Artificial intelligence for neurodegenerative experimental models.
FAIR
animal models
artificial intelligence
comparative biology
dementia
experimental models
iPSC
in silico
in vitro
in vivo
machine learning
neurodegeneration
preclinical
reproducibility
translation
Journal
Alzheimer's & dementia : the journal of the Alzheimer's Association
ISSN: 1552-5279
Titre abrégé: Alzheimers Dement
Pays: United States
ID NLM: 101231978
Informations de publication
Date de publication:
28 Sep 2023
28 Sep 2023
Historique:
revised:
11
08
2023
received:
17
04
2023
accepted:
14
08
2023
medline:
28
9
2023
pubmed:
28
9
2023
entrez:
28
9
2023
Statut:
aheadofprint
Résumé
Experimental models are essential tools in neurodegenerative disease research. However, the translation of insights and drugs discovered in model systems has proven immensely challenging, marred by high failure rates in human clinical trials. Here we review the application of artificial intelligence (AI) and machine learning (ML) in experimental medicine for dementia research. Considering the specific challenges of reproducibility and translation between other species or model systems and human biology in preclinical dementia research, we highlight best practices and resources that can be leveraged to quantify and evaluate translatability. We then evaluate how AI and ML approaches could be applied to enhance both cross-model reproducibility and translation to human biology, while sustaining biological interpretability. AI and ML approaches in experimental medicine remain in their infancy. However, they have great potential to strengthen preclinical research and translation if based upon adequate, robust, and reproducible experimental data. There are increasing applications of AI in experimental medicine. We identified issues in reproducibility, cross-species translation, and data curation in the field. Our review highlights data resources and AI approaches as solutions. Multi-omics analysis with AI offers exciting future possibilities in drug discovery.
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Medical Research Council
ID : MR/X005674/1
Pays : United Kingdom
Organisme : Alzheimer's Association
Pays : United States
Organisme : Parkinson's UK
Pays : United Kingdom
Informations de copyright
© 2023 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.
Références
Grapov D, Fahrmann J, Wanichthanarak K, Khoomrung S. Rise of deep learning for genomic, proteomic, and metabolomic data integration in precision medicine. OMICS. 2018;22:630-636. 10.1089/omi.2018.0097
Mullane K, Williams M. Preclinical Models of Alzheimer's Disease: relevance and Translational Validity. Curr Protoc Pharmacol. 2019;84:e57. 10.1002/cpph.57
Choi SH, Kim YH, Hebisch M, et al. A three-dimensional human neural cell culture model of Alzheimer's disease. Nature. 2014;515:274-278. 10.1038/nature13800
Stansley B, Post J, Hensley K. A comparative review of cell culture systems for the study of microglial biology in Alzheimer's disease. J Neuroinflammation. 2012;9:115. 10.1186/1742-2094-9-115
Kwak SS, Washicosky KJ, Brand E, et al. Amyloid-β42/40 ratio drives tau pathology in 3D human neural cell culture models of Alzheimer's disease. Nat Commun. 2020;11:1377. 10.1038/s41467-020-15120-3
Penney J, Ralvenius WT, Tsai L-H. Modeling Alzheimer's disease with iPSC-derived brain cells. Mol Psychiatry. 2020;25:148-167. 10.1038/s41380-019-0468-3
Fujimori K, Ishikawa M, Otomo A, et al. Modeling sporadic ALS in iPSC-derived motor neurons identifies a potential therapeutic agent. Nat Med. 2018;24:1579-1589. 10.1038/s41591-018-0140-5
Schweitzer JS, Song B, Herrington TM, et al. Personalized iPSC-Derived Dopamine Progenitor Cells for Parkinson's Disease. N Engl J Med. 2020;382:1926-1932. 10.1056/NEJMoa1915872
Croft CL, Futch HS, Moore BD, Golde TE. Organotypic brain slice cultures to model neurodegenerative proteinopathies. Mol Neurodegener. 2019;14:45. 10.1186/s13024-019-0346-0
Avossa D, Grandolfo M, Mazzarol F, Zatta M, Ballerini L. Early signs of motoneuron vulnerability in a disease model system: characterization of transverse slice cultures of spinal cord isolated from embryonic ALS mice. Neuroscience. 2006;138:1179-1194. 10.1016/j.neuroscience.2005.12.009
Humpel C. Organotypic brain slice cultures: a review. Neuroscience. 2015;305:86-98. 10.1016/j.neuroscience.2015.07.086
Papaspyropoulos A, Tsolaki M, Foroglou N, Pantazaki AA. Modeling and targeting Alzheimer's disease with organoids. Front Pharmacol. 2020;11:396. 10.3389/fphar.2020.00396
Zhao J, Fu Y, Yamazaki Y, et al. APOE4 exacerbates synapse loss and neurodegeneration in Alzheimer's disease patient iPSC-derived cerebral organoids. Nat Commun. 2020;11:5540. 10.1038/s41467-020-19264-0
Smits LM, Reinhardt L, Reinhardt P, et al. Modeling Parkinson's disease in midbrain-like organoids. NPJ Parkinsons Dis. 2019;5:5. 10.1038/s41531-019-0078-4
Monzel AS, Smits LM, Hemmer K, et al. Derivation of Human Midbrain-Specific Organoids from Neuroepithelial Stem Cells. Stem Cell Rep. 2017;8:1144-1154. 10.1016/j.stemcr.2017.03.010
Kounnas MZ, Danks AM, Cheng S, et al. Modulation of gamma-secretase reduces beta-amyloid deposition in a transgenic mouse model of Alzheimer's disease. Neuron. 2010;67:769-780. 10.1016/j.neuron.2010.08.018
Webster SJ, Bachstetter AD, Van Eldik LJ. Comprehensive behavioral characterization of an APP/PS-1 double knock-in mouse model of Alzheimer's disease. Alzheimers Res Ther. 2013;5:28. 10.1186/alzrt182
Saito T, Matsuba Y, Mihira N, et al. Single App knock-in mouse models of Alzheimer's disease. Nat Neurosci. 2014;17:661-663. 10.1038/nn.3697
Betarbet R, Sherer TB, MacKenzie G, Garcia-Osuna M, Panov AV, Greenamyre JT. Chronic systemic pesticide exposure reproduces features of Parkinson's disease. Nat Neurosci. 2000;3:1301-1306. 10.1038/81834
Cannon JR, Greenamyre JT. The role of environmental exposures in neurodegeneration and neurodegenerative diseases. Toxicol Sci. 2011;124:225-250. 10.1093/toxsci/kfr239
Mancuso R, Van Den Daele J, Fattorelli N, et al. Stem-cell-derived human microglia transplanted in mouse brain to study human disease. Nat Neurosci. 2019;22:2111-2116. 10.1038/s41593-019-0525-x
Mckean NE, Handley RR, Snell RG. A review of the current mammalian models of Alzheimer's disease and challenges that need to be overcome. Int J Mol Sci. 2021;22 :13168. 10.3390/ijms222313168
Anderson RM, Hadjichrysanthou C, Evans S, Wong MM. Why do so many clinical trials of therapies for Alzheimer's disease fail? Lancet. 2017;390:2327-2329. 10.1016/S0140-6736(17)32399-1
McGonigle P, Ruggeri B. Animal models of human disease: challenges in enabling translation. Biochem Pharmacol. 2014;87:162-171. 10.1016/j.bcp.2013.08.006
Sukoff Rizzo SJ, Masters A, Onos KD, et al. Improving preclinical to clinical translation in Alzheimer's disease research. Alzheimers Dement. 2020;6:e12038. 10.1002/trc2.12038
Doherty T, Yao Z, Khleifat AAl, et al. Artificial intelligence for dementia drug discovery and trials optimization. Alzheimers Dement. Portico. 2023. 10.1002/alz.13428
Bettencourt C, Skene N, Bandres-Ciga S, et al. Artificial intelligence for dementia genetics and omics. Alzheimers Dement. Portico. 2023. 10.1002/alz.13427
Winchester LM, Harshfield EL, Shi L, et al. Artificial intelligence for biomarker discovery in Alzheimer’s disease and dementia. Alzheimers Dement. Portico. 2023. 10.1002/alz.13390
Borchert RJ, Azevedo T, Badhwar A, et al. Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: A systematic review. Alzheimers Dement. Portico. 2023. 10.1002/alz.13412
Newby D, Orgeta V, Marshall CR, et al. Artificial intelligence for dementia prevention. Alzheimers Dement. Accepted.
Lyall DM, Kormilitzin A, Lancaster C, et al. Artificial intelligence for dementia-Applied models and digital health. Alzheimers Dement. Portico. 2023. 10.1002/alz.13391
Bucholc M, James C, Khleifat AA, et al. Artificial intelligence for dementia research methods optimization. Alzheimers Dement. Portico. 2023. 10.1002/alz.13441
Claerbout JF, Karrenbach M, Electronic documents give reproducible research a new meaning. SEG Technical Program Expanded Abstracts 1992, Society of Exploration Geophysicists. 1992. 10.1190/1.1822162
Stodden V, Leisch F, Peng RD. Implementing Reproducible Research. CRC Press; 2014.
Plesser HE. Reproducibility vs. replicability: a brief history of a confused terminology. Front Neuroinform. 2017;11:76. 10.3389/fninf.2017.00076
Wilkinson MD, Dumontier M, Aalbersberg IJJ, et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data. 2016;3:160018. 10.1038/sdata.2016.18
Lopez R, Regier J, Cole MB, Jordan MI, Yosef N. Deep generative modeling for single-cell transcriptomics. Nat Methods. 2018;15:1053-1058. 10.1038/s41592-018-0229-2
Lotfollahi M, Naghipourfar M, Luecken MD, et al. Mapping single-cell data to reference atlases by transfer learning. Nat Biotechnol. 2022;40:121-130. 10.1038/s41587-021-01001-7
Kilpinen H, Goncalves A, Leha A, et al. Corrigendum: common genetic variation drives molecular heterogeneity in human iPSCs. Nature. 2017;546:686. 10.1038/nature23012
D'Antonio M, Benaglio P, Jakubosky D, et al. Insights into the mutational burden of human induced pluripotent stem cells from an integrative multi-omics approach. Cell Rep. 2018;24:883-894. 10.1016/j.celrep.2018.06.091
Volpato V, Smith J, Sandor C, et al. Reproducibility of molecular phenotypes after long-term differentiation to human iPSC-Derived neurons: a multi-site omics study. Stem Cell Rep. 2018;11:897-911. 10.1016/j.stemcr.2018.08.013
Velasco S, Kedaigle AJ, Simmons SK, et al. Individual brain organoids reproducibly form cell diversity of the human cerebral cortex. Nature. 2019;570:523-527. 10.1038/s41586-019-1289-x
Doss MX, Sachinidis A. Current Challenges of iPSC-Based disease modeling and therapeutic implications. Cells. 2019;8:40. 10.3390/cells8050403
Jankowsky JL, Zheng H. Practical considerations for choosing a mouse model of Alzheimer's disease. Mol Neurodegener. 2017;12:89. 10.1186/s13024-017-0231-7
Carlson GA, Borchelt DR, Dake A, et al. Genetic modification of the phenotypes produced by amyloid precursor protein overexpression in transgenic mice. Hum Mol Genet. 1997;6:1951-1959. 10.1093/hmg/6.11.1951
Lehman EJH, Kulnane LS, Gao Y, et al. Genetic background regulates β-amyloid precursor protein processing and β-amyloid deposition in the mouse. Hum Mol Genet. 2003;12:2949-2956. 10.1093/hmg/ddg322
Jackson HM, Onos KD, Pepper KW, et al. DBA/2J genetic background exacerbates spontaneous lethal seizures but lessens amyloid deposition in a mouse model of Alzheimer's disease. PLoS One. 2015;10:e0125897. 10.1371/journal.pone.0125897
Ryman D, Gao Y, Lamb BT. Genetic loci modulating amyloid-beta levels in a mouse model of Alzheimer's disease. Neurobiol Aging. 2008;29:1190-1198. 10.1016/j.neurobiolaging.2007.02.017
Morihara T, Hayashi N, Yokokoji M, et al. Transcriptome analysis of distinct mouse strains reveals kinesin light chain-1 splicing as an amyloid-β accumulation modifier. Proc Natl Acad Sci USA. 2014;111:2638-2643. 10.1073/pnas.1307345111
Neuner SM, Heuer SE, Huentelman MJ, O'Connell KMS, Kaczorowski CC. Harnessing genetic complexity to enhance translatability of Alzheimer's disease mouse models: a path toward precision medicine. Neuron. 2019;101:399-411.e5. 10.1016/j.neuron.2018.11.040
O'Connell KMS, Ouellette AR, Neuner SM, Dunn AR, Kaczorowski CC. Genetic background modifies CNS-mediated sensorimotor decline in the AD-BXD mouse model of genetic diversity in Alzheimer's disease. Genes Brain Behav. 2019;18:e12603. 10.1111/gbb.12603
Ransohoff RM. All (animal) models (of neurodegeneration) are wrong. Are they also useful? J Exp Med. 2018;215:2955-2958. 10.1084/jem.20182042
Chesselet M-F, Richter F. Modelling of Parkinson's disease in mice. Lancet Neurol. 2011;10:1108-1118. 10.1016/S1474-4422(11)70227-7
Puzzo D, Gulisano W, Palmeri A, Arancio O. Rodent models for Alzheimer's disease drug discovery. Expert Opin Drug Discov. 2015;10:703-711. 10.1517/17460441.2015.1041913
Ashe KH, Zahs KR. Probing the biology of Alzheimer's disease in mice. Neuron. 2010;66:631-645. 10.1016/j.neuron.2010.04.031
LaFerla FM, Green KN. Animal models of Alzheimer disease. Cold Spring Harb Perspect Med. 2012;2:a006320. 10.1101/cshperspect.a006320
Price DL, Tanzi RE, Borchelt DR, Sisodia SS. Alzheimer's disease: genetic studies and transgenic models. Annu Rev Genet. 1998;32:461-493. 10.1146/annurev.genet.32.1.461
Roberson ED. Mouse models of frontotemporal dementia. Ann Neurol. 2012;72:837-849. 10.1002/ana.23722
Ittner LM, Halliday GM, Kril JJ, Götz J, Hodges JR, Kiernan MC. FTD and ALS-translating mouse studies into clinical trials. Nat Rev Neurol. 2015;11:360-366. 10.1038/nrneurol.2015.65
Veening-Griffioen DH, Ferreira GS, van Meer PJK, et al. Are some animal models more equal than others? A case study on the translational value of animal models of efficacy for Alzheimer's disease. Eur J Pharmacol. 2019;859:172524. 10.1016/j.ejphar.2019.172524
Vardigan JD, Cannon CE, Puri V, et al. Improved cognition without adverse effects: novel M1 muscarinic potentiator compares favorably to donepezil and xanomeline in rhesus monkey. Psychopharmacology (Berl). 2015;232:1859-1866. 10.1007/s00213-014-3813-x
Shin CY, Kim H-S, Cha K-H, et al. The effects of Donepezil, an acetylcholinesterase inhibitor, on impaired learning and memory in rodents. Biomol Ther. 2018;26:274-281. 10.4062/biomolther.2017.189
Cotman CW, Head E. The canine (dog) model of human aging and disease: dietary, environmental and immunotherapy approaches. J Alzheimers Dis. 2008;15:685-707. 10.3233/jad-2008-15413
Neus Bosch M, Pugliese M, Gimeno-Bayon J, Jose Rodriguez M, Mahy N. Dogs with cognitive dysfunction syndrome: a natural model of Alzheimer's disease. Curr Alzheimer Res. 2012;9:298-314. 10.2174/156720512800107546
Van Dam D, De Deyn PP. Animal models in the drug discovery pipeline for Alzheimer's disease. Br J Pharmacol. 2011;164:1285-1300. 10.1111/j.1476-5381.2011.01299.x
Hampel H, Nisticò R, Seyfried NT, et al. Omics sciences for systems biology in Alzheimer's disease: state-of-the-art of the evidence. Ageing Res Rev. 2021;69:101346. 10.1016/j.arr.2021.101346
Hay M, Thomas DW, Craighead JL, Economides C, Rosenthal J. Clinical development success rates for investigational drugs. Nat Biotechnol. 2014;32:40-51. 10.1038/nbt.2786
Munos B. Lessons from 60 years of pharmaceutical innovation. Nat Rev Drug Discov. 2009;8:959-968. 10.1038/nrd2961
Scannell JW, Blanckley A, Boldon H, Warrington B. Diagnosing the decline in pharmaceutical R&D efficiency. Nat Rev Drug Discov. 2012;11:191-200. 10.1038/nrd3681
Cummings J. Lessons learned from Alzheimer disease: clinical trials with negative outcomes. Clin Transl Sci. 2018;11:147-152. 10.1111/cts.12491
Tan C-C, Yu J-T, Wang H-F, et al. Efficacy and safety of donepezil, galantamine, rivastigmine, and memantine for the treatment of Alzheimer's disease: a systematic review and meta-analysis. J Alzheimers Dis. 2014;41:615-631. 10.3233/JAD-132690
Doody RS, Thomas RG, Farlow M, et al. Phase 3 trials of solanezumab for mild-to-moderate Alzheimer's disease. N Engl J Med. 2014;370:311-321. 10.1056/NEJMoa1312889
Doody RS, Raman R, Farlow M, et al. A phase 3 trial of semagacestat for treatment of Alzheimer's disease. N Engl J Med. 2013;369:341-350. 10.1056/NEJMoa1210951
Uddin MS, Kabir MT, Rahman MS, et al. Revisiting the amyloid cascade hypothesis: from Anti-Aβ therapeutics to auspicious new ways for Alzheimer's disease. Int J Mol Sci. 2020;21:5858. 10.3390/ijms21165858
van Dyck CH, Swanson CJ, Aisen P, et al. Lecanemab in early Alzheimer's disease. N Engl J Med. 2023;388:9-21. 10.1056/NEJMoa2212948
Sims JR, Zimmer JA, Evans CD, et al. Donanemab in early symptomatic Alzheimer disease: the TRAILBLAZER-ALZ 2 randomized clinical Trial. JAMA. 2023;330:512-527. 10.1001/jama.2023.13239
Tampi RR, Forester BP, Agronin M. Aducanumab: evidence from clinical trial data and controversies. Drugs Context. 2021;10. 10.7573/dic.2021-7-3
Howard R, Liu KY. Questions EMERGE as Biogen claims aducanumab turnaround. Nat Rev Neurol. 2020;16:63-64. 10.1038/s41582-019-0295-9
Salloway S, Chalkias S, Barkhof F, et al. Amyloid-Related imaging abnormalities in 2 Phase 3 Studies evaluating Aducanumab in patients with early Alzheimer disease. JAMA Neurol. 2022;79:13-21. 10.1001/jamaneurol.2021.4161
Freedman LP, Inglese J. The increasing urgency for standards in basic biologic research. Cancer Res. 2014;74:4024-4029. 10.1158/0008-5472.CAN-14-0925
Mlinarić A, Horvat M, Šupak Smolčić V. Dealing with the positive publication bias: why you should really publish your negative results. Biochem Med. 2017;27:030201. 10.11613/BM.2017.030201
Mazure CM, Swendsen J. Sex differences in Alzheimer's disease and other dementias. Lancet Neurol. 2016;15:451-452. 10.1016/S1474-4422(16)00067-3
Gillies GE, Pienaar IS, Vohra S, Qamhawi Z. Sex differences in Parkinson's disease. Front Neuroendocrinol. 2014;35:370-384. 10.1016/j.yfrne.2014.02.002
Beery AK, Zucker I. Sex bias in neuroscience and biomedical research. Neurosci Biobehav Rev. 2011;35:565-572. 10.1016/j.neubiorev.2010.07.002
Robinson PN, Köhler S, Bauer S, Seelow D, Horn D, Mundlos S. The Human Phenotype Ontology: a tool for annotating and analyzing human hereditary disease. Am J Hum Genet. 2008;83:610-615. 10.1016/j.ajhg.2008.09.017
Köhler S, Gargano M, Matentzoglu N, et al. The Human Phenotype Ontology in 2021. Nucleic Acids Res. 2021;49:D1207-D1217. 10.1093/nar/gkaa1043
Matentzoglu N, Osumi-Sutherland D, Balhoff JP, et al. uPheno 2: Framework for standardised representation of phenotypes across species 2019. 10.7490/f1000research.1116540.1
Köhler S, Doelken SC, Ruef BJ, et al. Construction and accessibility of a cross-species phenotype ontology along with gene annotations for biomedical research. F1000Res. 2013;2:30. 10.12688/f1000research.2-30.v2
NCBO BioPortal n.d. Accessed July 14, 2023. https://bioportal.bioontology.org/
Whetzel PL, Noy NF, Shah NH, et al. BioPortal: enhanced functionality via new Web services from the National Center for Biomedical Ontology to access and use ontologies in software applications. Nucleic Acids Res. 2011;39:W541-W545. 10.1093/nar/gkr469
Nam E, Lee Y-B, Moon C, Chang K-A. Serum tau proteins as potential biomarkers for the assessment of Alzheimer's disease progression. Int J Mol Sci. 2020;21. 10.3390/ijms21145007
Ou Y-N, Xu W, Li J-Q, et al. FDG-PET as an independent biomarker for Alzheimer's biological diagnosis: a longitudinal study. Alzheimers Res Ther. 2019;11:57. 10.1186/s13195-019-0512-1
Platt B, Drever B, Koss D, et al. Abnormal cognition, sleep, EEG and brain metabolism in a novel knock-in Alzheimer mouse, PLB1. PLoS One. 2011;6:e27068. 10.1371/journal.pone.0027068
Devoy A, Kalmar B, Stewart M, Park H, Burke B, Noy SJ. Humanized mutant FUS drives progressive motor neuron degeneration without aggregation in “FUSDelta14”knockin mice. Brain. 2017;140(11):2797-2805.
Kumar S, Stecher G, Suleski M, Hedges SB. TimeTree: a resource for timelines, timetrees, and divergence times. Mol Biol Evol. 2017;34:1812-1819. 10.1093/molbev/msx116
Martin T, Fraser HB. Comparative expression profiling reveals widespread coordinated evolution of gene expression across eukaryotes. Nat Commun. 2018;9:4963. 10.1038/s41467-018-07436-y
Chen J, Swofford R, Johnson J, et al. A quantitative framework for characterizing the evolutionary history of mammalian gene expression. Genome Res. 2019;29:53-63. 10.1101/gr.237636.118
Striedter GF. Précis of principles of brain evolution. Behav Brain Sci. 2006;29:1-12. 10.1017/S0140525X06009010. discussion 12-36.
Patzke N, Spocter MA, Karlsson KAE, et al. In contrast to many other mammals, cetaceans have relatively small hippocampi that appear to lack adult neurogenesis. Brain Struct Funct. 2015;220:361-383. 10.1007/s00429-013-0660-1
Schilder BM, Petry HM, Hof PR. Evolutionary shifts dramatically reorganized the human hippocampal complex. J Comp Neurol. 2020;528:3143-3170. 10.1002/cne.24822
Barger N, Hanson KL, Teffer K, Schenker-Ahmed NM, Semendeferi K. Evidence for evolutionary specialization in human limbic structures. Front Hum Neurosci. 2014;8:277. 10.3389/fnhum.2014.00277
Smaers JB, Gómez-Robles A, Parks AN, Sherwood CC. Exceptional evolutionary expansion of prefrontal cortex in great apes and humans. Curr Biol. 2017;27:1549. 10.1016/j.cub.2017.05.015
NCBI Resource Coordinators. Database resources of the National Center for biotechnology information. Nucleic Acids Res. 2017;45:D12-D17. 10.1093/nar/gkw1071
Schilder BM. Orthogene: Interspecies gene mapping. 2021. 10.18129/B9.bioc.orthogene
Enard W. The molecular basis of human brain evolution. Curr Biol. 2016;26:R1109-R1117. 10.1016/j.cub.2016.09.030
Dennis MY, Nuttle X, Sudmant PH, et al. Evolution of human-specific neural SRGAP2 genes by incomplete segmental duplication. Cell. 2012;149:912-922. 10.1016/j.cell.2012.03.033
Li J, Pan L, Pembroke WG, et al. Conservation and divergence of vulnerability and responses to stressors between human and mouse astrocytes. Nat Commun. 2021;12:3958. 10.1038/s41467-021-24232-3
Geirsdottir L, David E, Keren-Shaul H, et al. Cross-Species single-cell analysis reveals divergence of the primate microglia program. Cell. 2020;181:746. 10.1016/j.cell.2020.04.002
Sharma K, Bisht K, Eyo UB. A comparative biology of microglia across species. Front Cell Dev Biol. 2021;9:652748. 10.3389/fcell.2021.652748
Berto S, Mendizabal I, Usui N, et al. Accelerated evolution of oligodendrocytes in the human brain. Proc Natl Acad Sci USA. 2019;116:24334-24342. 10.1073/pnas.1907982116
Nguyen A, Bionaz M. Analysis of model organism viability through an interspecies pathway comparison pipeline using the dynamic impact approach. bioRxiv 2019:2019.12.18.448985. 10.1101/2019.12.18.448985
Jumper J, Evans R, Pritzel A, et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021;596:583-589. 10.1038/s41586-021-03819-2
Lin Z, Akin H, Rao R, et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science. 2023;379:1123-1130. 10.1126/science.ade2574
Sundaram L, Gao H, Padigepati SR, et al. Predicting the clinical impact of human mutation with deep neural networks. Nat Genet. 2018;50:1161-1170. 10.1038/s41588-018-0167-z
Gao H, Hamp T, Ede J, et al. The landscape of tolerated genetic variation in humans and primates. Science. 2023;380:eabn8153. 10.1126/science.abn8197
Avsec Ž, Agarwal V, Visentin D, et al. Effective gene expression prediction from sequence by integrating long-range interactions. Nat Methods. 2021;18:1196-1203. 10.1038/s41592-021-01252-x
Kelley DR. Cross-species regulatory sequence activity prediction. PLoS Comput Biol. 2020;16:e1008050. 10.1371/journal.pcbi.1008050
Mourad R. Semi-supervised learning improves regulatory sequence prediction with unlabeled sequences. BMC Bioinform. 2023;24(1). 10.1186/s12859-023-05303-2
Li J, Wang J, Zhang P, et al. Deep learning of cross-species single-cell landscapes identifies conserved regulatory programs underlying cell types. Nat Genet. 2022;54:1711-1720. 10.1038/s41588-022-01197-7
Minnoye L, Taskiran II, Mauduit D, et al. Cross-species analysis of enhancer logic using deep learning. Genome Res. 2020;30:1815-1834. 10.1101/gr.260844.120
Database APS. AlphaFold Protein Structure Database n.d. Accessed July 14, 2023. https://alphafold.ebi.ac.uk/
ESM Metagenomic Atlas n.d. Accessed July 14, 2023. https://esmatlas.com/
Akdel M, Pires DEV, Pardo EP, et al. A structural biology community assessment of AlphaFold2 applications. Nat Struct Mol Biol. 2022;29:1056-1067. 10.1038/s41594-022-00849-w
Greenwood AK, Montgomery KS, Kauer N, et al. The AD Knowledge Portal: a repository for multi-omic data on Alzheimer's disease and aging. Curr Protoc Hum Genet. 2020;108:e105. 10.1002/cphg.105
Bertram L, McQueen MB, Mullin K, Blacker D, Tanzi RE. Systematic meta-analyses of Alzheimer disease genetic association studies: the AlzGene database. Nat Genet. 2007;39:17-23. 10.1038/ng1934
Bauermeister S, Orton C, Thompson S, et al. The Dementias Platform UK (DPUK) Data Portal. Eur J Epidemiol. 2020;35:601-611. 10.1007/s10654-020-00633-4
Larkin A, Marygold SJ, Antonazzo G, et al. FlyBase: updates to the Drosophila melanogaster knowledge base. Nucleic Acids Res. 2021;49:D899-D907. 10.1093/nar/gkaa1026
The Gene Ontology resource: enriching a GOld mine. Nucleic Acids Res. 2021;49:D325-D334.
Lonsdale J, Thomas J, Salvatore M, et al. The Genotype-Tissue Expression (GTEx) project. Nat Genet. 2013;45:580-585. 10.1038/ng.2653
Karczewski KJ, Francioli LC, Tiao G, et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature. 2020;581:434-443. 10.1038/s41586-020-2308-7
Alanis-Lobato G, Andrade-Navarro MA, Schaefer MH. HIPPIE v2.0: enhancing meaningfulness and reliability of protein- interaction networks. Nucleic Acids Res. 2016;45:D408-D414. 10.1093/nar/gkw985
Keane TM, Goodstadt L, Danecek P. Mouse genomic variation and its effect on phenotypes and gene regulation. Nature. 2011;477:289-294. 10.1038/nature10413
Lindsay S, Copp AJ. MRC-wellcome trust human developmental biology resource: enabling studies of human developmental gene expression. Trends Genet. 2005;21:586-590. 10.1016/j.tig.2005.08.011
Das S, Abou-Haidar R, Rabalais H, et al. The C-BIG repository: an institution-level open science platform. Neuroinformatics. 2022;20:139-153. 10.1007/s12021-021-09516-9
Ochoa D, Hercules A, Carmona M, et al. Open Targets Platform: supporting systematic drug-target identification and prioritisation. Nucleic Acids Res. 2021;49:D1302-D1310. 10.1093/nar/gkaa1027
Safaei J, Maňuch J, Gupta A, Stacho L, Pelech S. Prediction of 492 human protein kinase substrate specificities. Proteome Sci. 2011;9(Suppl 1):S6. 10.1186/1477-5956-9-S1-S6
Hornbeck PV, Zhang B, Murray B, Kornhauser JM, Latham V, Skrzypek E. PhosphoSitePlus, 2014: mutations, PTMs and recalibrations. Nucleic Acids Res. 2015;43:D512-D520. 10.1093/nar/gku1267
Liu Z, Wang Y, Gao T, et al. CPLM: a database of protein lysine modifications. Nucleic Acids Res. 2014;42:D531-D536. 10.1093/nar/gkt1093
Yu K, Zhang Q, Liu Z, et al. qPhos: a database of protein phosphorylation dynamics in humans. Nucleic Acids Res. 2019;47:D451-D458. 10.1093/nar/gky1052
Yu K, Wang Y, Zheng Y, et al. qPTM: an updated database for PTM dynamics in human, mouse, rat and yeast. Nucleic Acids Res. 2023;51:D479-D487. 10.1093/nar/gkac820
Jassal B, Matthews L, Viteri G, et al. The reactome pathway knowledgebase. Nucleic Acids Res. 2020;48:D498-D503. 10.1093/nar/gkz1031
Burley SK, Bhikadiya C, Bi C, et al. RCSB Protein Data Bank: powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences. Nucleic Acids Res. 2021;49:D437-D451. 10.1093/nar/gkaa1038
Sudlow C, Gallacher J, Allen N, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12:e1001779. 10.1371/journal.pmed.1001779
O'Shea O, Abranches E. UK stem cell bank. Stem Cell Res. 2020;49:102019. 10.1016/j.scr.2020.102019
Zhang H, Loriaux P, Eng J, et al. UniPep-a database for human N-linked glycosites: a resource for biomarker discovery. Genome Biol. 2006;7:R73. 10.1186/gb-2006-7-8-R73
Harris TW, Arnaboldi V, Cain S, et al. WormBase: a modern model organism information resource. Nucleic Acids Res. 2020;48:D762-D767. 10.1093/nar/gkz920
Agoston DV. How to translate time? the temporal aspect of human and rodent biology. Front Neurol. 2017;8:92. 10.3389/fneur.2017.00092
Perel P, Roberts I, Sena E, et al. Comparison of treatment effects between animal experiments and clinical trials: systematic review. BMJ. 2007;334:197. 10.1136/bmj.39048.407928.BE
van der Worp HB, Howells DW, Sena ES, et al. Can animal models of disease reliably inform human studies? PLoS Med. 2010;7:e1000245. 10.1371/journal.pmed.1000245
Baker RE, Peña J-M, Jayamohan J, Jérusalem A. Mechanistic models versus machine learning, a fight worth fighting for the biological community? Biol Lett. 2018;14: 20170660. 10.1098/rsbl.2017.0660
Mirams GR, Pathmanathan P, Gray RA, Challenor P, Clayton RH. Uncertainty and variability in computational and mathematical models of cardiac physiology. J Physiol. 2016;594:6833-6847. 10.1113/JP271671
Vanlier J, Tiemann CA, Hilbers PAJ, van Riel NAW. An integrated strategy for prediction uncertainty analysis. Bioinformatics. 2012;28:1130-1135. 10.1093/bioinformatics/bts088
Vanlier J, Tiemann CA, Hilbers PAJ, van Riel NAW. Parameter uncertainty in biochemical models described by ordinary differential equations. Math Biosci. 2013;246:305-314. 10.1016/j.mbs.2013.03.006
Rozendaal YJW, Wang Y, Paalvast Y, et al. In vivo and in silico dynamics of the development of Metabolic Syndrome. PLoS Comput Biol. 2018;14:e1006145. 10.1371/journal.pcbi.1006145
Tiemann CA, Vanlier J, Oosterveer MH, Groen AK, Hilbers PAJ, van Riel NAW. Parameter trajectory analysis to identify treatment effects of pharmacological interventions. PLoS Comput Biol. 2013;9:e1003166. 10.1371/journal.pcbi.1003166
Breiman L. Statistical modeling: the two cultures (with comments and a rejoinder by the author). SSO Schweiz Monatsschr Zahnheilkd. 2001;16:199-231. 10.1214/ss/1009213726
Luechtefeld T, Marsh D, Rowlands C, Hartung T. Machine Learning of Toxicological Big Data Enables Read-Across Structure Activity Relationships (RASAR) outperforming animal test reproducibility. Toxicol Sci. 2018;165:198-212. 10.1093/toxsci/kfy152
Rhrissorrakrai K, Belcastro V, Bilal E, et al. Understanding the limits of animal models as predictors of human biology: lessons learned from the sbv IMPROVER species translation challenge. Bioinformatics. 2015;31:471-483. 10.1093/bioinformatics/btu611
Anvar SY, Tucker A, Vinciotti V, et al. Interspecies translation of disease networks increases robustness and predictive accuracy. PLoS Comput Biol. 2011;7:e1002258. 10.1371/journal.pcbi.1002258
Brubaker DK, Proctor EA, Haigis KM, Lauffenburger DA. Computational translation of genomic responses from experimental model systems to humans. PLoS Comput Biol. 2019;15:e1006286. 10.1371/journal.pcbi.1006286
Normand R, Du W, Briller M, et al. Found in translation: a machine learning model for mouse-to-human inference. Nat Methods. 2018;15:1067-1073. 10.1038/s41592-018-0214-9
Perosa V, Scherlek AA, Kozberg MG, et al. Deep learning assisted quantitative assessment of histopathological markers of Alzheimer's disease and cerebral amyloid angiopathy. Acta Neuropathol Commun. 2021;9:141. 10.1186/s40478-021-01235-1
Signaevsky M, Prastawa M, Farrell K, et al. Artificial intelligence in neuropathology: deep learning-based assessment of Tauopathy. Lab Invest. 2019;99:1019-1029. 10.1038/s41374-019-0202-4
Xie C, Zhuang X-X, Niu Z, et al. Amelioration of Alzheimer's disease pathology by mitophagy inducers identified via machine learning and a cross-species workflow. Nat Biomed Eng. 2022;6:76-93. 10.1038/s41551-021-00819-5
Stumpf PS, Du X, Imanishi H, et al. Transfer learning efficiently maps bone marrow cell types from mouse to human using single-cell RNA sequencing. Commun Biol. 2020;3:736. 10.1038/s42003-020-01463-6
Eraslan G, Avsec Ž, Gagneur J, Theis FJ. Deep learning: new computational modelling techniques for genomics. Nat Rev Genet. 2019;20:389-403. 10.1038/s41576-019-0122-6
Argelaguet R, Cuomo ASE, Stegle O, Marioni JC. Computational principles and challenges in single-cell data integration. Nat Biotechnol. 2021;39:1202-1215. 10.1038/s41587-021-00895-7
Cao Z-J, Wei L, Lu S, Yang D-C, Gao G. Searching large-scale scRNA-seq databases via unbiased cell embedding with Cell BLAST. Nat Commun. 2020;11:3458. 10.1038/s41467-020-17281-7
Xiang R, Wang W, Yang L, Wang S, Xu C, Chen X. A comparison for dimensionality reduction methods of single-cell RNA-seq data. Front Genet. 2021;12:646936. 10.3389/fgene.2021.646936
Lazic SE. The problem of pseudoreplication in neuroscientific studies: is it affecting your analysis? BMC Neurosci. 2010;11:5. 10.1186/1471-2202-11-5
Squair JW, Gautier M, Kathe C, et al. Confronting false discoveries in single-cell differential expression. Nat Commun. 2021;12:5692. 10.1038/s41467-021-25960-2
Murphy AE, Skene NG. A balanced measure shows superior performance of pseudobulk methods in single-cell RNA-sequencing analysis. Nat Commun. 2022;13:7851. 10.1038/s41467-022-35519-4
Lotfollahi M, Wolf FA, Theis FJ. scGen predicts single-cell perturbation responses. Nat Methods. 2019;16:715-721. 10.1038/s41592-019-0494-8
Lotfollahi M, Susmelj AK, De Donno C, et al. Learning interpretable cellular responses to complex perturbations in high-throughput screens. bioRxiv 2021:2021.04.14.439903. 10.1101/2021.04.14.439903
Cui H, Wang C, Maan H, Pang K, Luo F, Wang B, scGPT: Towards Building a Foundation Model for Single-Cell Multi-omics Using Generative AI. bioRxiv 2023:2023.04.30.538439. 10.1101/2023.04.30.538439
Yang F, Wang W, Wang F, et al. scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data. Nature Machine Intelligence. 2022;4:852-866. 10.1038/s42256-022-00534-z
Theodoris CV, Xiao L, Chopra A, et al. Transfer learning enables predictions in network biology. Nature. 2023;618:616-624. 10.1038/s41586-023-06139-9
Zhang X-M, Liang L, Liu L, Tang M-J. Graph neural networks and their current applications in bioinformatics. Front Genet. 2021;12:690049. 10.3389/fgene.2021.690049
Burkhart JG, Wu G, Song X, et al. Biology-inspired graph neural network encodes reactome and reveals biochemical reactions of disease. Patterns Prejudice. 2023;4:100758. 10.1016/j.patter.2023.100758
You R, Yao S, Mamitsuka H, Zhu S. DeepGraphGO: graph neural network for large-scale, multispecies protein function prediction. Bioinformatics. 2021;37:i262-i271. 10.1093/bioinformatics/btab270
Ktena SI, Parisot S, Ferrante E, et al. Metric learning with spectral graph convolutions on brain connectivity networks. Neuroimage. 2018;169:431-442. 10.1016/j.neuroimage.2017.12.052
Song T-A, Chowdhury SR, Yang F, et al. Graph convolutional neural networks for Alzheimer's disease classification. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). IEEE; 2019:414-417. 10.1109/ISBI.2019.8759531
Klepl D, He F, Wu M, Blackburn DJ, Sarrigiannis P. EEG-based graph neural network classification of alzheimer's disease: an empirical evaluation of functional connectivity methods. IEEE Trans Neural Syst Rehabil Eng. 2022;30:2651-2660. 10.1109/TNSRE.2022.3204913
Rao A, Vg S, Joseph T, Kotte S, Sivadasan N, Srinivasan R. Phenotype-driven gene prioritization for rare diseases using graph convolution on heterogeneous networks. BMC Med Genomics. 2018;11:57. 10.1186/s12920-018-0372-8
Liu Q, Hu Z, Jiang R, Zhou M. DeepCDR: a hybrid graph convolutional network for predicting cancer drug response. Bioinformatics. 2020;36:i911-i918. 10.1093/bioinformatics/btaa822
Singha M, Pu L, Shawky A-E-M, et al. GraphGR: A graph neural network to predict the effect of pharmacotherapy on the cancer cell growth. bioRxiv 2020:2020.05.20.107458. 10.1101/2020.05.20.107458
Kazi A, Shekarforoush S, Arvind Krishna S, et al. Graph Convolution Based Attention Model for Personalized Disease Prediction. Medical Image Computing and Computer Assisted Intervention - MICCAI 2019. Springer International Publishing; 2019:122-130. 10.1007/978-3-030-32251-9_14
Wang T, Shao W, Huang Z, et al. MOGONET integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification. Nat Commun. 2021;12:3445. 10.1038/s41467-021-23774-w
Goodman B, Flaxman S. European union regulations on algorithmic decision-making and a ‘‘right to explanation. AIMag. 2017;38:50-57. 10.1609/aimag.v38i3.2741
Chen JH, Asch SM. Machine learning and prediction in medicine-beyond the peak of inflated expectations. N Engl J Med. 2017;376:2507-2509. 10.1056/NEJMp1702071
Wang F, Kaushal R, Khullar D. Should health care demand interpretable artificial intelligence or accept “black box” medicine? Ann Intern Med. 2020;172:59-60. 10.7326/M19-2548
Heaven D. Why deep-learning AIs are so easy to fool. Nature. 2019;574:163-166. 10.1038/d41586-019-03013-5
Lapuschkin S, Wäldchen S, Binder A, Montavon G, Samek W, Müller K-R. Unmasking Clever Hans predictors and assessing what machines really learn. Nat Commun. 2019;10:1096. 10.1038/s41467-019-08987-4
Barredo Arrieta A, Díaz-Rodríguez N, Del Ser J, et al. Explainable Artificial Intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf Fusion. 2020;58:82-115. 10.1016/j.inffus.2019.12.012
Finlayson SG, Bowers JD, Ito J, Zittrain JL, Beam AL, Kohane IS. Adversarial attacks on medical machine learning. Science. 2019;363:1287-1289. 10.1126/science.aaw4399
Martens D, Vanthienen J, Verbeke W, Baesens B. Performance of classification models from a user perspective. Decis Support Syst. 2011;51:782-793. 10.1016/j.dss.2011.01.013
Guidotti R, Monreale A, Ruggieri S, Turini F, Giannotti F, Pedreschi D. A survey of methods for explaining black box models. ACM Comput Surv. 2018;51:1-42. 10.1145/3236009
Wiens J, Saria S, Sendak M, et al. Do no harm: a roadmap for responsible machine learning for health care. Nat Med. 2019;25:1337-1340. 10.1038/s41591-019-0548-6
Tjoa E, Guan C. A survey on explainable artificial intelligence (XAI): toward medical XAI. IEEE Trans Neural Netw Learn Syst. 2021;32:4793-4813. 10.1109/TNNLS.2020.3027314
Jiménez-Luna J, Grisoni F, Schneider G. Drug discovery with explainable artificial intelligence. Nature Machine Intelligence. 2020;2:573-584. 10.1038/s42256-020-00236-4
Kırboğa KK, Abbasi S, Küçüksille EU. Explainability and white box in drug discovery. Chem Biol Drug Des. 2023;102:217-233. 10.1111/cbdd.14262
Vo TH, Nguyen NTK, Kha QH, Le NQK. On the road to explainable AI in drug-drug interactions prediction: a systematic review. Comput Struct Biotechnol J. 2022;20:2112-2123. 10.1016/j.csbj.2022.04.021
van der Velden BHM, Kuijf HJ, Gilhuijs KGA, Viergever MA. Explainable artificial intelligence (XAI) in deep learning-based medical image analysis. Med Image Anal. 2022;79:102470. 10.1016/j.media.2022.102470
Cole J, Wood D, Booth T. Visual attention as a model for interpretable neuroimage classification in dementia. Alzheimers Dement. 2020;16:e037351. 10.1002/alz.037351
Essemlali A, St-Onge E, Descoteaux M, Jodoin P-M. Understanding Alzheimer disease's structural connectivity through explainable AI. In: Arbel T, Ben Ayed I, de Bruijne M, Descoteaux M, Lombaert H, Pal C, eds. Proceedings of the Third Conference on Medical Imaging with Deep Learning. PMLR; 217-229. PMLR; 06-08 Jul 2020.
El-Sappagh S, Alonso JM, Islam SMR, Sultan AM, Kwak KS. A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer's disease. Sci Rep. 2021;11:2660. 10.1038/s41598-021-82098-3
Bycroft C, Freeman C, Petkova D, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature. 2018;562:203-209. 10.1038/s41586-018-0579-z
England G. Newborn genomes programme. Genomics England. 2022. Accessed July 14, 2023. https://www.genomicsengland.co.uk/initiatives/newborns
Ritchie MD, Holzinger ER, Li R, Pendergrass SA, Kim D. Methods of integrating data to uncover genotype-phenotype interactions. Nat Rev Genet. 2015;16:85-97. 10.1038/nrg3868
Kulasingam V, Pavlou MP, Diamandis EP. Integrating high-throughput technologies in the quest for effective biomarkers for ovarian cancer. Nat Rev Cancer. 2010;10:371-378. 10.1038/nrc2831
Nariai N, Kolaczyk ED, Kasif S. Probabilistic protein function prediction from heterogeneous genome-wide data. PLoS One. 2007;2:e337. 10.1371/journal.pone.0000337
Zhang S, Fan R, Liu Y, Chen S, Liu Q, Zeng W. Applications of transformer-based language models in bioinformatics: a survey. Bioinform Adv. 2023;3:vbad001. 10.1093/bioadv/vbad001
Haupt CE, Marks M. AI-Generated Medical Advice-GPT and Beyond. JAMA. 2023;329:1349-1350. 10.1001/jama.2023.5321
Cheng K, Guo Q, He Y, Lu Y, Gu S, Wu H. Exploring the Potential of GPT-4 in biomedical engineering: the dawn of a new era. Ann Biomed Eng. 2023;51:1645-1653. 10.1007/s10439-023-03221-1
Singhal K, Azizi S, Tu T, et al. Large language models encode clinical knowledge. Nature. 2023;620:172-180. 10.1038/s41586-023-06291-2
Zhang K, Yu J, Yan Z, et al. BiomedGPT: A Unified and Generalist Biomedical Generative Pre-trained Transformer for Vision, Language, and Multimodal Tasks. arXiv [csCL]. 2023.
BioMedLM. BioMedLM n.d. Accessed July 29, 2023. https://github.com/stanford-crfm/BioMedLM
Jin Q, Yang Y, Chen Q, Lu Z, GeneGPT: Augmenting Large Language Models with Domain Tools for Improved Access to Biomedical Information. ArXiv 2023.
Luo R, Sun L, Xia Y, et al. BioGPT: generative pre-trained transformer for biomedical text generation and mining. Brief Bioinform. 2022;23:bbac409. 10.1093/bib/bbac409
Gu Y, Tinn R, Cheng H, et al. Domain-specific language model pretraining for biomedical natural language processing. arXiv [csCL]. 2020.
Yasunaga M, Leskovec J, Ling P. LinkBERT: Pretraining Language Models with Document Links. arXiv [csCL]. 2022.
Taylor R, Kardas M, Cucurull G, et al. Galactica: A Large Language Model for Science. arXiv [csCL]. 2022.
Shin H-C, Zhang Y, Bakhturina E, et al. BioMegatron: Larger Biomedical Domain Language Model. arXiv [csCL]. 2020.
Auto-GPT: An experimental open-source attempt to make GPT-4 fully autonomous. Github; n.d.