative
Mueller, S. G., Weiner, M. W., Thal, L. J., Petersen, R. C., Jack, C., Jagust, W., Trojanowski, J. Q., Toga, A. W., & Beckett, L. (2005) · Neuroimaging Clinics of North America · doi:10.1016/j.nic.2005.09.008 · Cited by 1,525 · see citing works
The Reactome pathway Knowledgebase
Fabregat, A., Sidiropoulos, K., Garapati, P., Gillespie, M., Hausmann, K., Haw, R., Jassal, B., Jupe, S., Korninger, F., McKay, S., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1351 · Cited by 1,201 · see citing works
Group lasso with overlap and graph lasso
Jacob, L., Obozinski, G., & Vert, J. (2009) · International Conference on Machine Learning · doi:10.1145/1553374.1553431 · Cited by 842 · see citing works
Beyond GWASs: Illuminating the Dark Road from Association to Function
Edwards, S. L., Beesley, J., French, J. D., & Dunning, A. M. (2013) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2013.10.012 · Cited by 784 · see citing works
More Is Better: Recent Progress in Multi-Omics Data Integration Methods
Huang, S., Chaudhary, K., & Garmire, L. X. (2017) · Frontiers in Genetics · doi:10.3389/fgene.2017.00084 · Cited by 712 · see citing works
Overview and Findings from the Religious Orders Study
Bennett, D. A., Schneider, J. A., Arvanitakis, Z., & Wilson, R. S. (2012) · Current Alzheimer Research · doi:10.2174/156720512801322573 · Cited by 647 · see citing works
Genetic variability in the regulation of gene expression in ten regions of the human brain
UK Brain Expression Consortium, Ramasamy, A., North American Brain Expression Consortium, Trabzuni, D., Guelfi, S., Varghese, V., Smith, C., Walker, R., De, T., Coin, L., et al. (2014) · Nature Neuroscience · doi:10.1038/nn.3801 · Cited by 644 · see citing works
Payne, J. P. (1969) · British Journal of Anaesthesia · doi:10.1093/bja/41.4.356 · Cited by 603 · see citing works
Revealing the architecture of gene regulation: the promise of eQTL studies
Gilad, Y., Rifkin, S. A., & Pritchard, J. K. (2008) · Trends in Genetics · doi:10.1016/j.tig.2008.06.001 · Cited by 531 · see citing works
Altered Insulin Signaling in Alzheimer’s Disease Brain – Special Emphasis on PI3K-Akt Pathway
Gabbouj, S., Ryhänen, S., Marttinen, M., Wittrahm, R., Takalo, M., Kemppainen, S., Martiskainen, H., Tanila, H., Haapasalo, A., Hiltunen, M., et al. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00629 · Cited by 401 · see citing works
The Role of Mitogen-Activated Protein Kinase Pathways in Alzheimer’s Disease
Zhu, X., Lee, H., Raina, A. K., Perry, G. L., & Smith, M. A. (2002) · Neurosignals · doi:10.1159/000067426 · Cited by 392 · see citing works
Napoli, C., Martin-Padura, I., de Nigris, F., Giorgio, M., Mansueto, G., Somma, P., Condorelli, M., Sica, G., De Rosa, G., & Pelicci, P. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0336359100 · Cited by 380 · see citing works
Inverse occurrence of cancer and Alzheimer disease: A population-based incidence study
Musicco, M., Adorni, F., Di Santo, S., Prinelli, F., Pettenati, C., Caltagirone, C., Palmer, K., & Russo, A. (2013) · Neurology · doi:10.1212/wnl.0b013e31829c5ec1 · Cited by 295 · see citing works
P300 assessment of early Alzheimer's disease
Polich, J. M., Ladish, C., & Bloom, F. E. (1990) · Electroencephalography and Clinical Neurophysiology/Evoked Potentials Section · doi:10.1016/0168-5597(90)90036-d · Cited by 278 · see citing works
Insulin resistance predicts brain amyloid deposition in late middle‐aged adults
Willette, A. A., Johnson, S. C., Birdsill, A. C., Sager, M. A., Christian, B., Baker, L. D., Craft, S., Oh, J. M., Statz, E. H., Hermann, B. P., et al. (2014) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2014.03.011 · Cited by 261 · see citing works
Roe, C. M., Behrens, M. I., Xiong, C., Miller, J. P., & Morris, J. C. (2005) · Neurology · doi:10.1212/01.wnl.0000152889.94785.51 · Cited by 225 · see citing works
Kumar, S., Ambrosini, G., & Bücher, P. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw1064 · Cited by 208 · see citing works
Kirouac, L., Rajic, A. J., Cribbs, D. H., & Padmanabhan, J. (2017) · eneuro · doi:10.1523/eneuro.0149-16.2017 · Cited by 207 · see citing works
Interpretable whole-brain prediction analysis with GraphNet
Grosenick, L., Klingenberg, B., Katovich, K., Knutson, B. D., & Taylor, J. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2012.12.062 · Cited by 197 · see citing works
Roncarati, R., Šestan, N., Scheinfeld, M. H., Berechid, B. E., Lopez, P., Meucci, O., McGlade, J. C., RAKIC, P. T., & D'adamio, L. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.102192599 · Cited by 193 · see citing works
A genome-wide scan for common variants affecting the rate of age-related cognitive decline
Jager, P. L. D., Shulman, J. M., Chibnik, L. B., Keenan, B. T., Raj, T., Wilson, R. S., Yu, L., Leurgans, S. E., Tran, D., Aubin, C., et al. (2011) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2011.09.033 · Cited by 192 · see citing works
Direct interaction between Shc and the platelet-derived growth factor beta-receptor.
Yokote, K., Mori, S., Hansen, K. H., McGlade, J. C., Pawson, T. J., Heldin, C., & Claesson, L. (1994) · Journal of Biological Chemistry · doi:10.1016/s0021-9258(17)36611-5 · Cited by 171 · see citing works
Dankort, D. L., Maślikowski, B. M., Warner, N. E., Kanno, N., Kim, H. L., Wang, Z., Moran, M. F., Oshima, R. G., Cardiff, R. D., & Muller, W. J. (2001) · Molecular and Cellular Biology · doi:10.1128/mcb.21.5.1540-1551.2001 · Cited by 163 · see citing works
Kim, S., & Xing, E. P. (2012) · The Annals of Applied Statistics · doi:10.1214/12-aoas549 · Cited by 161 · see citing works
Kim, S., & Xing, E. P. (2018) · doi:10.1184/r1/6476411.v1 · Cited by 143 · see citing works
Label-Free Quantitative LC–MS Proteomics of Alzheimer’s Disease and Normally Aged Human Brains
Andreev, V. P., Petyuk, V., Brewer, H. M., Karpievitch, Y. V., Xie, F., Clarke, J., Camp, D., Smith, R. D., Lieberman, A. P., Albin, R. L., et al. (2012) · Journal of Proteome Research · doi:10.1021/pr3001546 · Cited by 140 · see citing works
Russo, C., Dolcini, V., Salis, S., Venezia, V., Zambrano, N., Russo, T., & Schettini, G. (2002) · Journal of Biological Chemistry · doi:10.1074/jbc.m110785200 · Cited by 132 · see citing works
Efficient Methods for Overlapping Group Lasso
Yuan, L., Liu, J., & Ye, J. (2011) · Cited by 126 · see citing works
P300 Amplitude in Alzheimer’s Disease
Hedges, D., Janis, R. B., Mickelson, S., Keith, C. M., Bennett, D. P., & Brown, B. L. (2014) · Clinical EEG and Neuroscience · doi:10.1177/1550059414550567 · Cited by 108 · see citing works
Emerging roles for SH2/PTB-containing Shc adaptor proteins in the developing mammalian brain
Cattaneo, E. F., & Pelicci, P. G. (1998) · Trends in Neurosciences · doi:10.1016/s0166-2236(98)01282-x · Cited by 104 · see citing works
Alzheimer’s Disease and Cancer: When Two Monsters Cannot Be Together
Majd, S., Power, J., & Majd, Z. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00155 · Cited by 102 · see citing works
Focal adhesions regulate Aβ signaling and cell death in Alzheimer's disease
Caltagarone, J., Jing, Z., & Bowser, R. (2007) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2006.11.007 · Cited by 102 · see citing works
Thung, K., Chong-yaw Wee, A., Yap, P., & Shen, D. G. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.01.033 · Cited by 95 · see citing works
ECM receptors in neuronal structure, synaptic plasticity, and behavior
Kerrisk, M. E., Cingolani, L. A., & Koleske, A. J. (2014) · Progress in brain research · doi:10.1016/b978-0-444-63486-3.00005-0 · Cited by 90 · see citing works
Midlife insulin resistance, APOE genotype, and late-life brain amyloid accumulation
Ekblad, L. L., Johansson, J. J., Helin, S., Viitanen, M. H., Laine, H. K., Puukka, P. J., Jula, A., & Rinne, J. O. (2018) · Neurology · doi:10.1212/wnl.0000000000005214 · Cited by 84 · see citing works
Nho, K. T., Corneveaux, J. J., Kim, S., Lin, H., Risacher, S. L., Shen, L., Swaminathan, S., Ramanan, V. K., Liu, Y., Foroud, T., et al. (2013) · Molecular Psychiatry · doi:10.1038/mp.2013.24 · Cited by 81 · see citing works
Self-citationHorgusluoglu, E., Nho, K. T., Risacher, S. L., Kim, S., Foroud, T., Shaw, L. M. J., Trojanowski, J. Q., Aisen, P. S. S., Petersen, R. C., Jack, C. R., et al. (2017) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2017.08.010 · Cited by 78 · see citing works
Self-citationSaucier, C., Papavasiliou, V., Palazzo, A. F., Naujokas, M. A., Kremer, R. B., & Park, M. (2002) · Oncogene · doi:10.1038/sj.onc.1205261 · Cited by 63 · see citing works
Integrative Omics Analysis of Rheumatoid Arthritis Identifies Non-Obvious Therapeutic Targets
Whitaker, J. W., Boyle, D. L., Bartók, B., Ball, S. T., Gay, S., Wang, W., & Firestein, G. S. (2015) · PLoS ONE · doi:10.1371/journal.pone.0124254 · Cited by 61 · see citing works
F-box Only Protein 2 (Fbxo2) Regulates Amyloid Precursor Protein Levels and Processing
Atkin, G., Hunt, J. F. V., Minakawa, E. N., Sharkey, L. M., Tipper, N., Tennant, W., & Paulson, H. L. (2014) · Journal of Biological Chemistry · doi:10.1074/jbc.m113.515056 · Cited by 44 · see citing works
Engelman, C. D., Koscik, R. L., Jonaitis, E. M., Okonkwo, O. C., Hermann, B. P., Rue, A. L., & Sager, M. A. (2013) · Journal of Alzheimer s Disease · doi:10.3233/jad-130482 · Cited by 39 · see citing works
The Inverse Relationship Between Cancer and Alzheimer's Disease: A Possible Mechanism
Nixon, D. W. (2017) · Current Alzheimer Research · doi:10.2174/1567205014666170216152905 · Cited by 39 · see citing works
Petyuk, V., Qian, W., Smith, R. D., & Smith, D. (2009) · Methods · doi:10.1016/j.ymeth.2009.07.009 · Cited by 37 · see citing works
H63D mutation in hemochromatosis alters cholesterol metabolism and induces memory impairment
Ali-Rahmani, F. G., Grigson, P. S., Lee, S. Y., Neely, E. B., Connor, J. R., & Schengrund, C. (2013) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2013.12.014 · Cited by 32 · see citing works
Integrative analysis of multiple diverse omics datasets by sparse group multitask regression
Lin, D., Zhang, J., Li, J., He, H., Deng, H., & Wang, Y.-P. (2014) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2014.00062 · Cited by 31 · see citing works
Zhang, J., Tan, L., Xu, C., He, H., Tian, Q., Zhou, Y. G., Qiu, C., Chen, X., & Deng, H. (2015) · PLoS ONE · doi:10.1371/journal.pone.0138524 · Cited by 29 · see citing works
Identification of cancer fusion drivers using network fusion centrality
Wu, C.-C., Kannan, K., Lin, S. H., Yen, L., & Milosavljevic, A. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt131 · Cited by 22 · see citing works
A Convex Formulation of Modularity Maximization for Community Detection
Chan, E. Y. K., & Yeung, D. (2011) · doi:10.5591/978-1-57735-516-8/ijcai11-370 · Cited by 22 · see citing works
Pérez‐Cañamás, A., Sarroca, S., Melero‐Jerez, C., Porquet, D., Sansa, J., Knafo, S., Esteban, J. A., Sanfeliu, C., & Ledesma, M. D. (2016) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2016.08.009 · Cited by 21 · see citing works
Modifying Rap1-signalling by targeting Pde6δ is neuroprotective in models of Alzheimer’s disease
Dumbacher, M., Dooren, T. V., Princen, K., Witte, K. D., Farinelli, M., Lievens, S., Tavernier, J. H., Dehaen, W., Wera, S., Winderickx, J., et al. (2018) · Molecular Neurodegeneration · doi:10.1186/s13024-018-0283-3 · Cited by 18 · see citing works
Quesada (2001) · doi:10.1175/1520-0442(2001)014
Flagged first, then by citations
Research works printed but not linked: 2
- [1]Organization WH, et al Global Health Estimates 2016: Deaths by Cause. Age, Sex, by Country and by Region. 2000; 2016: 2018.
- [23]Hildebrand R. Identification of community structure in networks with convex optimization. arXiv preprint arXiv:08061896. 2008;.
Cited by
Sorted by citations
Ren, F., Wei, J., Chen, Q., Hu, M., Yu, L., Mi, J., Zhou, X., Qin, D., Wu, J., & Wu, A. (2025) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2025.07.030 · Cited by 97 · see citing works
The ROSMAP project: aging and neurodegenerative diseases through omic sciences
Pérez-González, A. P., García-Kroepfly, A. L., Pérez-Fuentes, K. A., García-Reyes, R. I., Solis-Roldan, F. F., Alba-González, J. A., Lemus, E. H., & de Anda‐Jáuregui, G. (2024) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2024.1443865 · Cited by 44 · see citing works
Garcia‐Segura, M. E., Durainayagam, B. R., Liggi, S., Graça, G., Jiménez, B., Dehghan, A., Tzoulaki, I., Karaman, İ., Elliott, P., & Griffin, J. L. (2022) · Journal of Neurochemistry · doi:10.1111/jnc.15719 · Cited by 25 · see citing works
Deutelmoser, H., Scherer, D., Brenner, H., Waldenberger, M., study, I., Suhre, K., Kastenmüller, G., & Bermejo, J. L. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa230 · Cited by 16 · see citing works
Behrens, L. M. P., Fernandes, G. D. S., Gonçalves, G. F., Nunes, F. V. M., Weimer, R. D., Moreira, J. C. F., & Dorn, M. (2026) · Neuroscience · doi:10.1016/j.neuroscience.2026.01.019 · Cited by 8 · see citing works
Upregulation of extracellular proteins in a mouse model of Alzheimer’s disease
Kim, S., Fuselier, J., Latoff, A., Manges, J., Jazwinski, S. M., & Zsombok, A. (2023) · Scientific Reports · doi:10.1038/s41598-023-33677-z · Cited by 5 · see citing works
Learning Biomedical Networks: Toward Data-Informed Clinical Decision and Therapy
Lopes, M. B., & Vinga, S. (2021) · Computational biology · doi:10.1007/978-3-030-69951-2_3 · Cited by 1 · see citing works
Díaz, A. G., Possenti, A., Urrutia, G. A., Bian, Y., Kedia, S., Böken, D., Lim, C. M., Licastro, D., Mannini, B., Klenerman, D., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.01.610673
Revolutionizing genomic medicine with artificial intelligence and data analytics
Patra, P. P. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-27638-5.00013-4
- Authors
- Xie, L., Varathan, P., Nho, K., Saykin, A. J., Salama, P., & Yan, J.
- Affiliations
- Indiana University Indianapolis (US)
- Indiana University Hospital (US)
- Corresponding author
- Yan, Jingwen
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2020-06-17
- Volume
- 15
- Issue
- 6
- Pages
- e0234748
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC7299377
- OpenAlex
- W3035808001
- Cited by
- 9
- References
- 69
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 2 · 2023: 1 · 2022: 1 · 2021: 1 · 2020: 1
- FWCI
- 0.32
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- IUScholarWorks (Indiana University) (submittedVersion, cc-by)
- Purdue University Indianapolis (Indiana University) (submittedVersion, cc-by)
- Funders
- National Institutes of Health (R21 AG066135, R01 AG019771, P30 AG010133)
- Division of Information and Intelligent Systems (CRII 1755836)
- Indiana University (Indiana University Collaborative Research Grant)
- Indiana Clinical and Translational Sciences Institute (Pilot grant)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- Alzheimer's disease, multi-omics biomarkers, functional connectivity, network modularity, gene expression, protein expression, cognitive performance, apolipoprotein E, TOMM40, clusterin, biomarker selection
- MeSH terms
- Aged, Aged, 80 and over, Alzheimer Disease, Female, Humans, Male, Biomarkers, Computational Biology, Polymorphism, Single Nucleotide, Genomics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
