Bertelli MO, Munir K, Harris J, Salvador-Carulla L. “Intellectual developmental disorders”: reflections on the international consensus document for redefining “mental retardation-intellectual disability” in ICD-11. Adv Ment Health Intellect Disabil. 2016;10:36–58.
Article
PubMed
PubMed Central
Google Scholar
Fink A, Wright L, Wormald R. Detection and prevention of treatable visual failure in general practice: room for improvement. Br J Gen Pract. 1994;44:587–9.
CAS
PubMed
PubMed Central
Google Scholar
Carvill S. Sensory impairments, intellectual disability and psychiatry. J Intellect Disabil Res. 2001;45:467–83.
Article
CAS
PubMed
Google Scholar
Park Y, Greene CS. A parasite’s perspective on data sharing. GigaScience. 2018;7(11):giy129.
Article
PubMed Central
Google Scholar
Raichle ME. A brief history of human brain mapping. Trends Neurosci. 2009;32:118–26.
Article
CAS
PubMed
Google Scholar
Singh SP. Magnetoencephalography: basic principles. Ann Indian Acad Neurol. 2014;17(Suppl 1):S107–12.
Article
PubMed
PubMed Central
Google Scholar
Levin AR, Varcin KJ, O’Leary HM, Tager-Flusberg H, Nelson CA. EEG power at 3 months in infants at high familial risk for autism. J Neurodev Disord. 2017;9:34.
Article
PubMed
PubMed Central
Google Scholar
Gabard-Durnam L, Tierney AL, Vogel-Farley V, Tager-Flusberg H, Nelson CA. Alpha asymmetry in infants at risk for autism spectrum disorders. J Autism Dev Disord. 2015;45:473–80.
Article
PubMed
PubMed Central
Google Scholar
Gabard-Durnam LJ, Wilkinson C, Kapur K, Tager-Flusberg H, Levin AR, Nelson CA. Longitudinal EEG power in the first postnatal year differentiates autism outcomes. Nat Commun. 2019;10:4188.
Article
PubMed
PubMed Central
CAS
Google Scholar
Cherkassky VL, Kana RK, Keller TA, Just MA. Functional connectivity in a baseline resting-state network in autism. Neuroreport. 2006;17:1687–90.
Article
PubMed
Google Scholar
Murias M, Webb SJ, Greenson J, Dawson G. Resting state cortical connectivity reflected in EEG coherence in individuals with autism. Biol Psychiatry. 2007;62:270–3.
Article
PubMed
PubMed Central
Google Scholar
Arns M, Conners CK, Kraemer HC. A decade of EEG theta/beta ratio research in ADHD: a meta-analysis. J Atten Disord. 2013;17:374–83.
Article
PubMed
Google Scholar
Oberman LM, Hubbard EM, McCleery JP, Altschuler EL, Ramachandran VS, Pineda JA. EEG evidence for mirror neuron dysfunction in autism spectrum disorders. Brain Res Cogn Brain Res. 2005;24:190–8.
Article
PubMed
Google Scholar
Ethridge LE, De Stefano LA, Schmitt LM, Woodruff NE, Brown KL, Tran M, et al. Auditory EEG biomarkers in Fragile X syndrome: clinical relevance. Front Integr Neurosci. 2019;13:60.
Article
PubMed
PubMed Central
Google Scholar
Anand SS, Singh H, Dash AK. Clinical applications of PET and PET-CT. Med J Armed Forces India. 2009;65:353–8.
Article
CAS
PubMed
PubMed Central
Google Scholar
Chugani DC, Muzik O, Behen M, Rothermel R, Janisse JJ, Lee J, et al. Developmental changes in brain serotonin synthesis capacity in autistic and nonautistic children. Ann Neurol. 1999;45:287–95.
Article
CAS
PubMed
Google Scholar
Ekmekcioglu E, Cimtay Y. Loughborough University Multimodal Emotion Dataset-2. figshare. Dataset. 2020. https://doi.org/10.6084/m9.figshare.12644033.v5.
Koelstra S, Muhl C, Soleymani M, Lee J-S, Yazdani A, Ebrahimi T, et al. DEAP: a database for emotion analysis; using physiological signals. IEEE Trans Affect Comput. 2012;3:18–31.
Article
Google Scholar
Duan R-N, Zhu J-Y, Lu B-L. Differential entropy feature for EEG-based emotion classification. In: 2013 6th international IEEE/EMBS conference on neural engineering (NER); 2013. p. 81–4.
Chapter
Google Scholar
Zheng W-L, Lu B-L. Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks. IEEE Trans Auton Ment Dev. 2015;7:162–75.
Article
Google Scholar
Ramaswami G, Won H, Gandal MJ, Haney J, Wang JC, Wong CCY, et al. Integrative genomics identifies a convergent molecular subtype that links epigenomic with transcriptomic differences in autism. Nat Commun. 2020;11:4873.
Article
CAS
PubMed
PubMed Central
Google Scholar
Gupta S, Ellis SE, Ashar FN, Moes A, Bader JS, Zhan J, et al. Transcriptome analysis reveals dysregulation of innate immune response genes and neuronal activity-dependent genes in autism. Nat Commun. 2014;5:5748.
Article
CAS
PubMed
Google Scholar
Wright C, Shin JH, Rajpurohit A, Deep-Soboslay A, Collado-Torres L, Brandon NJ, et al. Altered expression of histamine signaling genes in autism spectrum disorder. Transl Psychiatry. 2017;7:e1126.
Article
CAS
PubMed
PubMed Central
Google Scholar
Li J, Shi M, Ma Z, Zhao S, Euskirchen G, Ziskin J, et al. Integrated systems analysis reveals a molecular network underlying autism spectrum disorders. Mol Syst Biol. 2014;10:774.
Article
PubMed
PubMed Central
CAS
Google Scholar
Parikshak NN, Swarup V, Belgard TG, Irimia M, Ramaswami G, Gandal MJ, et al. Genome-wide changes in lncRNA, splicing, and regional gene expression patterns in autism. Nature. 2016;540:423–7.
Article
CAS
PubMed
PubMed Central
Google Scholar
Ziats MN, Rennert OM. Aberrant expression of long noncoding RNAs in autistic brain. J Mol Neurosci. 2013;49:589–93.
Article
CAS
PubMed
Google Scholar
Rahman MR, Petralia MC, Ciurleo R, Bramanti A, Fagone P, Shahjaman M, et al. Comprehensive analysis of RNA-Seq gene expression profiling of brain transcriptomes reveals novel genes, regulators, and pathways in autism spectrum disorder. Brain Sci. 2020;10:E747.
Article
PubMed
CAS
Google Scholar
Ch’ng C, Kwok W, Rogic S, Pavlidis P. Meta-analysis of gene expression in autism spectrum disorder. Autism Res. 2015;8:593–608.
Article
PubMed
PubMed Central
Google Scholar
He Y, Zhou Y, Ma W, Wang J. An integrated transcriptomic analysis of autism spectrum disorder. Sci Rep. 2019;9:11818.
Article
PubMed
PubMed Central
CAS
Google Scholar
Forés-Martos J, Catalá-López F, Sánchez-Valle J, Ibáñez K, Tejero H, Palma-Gudiel H, et al. Transcriptomic metaanalyses of autistic brains reveals shared gene expression and biological pathway abnormalities with cancer. Mol Autism. 2019;10:17.
Article
PubMed
PubMed Central
Google Scholar
Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, et al. NCBI GEO: archive for functional genomics data sets--update. Nucleic Acids Res. 2013;41 Database issue:D991–5.
Google Scholar
Athar A, Füllgrabe A, George N, Iqbal H, Huerta L, Ali A, et al. ArrayExpress update - from bulk to single-cell expression data. Nucleic Acids Res. 2019;47:D711–5.
Article
CAS
PubMed
Google Scholar
Mailman MD, Feolo M, Jin Y, Kimura M, Tryka K, Bagoutdinov R, et al. The NCBI dbGaP database of genotypes and phenotypes. Nat Genet. 2007;39:1181–6.
Article
CAS
PubMed
PubMed Central
Google Scholar
Vogel Ciernia A, LaSalle J. The landscape of DNA methylation amid a perfect storm of autism aetiologies. Nat Rev Neurosci. 2016;17:411–23.
Article
PubMed
CAS
Google Scholar
Lieberman-Aiden E, van Berkum NL, Williams L, Imakaev M, Ragoczy T, Telling A, et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science. 2009;326:289–93.
Article
CAS
PubMed
PubMed Central
Google Scholar
Buenrostro JD, Giresi PG, Zaba LC, Chang HY, Greenleaf WJ. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat Methods. 2013;10:1213–8.
Article
CAS
PubMed
PubMed Central
Google Scholar
Greenberg MVC, Bourc’his D. The diverse roles of DNA methylation in mammalian development and disease. Nat Rev Mol Cell Biol. 2019;20:590–607.
Article
CAS
PubMed
Google Scholar
Ladd-Acosta C, Hansen KD, Briem E, Fallin MD, Kaufmann WE, Feinberg AP. Common DNA methylation alterations in multiple brain regions in autism. Mol Psychiatry. 2014;19:862–71.
Article
CAS
PubMed
Google Scholar
Nardone S, Sams DS, Reuveni E, Getselter D, Oron O, Karpuj M, et al. DNA methylation analysis of the autistic brain reveals multiple dysregulated biological pathways. Transl Psychiatry. 2014;4:e433.
Article
CAS
PubMed
PubMed Central
Google Scholar
Andrews SV, Sheppard B, Windham GC, Schieve LA, Schendel DE, Croen LA, et al. Case-control meta-analysis of blood DNA methylation and autism spectrum disorder. Mol Autism. 2018;9:40.
Article
PubMed
PubMed Central
CAS
Google Scholar
Habib N, Avraham-Davidi I, Basu A, Burks T, Shekhar K, Hofree M, et al. Massively-parallel single nucleus RNA-seq with DroNc-seq. Nat Methods. 2017;14:955–8.
Article
CAS
PubMed
PubMed Central
Google Scholar
Macosko EZ, Basu A, Satija R, Nemesh J, Shekhar K, Goldman M, et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell. 2015;161:1202–14.
Article
CAS
PubMed
PubMed Central
Google Scholar
Maglorius Renkilaraj MRL, Baudouin L, Wells CM, Doulazmi M, Wehrlé R, Cannaya V, et al. The intellectual disability protein PAK3 regulates oligodendrocyte precursor cell differentiation. Neurobiol Dis. 2017;98:137–48.
Article
CAS
PubMed
Google Scholar
Lake BB, Ai R, Kaeser GE, Salathia NS, Yung YC, Liu R, et al. Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain. Science. 2016;352:1586–90.
Article
CAS
PubMed
PubMed Central
Google Scholar
Zhong S, Zhang S, Fan X, Wu Q, Yan L, Dong J, et al. A single-cell RNA-seq survey of the developmental landscape of the human prefrontal cortex. Nature. 2018;555:524–8.
Article
CAS
PubMed
Google Scholar
Lake BB, Chen S, Sos BC, Fan J, Kaeser GE, Yung YC, et al. Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain. Nat Biotechnol. 2018;36:70–80.
Article
CAS
PubMed
Google Scholar
Mathys H, Davila-Velderrain J, Peng Z, Gao F, Mohammadi S, Young JZ, et al. Single-cell transcriptomic analysis of Alzheimer’s disease. Nature. 2019;570:332–7.
Article
CAS
PubMed
PubMed Central
Google Scholar
Velmeshev D, Schirmer L, Jung D, Haeussler M, Perez Y, Mayer S, et al. Single-cell genomics identifies cell type-specific molecular changes in autism. Science. 2019;364:685–9.
Article
CAS
PubMed
PubMed Central
Google Scholar
Nagy C, Maitra M, Tanti A, Suderman M, Théroux J-F, Davoli MA, et al. Single-nucleus transcriptomics of the prefrontal cortex in major depressive disorder implicates oligodendrocyte precursor cells and excitatory neurons. Nat Neurosci. 2020;23:771–81.
Article
CAS
PubMed
Google Scholar
Sanchis-Juan A, Bitsara C, Low KY, Carss KJ, French CE, Spasic-Boskovic O, et al. Rare genetic variation in 135 families with family history suggestive of X-linked intellectual disability. Front Genet. 2019;10:578.
Article
CAS
PubMed
PubMed Central
Google Scholar
Janecka M, Kodesh A, Levine SZ, Lusskin SI, Viktorin A, Rahman R, et al. Association of autism spectrum disorder with prenatal exposure to medication affecting neurotransmitter systems. JAMA Psychiatry. 2018;75:1217–24.
Article
PubMed
PubMed Central
Google Scholar
Iossifov I, Levy D, Allen J, Ye K, Ronemus M, Lee Y-H, et al. Low load for disruptive mutations in autism genes and their biased transmission. Proc Natl Acad Sci U S A. 2015;112:E5600–7.
Article
CAS
PubMed
PubMed Central
Google Scholar
Stessman HAF, Xiong B, Coe BP, Wang T, Hoekzema K, Fenckova M, et al. Targeted sequencing identifies 91 neurodevelopmental-disorder risk genes with autism and developmental-disability biases. Nat Genet. 2017;49:515–26.
Article
CAS
PubMed
PubMed Central
Google Scholar
Iossifov I, O’Roak BJ, Sanders SJ, Ronemus M, Krumm N, Levy D, et al. The contribution of de novo coding mutations to autism spectrum disorder. Nature. 2014;515:216–21.
Article
CAS
PubMed
PubMed Central
Google Scholar
Satterstrom FK, Kosmicki JA, Wang J, Breen MS, De Rubeis S, An J-Y, et al. Large-scale exome sequencing study implicates both developmental and functional changes in the neurobiology of autism. Cell. 2020;180:568–584.e23.
Article
CAS
PubMed
PubMed Central
Google Scholar
Sanders SJ, Murtha MT, Gupta AR, Murdoch JD, Raubeson MJ, Willsey AJ, et al. De novo mutations revealed by whole-exome sequencing are strongly associated with autism. Nature. 2012;485:237–41.
Article
CAS
PubMed
PubMed Central
Google Scholar
Forsingdal A, Fejgin K, Nielsen V, Werge T, Nielsen J. 15q13.3 homozygous knockout mouse model display epilepsy-, autism- and schizophrenia-related phenotypes. Transl Psychiatry. 2016;6:e860.
Article
CAS
PubMed
PubMed Central
Google Scholar
Ben-Shachar S, Lanpher B, German JR, Qasaymeh M, Potocki L, Nagamani SCS, et al. Microdeletion 15q13.3: a locus with incomplete penetrance for autism, mental retardation, and psychiatric disorders. J Med Genet. 2009;46:382–8.
Article
CAS
PubMed
Google Scholar
Helbig I, Mefford HC, Sharp AJ, Guipponi M, Fichera M, Franke A, et al. 15q13.3 microdeletions increase risk of idiopathic generalized epilepsy. Nat Genet. 2009;41:160–2.
Article
CAS
PubMed
PubMed Central
Google Scholar
Sharp AJ, Mefford HC, Li K, Baker C, Skinner C, Stevenson RE, et al. A recurrent 15q13.3 microdeletion syndrome associated with mental retardation and seizures. Nat Genet. 2008;40:322–8.
Article
CAS
PubMed
PubMed Central
Google Scholar
Girirajan S, Rosenfeld JA, Cooper GM, Antonacci F, Siswara P, Itsara A, et al. A recurrent 16p12.1 microdeletion supports a two-hit model for severe developmental delay. Nat Genet. 2010;42:203–9.
Article
CAS
PubMed
PubMed Central
Google Scholar
Antonacci F, Kidd JM, Marques-Bonet T, Teague B, Ventura M, Girirajan S, et al. A large and complex structural polymorphism at 16p12.1 underlies microdeletion disease risk. Nat Genet. 2010;42:745–50.
Article
CAS
PubMed
PubMed Central
Google Scholar
Rees E, Walters JTR, Chambert KD, O’Dushlaine C, Szatkiewicz J, Richards AL, et al. CNV analysis in a large schizophrenia sample implicates deletions at 16p12.1 and SLC1A1 and duplications at 1p36.33 and CGNL1. Hum Mol Genet. 2014;23:1669–76.
Article
CAS
PubMed
Google Scholar
Kushima I, Aleksic B, Nakatochi M, Shimamura T, Okada T, Uno Y, et al. Comparative analyses of copy-number variation in autism spectrum disorder and schizophrenia reveal etiological overlap and biological insights. Cell Rep. 2018;24:2838–56.
Article
CAS
PubMed
Google Scholar
Gudmundsson OO, Walters GB, Ingason A, Johansson S, Zayats T, Athanasiu L, et al. Attention-deficit hyperactivity disorder shares copy number variant risk with schizophrenia and autism spectrum disorder. Transl Psychiatry. 2019;9:258.
Article
PubMed
PubMed Central
CAS
Google Scholar
Harold D, Abraham R, Hollingworth P, Sims R, Gerrish A, Hamshere ML, et al. Genome-wide association study identifies variants at CLU and PICALM associated with Alzheimer’s disease. Nat Genet. 2009;41:1088–93.
Article
CAS
PubMed
PubMed Central
Google Scholar
Hollingworth P, Harold D, Sims R, Gerrish A, Lambert J-C, Carrasquillo MM, et al. Common variants at ABCA7, MS4A6A/MS4A4E, EPHA1, CD33 and CD2AP are associated with Alzheimer’s disease. Nat Genet. 2011;43:429–35.
Article
CAS
PubMed
PubMed Central
Google Scholar
Nalls MA, Blauwendraat C, Vallerga CL, Heilbron K, Bandres-Ciga S, Chang D, et al. Identification of novel risk loci, causal insights, and heritable risk for Parkinson’s disease: a meta-analysis of genome-wide association studies. Lancet Neurol. 2019;18:1091–102.
Article
CAS
PubMed
PubMed Central
Google Scholar
International League Against Epilepsy Consortium on Complex Epilepsies. Electronic address: epilepsy-austin@unimelb.edu.au. Genetic determinants of common epilepsies: a meta-analysis of genome-wide association studies. Lancet Neurol. 2014;13:893–903.
Article
CAS
Google Scholar
Kaufmann WE, Kidd SA, Andrews HF, Budimirovic DB, Esler A, Haas-Givler B, et al. Autism spectrum disorder in Fragile X syndrome: cooccurring conditions and current treatment. Pediatrics. 2017;139(Suppl 3):S194–206.
Article
PubMed
Google Scholar
Startin CM, Hamburg S, Hithersay R, Davies A, Rodger E, Aggarwal N, et al. The LonDownS adult cognitive assessment to study cognitive abilities and decline in Down syndrome. Wellcome Open Res. 2016;1:11.
Article
PubMed
PubMed Central
Google Scholar
Li M, Santpere G, Imamura Kawasawa Y, Evgrafov OV, Gulden FO, Pochareddy S, et al. Integrative functional genomic analysis of human brain development and neuropsychiatric risks. Science. 2018;362:eaat7615.
Article
CAS
PubMed
PubMed Central
Google Scholar
PsychENCODE Consortium, Akbarian S, Liu C, Knowles JA, Vaccarino FM, Farnham PJ, et al. The PsychENCODE project. Nat Neurosci. 2015;18:1707–12.
Article
CAS
Google Scholar
Jourdon A, Scuderi S, Capauto D, Abyzov A, Vaccarino FM. PsychENCODE and beyond: transcriptomics and epigenomics of brain development and organoids. Neuropsychopharmacology. 2021;46:70–85.
Article
PubMed
Google Scholar
Gorkin DU, Barozzi I, Zhao Y, Zhang Y, Huang H, Lee AY, et al. An atlas of dynamic chromatin landscapes in mouse fetal development. Nature. 2020;583:744–51.
Article
CAS
PubMed
PubMed Central
Google Scholar
Song L, Pan S, Zhang Z, Jia L, Chen W-H, Zhao X-M. STAB: a spatio-temporal cell atlas of the human brain. Nucleic Acids Res. 2021;49:D1029–37.
Article
CAS
PubMed
Google Scholar
Pașca SP. The rise of three-dimensional human brain cultures. Nature. 2018;553:437–45.
Article
PubMed
CAS
Google Scholar
Duval K, Grover H, Han L-H, Mou Y, Pegoraro AF, Fredberg J, et al. Modeling physiological events in 2D vs. 3D cell culture. Physiology (Bethesda). 2017;32:266–77.
CAS
Google Scholar
Meshorer E, Testa G, editors. Stem cell epigenetics. Walthum: Elsevier; 2020.
Google Scholar
Gordon A, Yoon S-J, Tran SS, Makinson CD, Park JY, Andersen J, et al. Long-term maturation of human cortical organoids matches key early postnatal transitions. Nat Neurosci. 2021;24:331–42.
Article
CAS
PubMed
PubMed Central
Google Scholar
Trevino AE, Sinnott-Armstrong N, Andersen J, Yoon S-J, Huber N, Pritchard JK, et al. Chromatin accessibility dynamics in a model of human forebrain development. Science. 2020;367:eaay1645.
Article
CAS
PubMed
PubMed Central
Google Scholar
Kanton S, Boyle MJ, He Z, Santel M, Weigert A, Sanchís-Calleja F, et al. Organoid single-cell genomic atlas uncovers human-specific features of brain development. Nature. 2019;574:418–22.
Article
CAS
PubMed
Google Scholar
Bakken TE, Miller JA, Ding S-L, Sunkin SM, Smith KA, Ng L, et al. A comprehensive transcriptional map of primate brain development. Nature. 2016;535:367–75.
Article
CAS
PubMed
PubMed Central
Google Scholar
Leigh SR. Brain growth, life history, and cognition in primate and human evolution. Am J Primatol. 2004;62:139–64.
Article
CAS
PubMed
Google Scholar
Butler A, Hoffman P, Smibert P, Papalexi E, Satija R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol. 2018;36:411–20.
Article
CAS
PubMed
PubMed Central
Google Scholar
Pollen AA, Bhaduri A, Andrews MG, Nowakowski TJ, Meyerson OS, Mostajo-Radji MA, et al. Establishing cerebral organoids as models of human-specific brain evolution. Cell. 2019;176:743–756.e17.
Article
CAS
PubMed
PubMed Central
Google Scholar
Mariani J, Coppola G, Zhang P, Abyzov A, Provini L, Tomasini L, et al. FOXG1-dependent dysregulation of GABA/glutamate neuron differentiation in autism spectrum disorders. Cell. 2015;162:375–90.
Article
CAS
PubMed
PubMed Central
Google Scholar
Villa C, Combi R, Conconi D, Lavitrano M. Patient-derived induced pluripotent stem cells (iPSCs) and cerebral organoids for drug screening and development in autism spectrum disorder: opportunities and challenges. Pharmaceutics. 2021;13:280.
Article
CAS
PubMed
PubMed Central
Google Scholar
Adrien JL, Faure M, Perrot A, Hameury L, Garreau B, Barthelemy C, et al. Autism and family home movies: preliminary findings. J Autism Dev Disord. 1991;21:43–9.
Article
CAS
PubMed
Google Scholar
Adrien JL, Perrot A, Sauvage D, Leddet I, Larmande C, Hameury L, et al. Early symptoms in autism from family home movies. Evaluation and comparison between 1st and 2nd year of life using I.B.S.E. scale. Acta Paedopsychiatr. 1992;55:71–5.
CAS
PubMed
Google Scholar
Werner E, Dawson G. Validation of the phenomenon of autistic regression using home videotapes. Arch Gen Psychiatry. 2005;62:889–95.
Article
CAS
PubMed
Google Scholar
Baranek GT, Danko CD, Skinner ML, Bailey DB, Hatton DD, Roberts JE, et al. Video analysis of sensory-motor features in infants with fragile X syndrome at 9-12 months of age. J Autism Dev Disord. 2005;35:645–56.
Article
PubMed
Google Scholar
Kalantarian H, Jedoui K, Washington P, Tariq Q, Dunlap K, Schwartz J, et al. Labeling images with facial emotion and the potential for pediatric healthcare. Artif Intell Med. 2019;98:77–86.
Article
PubMed
PubMed Central
Google Scholar
Kalantarian H, Washington P, Schwartz J, Daniels J, Haber N, Wall D. A gamified mobile system for crowdsourcing video for autism research. In: 2018 IEEE international conference on healthcare informatics (ICHI). New York: IEEE; 2018. p. 350–2.
Chapter
Google Scholar
Alcañiz Raya M, Marín-Morales J, Minissi ME, Teruel Garcia G, Abad L, Chicchi Giglioli IA. Machine learning and virtual reality on body movements’ behaviors to classify children with autism spectrum disorder. J Clin Med. 2020;9:E1260.
Article
PubMed
Google Scholar
Mazurek MO, Wenstrup C. Television, video game and social media use among children with ASD and typically developing siblings. J Autism Dev Disord. 2013;43:1258–71.
Article
PubMed
Google Scholar
Saha A, Agarwal N. Modeling social support in autism community on social media. Netw Model Anal Health Inform Bioinforma. 2016;5:8.
Article
Google Scholar
Blumenthal D, Tavenner M. The “meaningful use” regulation for electronic health records. N Engl J Med. 2010;363:501–4.
Article
CAS
PubMed
Google Scholar
Lingren T, Chen P, Bochenek J, Doshi-Velez F, Manning-Courtney P, Bickel J, et al. Electronic health record based algorithm to identify patients with autism spectrum disorder. PLoS One. 2016;11:e0159621.
Article
PubMed
PubMed Central
CAS
Google Scholar
Brooks JD, Bronskill SE, Fu L, Saxena FE, Arneja J, Pinzaru VB, et al. Identifying children and youth with autism spectrum disorder in electronic medical records: examining health system utilization and comorbidities. Autism Res. 2021;14:400–10.
Article
PubMed
Google Scholar
Alexeeff SE, Yau V, Qian Y, Davignon M, Lynch F, Crawford P, et al. Medical conditions in the first years of life associated with future diagnosis of ASD in children. J Autism Dev Disord. 2017;47:2067–79.
Article
PubMed
PubMed Central
Google Scholar
Croen LA, Zerbo O, Qian Y, Massolo ML, Rich S, Sidney S, et al. The health status of adults on the autism spectrum. Autism. 2015;19:814–23.
Article
PubMed
Google Scholar
Movaghar A, Page D, Scholze D, Hong J, DaWalt LS, Kuusisto F, et al. Artificial intelligence-assisted phenotype discovery of fragile X syndrome in a population-based sample. Genet Med. 2021;23:1273–80.
Article
CAS
PubMed
PubMed Central
Google Scholar
Greener JG, Kandathil SM, Moffat L, Jones DT. A guide to machine learning for biologists. Nature reviews. Mol Cell Biol. 2022;23:40–55.
van Dijk D, Sharma R, Nainys J, Yim K, Kathail P, Carr AJ, et al. Recovering gene interactions from single-cell data using data diffusion. Cell. 2018;174:716–729.e27.
Article
PubMed
PubMed Central
CAS
Google Scholar
Huang M, Wang J, Torre E, Dueck H, Shaffer S, Bonasio R, et al. SAVER: gene expression recovery for single-cell RNA sequencing. Nat Methods. 2018;15:539–42.
Article
CAS
PubMed
PubMed Central
Google Scholar
Li WV, Li JJ. An accurate and robust imputation method scImpute for single-cell RNA-seq data. Nat Commun. 2018;9:997.
Article
PubMed
PubMed Central
CAS
Google Scholar
Tracy S, Yuan G-C, Dries R. RESCUE: imputing dropout events in single-cell RNA-sequencing data. BMC Bioinformatics. 2019;20:388.
Article
PubMed
PubMed Central
CAS
Google Scholar
Haghverdi L, Lun ATL, Morgan MD, Marioni JC. Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat Biotechnol. 2018;36:421–7.
Article
CAS
PubMed
PubMed Central
Google Scholar
Stuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E, Mauck WM, et al. Comprehensive integration of single-cell data. Cell. 2019;177:1888–1902.e21.
Article
CAS
PubMed
PubMed Central
Google Scholar
Klöppel S, Stonnington CM, Chu C, Draganski B, Scahill RI, Rohrer JD, et al. Automatic classification of MR scans in Alzheimer’s disease. Brain. 2008;131(Pt 3):681–9.
Article
PubMed
Google Scholar
Gerardin E, Chételat G, Chupin M, Cuingnet R, Desgranges B, Kim H-S, et al. Multidimensional classification of hippocampal shape features discriminates Alzheimer’s disease and mild cognitive impairment from normal aging. Neuroimage. 2009;47:1476–86.
Article
PubMed
Google Scholar
Kim J, Calhoun VD, Shim E, Lee J-H. Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: evidence from whole-brain resting-state functional connectivity patterns of schizophrenia. Neuroimage. 2016;124 Pt A:127–46.
Article
Google Scholar
Meda SA, Gill A, Stevens MC, Lorenzoni RP, Glahn DC, Calhoun VD, et al. Differences in resting-state fMRI functional network connectivity between schizophrenia and psychotic bipolar probands and their unaffected first-degree relatives. Biol Psychiatry. 2012;71:881.
Article
PubMed
PubMed Central
Google Scholar
Hazlett HC, Gu H, Munsell BC, Kim SH, Styner M, Wolff JJ, et al. Early brain development in infants at high risk for autism spectrum disorder. Nature. 2017;542:348–51.
Article
CAS
PubMed
PubMed Central
Google Scholar
Heinsfeld AS, Franco AR, Craddock RC, Buchweitz A, Meneguzzi F. Identification of autism spectrum disorder using deep learning and the ABIDE dataset. Neuroimage Clin. 2018;17:16–23.
Article
PubMed
Google Scholar
Li X, Dvornek NC, Zhuang J, Ventola P, Duncan JS. Brain biomarker interpretation in ASD using deep learning and fMRI. Med Image Comput Comput Assist Interv. 2018;11072:206–14.
PubMed
PubMed Central
Google Scholar
Chen H, Song Y, Li X. Use of deep learning to detect personalized spatial-frequency abnormalities in EEGs of children with ADHD. J Neural Eng. 2019;16:066046.
Article
PubMed
Google Scholar
Tenev A, Markovska-Simoska S, Kocarev L, Pop-Jordanov J, Müller A, Candrian G. Machine learning approach for classification of ADHD adults. Int J Psychophysiol. 2014;93:162–6.
Article
PubMed
Google Scholar
Eslami T, Mirjalili V, Fong A, Laird AR, Saeed F. ASD-DiagNet: a hybrid learning approach for detection of autism spectrum disorder using fMRI data. Front Neuroinform. 2019;13:70.
Article
PubMed
PubMed Central
Google Scholar
Han S, Huang W, Zhang Y, Zhao J, Chen H. Recognition of early-onset schizophrenia using deep-learning method. Appl Inform. 2017;4:16.
Article
Google Scholar
Stahl A, Schellewald C, Stavdahl Ø, Aamo OM, Adde L, Kirkerød H. An optical flow-based method to predict infantile cerebral palsy. IEEE Trans Neural Syst Rehabil Eng. 2012;20:605–14.
Article
PubMed
Google Scholar
Koivu A, Korpimäki T, Kivelä P, Pahikkala T, Sairanen M. Evaluation of machine learning algorithms for improved risk assessment for Down’s syndrome. Comput Biol Med. 2018;98:1–7.
Article
PubMed
Google Scholar
Naderi H, Soleimani BH, Matwin S. Multimodal deep learning for mental disorders prediction from audio speech samples. arXiv:190901067 [cs, eess, stat]; 2020.
Google Scholar
Cogill S, Wang L. Support vector machine model of developmental brain gene expression data for prioritization of autism risk gene candidates. Bioinformatics. 2016;32:3611–8.
CAS
PubMed
Google Scholar
Feng B, Hoskins W, Zhang Y, Meng Z, Samuels DC, Wang J, et al. Bi-stream CNN Down syndrome screening model based on genotyping array. BMC Med Genomics. 2018;11(Suppl 5):105.
Article
CAS
PubMed
PubMed Central
Google Scholar
Zhou J, Troyanskaya OG. Predicting effects of noncoding variants with deep learning-based sequence model. Nat Methods. 2015;12:931–4.
Article
CAS
PubMed
PubMed Central
Google Scholar
Zhou J, Park CY, Theesfeld CL, Wong AK, Yuan Y, Scheckel C, et al. Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk. Nat Genet. 2019;51:973–80.
Article
CAS
PubMed
PubMed Central
Google Scholar
Liu L, Feng X, Li H, Cheng Li S, Qian Q, Wang Y. Deep learning model reveals potential risk genes for ADHD, especially Ephrin receptor gene EPHA5. Brief Bioinform. 2021;22(6):bbab207.
Article
PubMed
PubMed Central
CAS
Google Scholar
Wang H, Avillach P. Diagnostic classification and prognostic prediction using common genetic variants in autism spectrum disorder: genotype-based deep learning. JMIR Med Inform. 2021;9:e24754.
Article
PubMed
PubMed Central
Google Scholar
Hasin Y, Seldin M, Lusis A. Multi-omics approaches to disease. Genome Biol. 2017;18:83.
Article
PubMed
PubMed Central
CAS
Google Scholar
Guan F, Ni T, Zhu W, Williams LK, Cui L-B, Li M, et al. Integrative omics of schizophrenia: from genetic determinants to clinical classification and risk prediction. Mol Psychiatry. 2022;27:113–26.
Chen J, Dong G, Song L, Zhao X, Cao J, Luo X, et al. Integration of multimodal data for deciphering brain disorders. Annu Rev Biomed Data Sci. 2021;4:43–56.
Article
PubMed
Google Scholar
Dong X, Liu C, Dozmorov M. Review of multi-omics data resources and integrative analysis for human brain disorders. Brief Funct Genomics. 2021;20:223–34.
Article
PubMed
Google Scholar
Ahmed Z. Practicing precision medicine with intelligently integrative clinical and multi-omics data analysis. Hum Genomics. 2020;14:35.
Article
PubMed
PubMed Central
CAS
Google Scholar
Pillai PS, Leong T-Y. Alzheimer’s disease neuroimaging initiative. fusing heterogeneous data for Alzheimer’s disease classification. Stud Health Technol Inform. 2015;216:731–5.
PubMed
Google Scholar
Zhang D, Wang Y, Zhou L, Yuan H, Shen D. Alzheimer’s Disease Neuroimaging Initiative. Multimodal classification of Alzheimer’s disease and mild cognitive impairment. Neuroimage. 2011;55:856–67.
Article
PubMed
Google Scholar
Colby J, Rudie J, Brown J, Douglas P, Cohen M, Shehzad Z. Insights into multimodal imaging classification of ADHD. Front Syst Neurosci. 2012;6:59.
Article
PubMed
PubMed Central
Google Scholar
Libero LE, DeRamus TP, Lahti AC, Deshpande G, Kana RK. Multimodal neuroimaging based classification of autism spectrum disorder using anatomical, neurochemical, and white matter correlates. Cortex. 2015;66:46–59.
Article
PubMed
PubMed Central
Google Scholar
Akhavan Aghdam M, Sharifi A, Pedram MM. Combination of rs-fMRI and sMRI data to discriminate autism spectrum disorders in young children using deep belief network. J Digit Imaging. 2018;31:895–903.
Article
PubMed
PubMed Central
Google Scholar
Luo Q, Chen Q, Wang W, Desrivières S, Quinlan EB, Jia T, et al. Association of a schizophrenia-risk nonsynonymous variant with putamen volume in adolescents: a voxelwise and genome-wide association study. JAMA Psychiatry. 2019;76:435–45.
Article
PubMed
PubMed Central
Google Scholar
Erk S, Mohnke S, Ripke S, Lett TA, Veer IM, Wackerhagen C, et al. Functional neuroimaging effects of recently discovered genetic risk loci for schizophrenia and polygenic risk profile in five RDoC subdomains. Transl Psychiatry. 2017;7:e997.
Article
CAS
PubMed
PubMed Central
Google Scholar
Neilson E, Shen X, Cox SR, Clarke T-K, Wigmore EM, Gibson J, et al. Impact of polygenic risk for schizophrenia on cortical structure in UK biobank. Biol Psychiatry. 2019;86:536–44.
Article
PubMed
Google Scholar
Warland A, Kendall KM, Rees E, Kirov G, Caseras X. Schizophrenia-associated genomic copy number variants and subcortical brain volumes in the UK Biobank. Mol Psychiatry. 2020;25:854–62.
Article
CAS
PubMed
Google Scholar
Berto S, Wang G-Z, Germi J, Lega BC, Konopka G. Human genomic signatures of brain oscillations during memory encoding. Cereb Cortex. 2018;28:1733–48.
Article
PubMed
Google Scholar
Zhao X, Chen J, Xiao P, Feng J, Nie Q, Zhao X-M. Identifying age-specific gene signatures of the human cerebral cortex with joint analysis of transcriptomes and functional connectomes. Brief Bioinform. 2021;22:bbaa388.
Article
PubMed
CAS
Google Scholar
van den Heuvel MP, Scholtens LH, de Lange SC, Pijnenburg R, Cahn W, van Haren NEM, et al. Evolutionary modifications in human brain connectivity associated with schizophrenia. Brain. 2019;142:3991–4002.
Article
PubMed
PubMed Central
Google Scholar
Li G, Han D, Wang C, Hu W, Calhoun VD, Wang Y-P. Application of deep canonically correlated sparse autoencoder for the classification of schizophrenia. Comput Methods Programs Biomed. 2020;183:105073.
Article
PubMed
Google Scholar
Wang D, Liu S, Warrell J, Won H, Shi X, Navarro FCP, et al. Comprehensive functional genomic resource and integrative model for the human brain. Science. 2018;362:eaat8464.
Article
CAS
PubMed
PubMed Central
Google Scholar
Argelaguet R, Velten B, Arnol D, Dietrich S, Zenz T, Marioni JC, et al. Multi-Omics Factor Analysis—a framework for unsupervised integration of multi-omics data sets. Mol Syst Biol. 2018;14:e8124.
Higdon R, Earl RK, Stanberry L, Hudac CM, Montague E, Stewart E, et al. The promise of multi-omics and clinical data integration to identify and target personalized healthcare approaches in autism spectrum disorders. OMICS. 2015;19:197–208.
Article
CAS
PubMed
PubMed Central
Google Scholar
van Bokhoven H. Genetic and epigenetic networks in intellectual disabilities. Annu Rev Genet. 2011;45:81–104.
Article
PubMed
CAS
Google Scholar
Kang HJ, Kawasawa YI, Cheng F, Zhu Y, Xu X, Li M, et al. Spatio-temporal transcriptome of the human brain. Nature. 2011;478:483–9.
Article
CAS
PubMed
PubMed Central
Google Scholar
McKenzie AT, Wang M, Hauberg ME, Fullard JF, Kozlenkov A, Keenan A, et al. Brain cell type specific gene expression and co-expression network architectures. Sci Rep. 2018;8:8868.
Article
PubMed
PubMed Central
CAS
Google Scholar
Gaiteri C, Ding Y, French B, Tseng GC, Sibille E. Beyond modules and hubs: the potential of gene coexpression networks for investigating molecular mechanisms of complex brain disorders. Genes Brain Behav. 2014;13:13–24.
Article
CAS
PubMed
Google Scholar
Parikshak NN, Gandal MJ, Geschwind DH. Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders. Nat Rev Genet. 2015;16:441–58.
Article
CAS
PubMed
PubMed Central
Google Scholar
Voineagu I, Wang X, Johnston P, Lowe JK, Tian Y, Horvath S, et al. Transcriptomic analysis of autistic brain reveals convergent molecular pathology. Nature. 2011;474:380–4.
Article
CAS
PubMed
PubMed Central
Google Scholar
Johnson MR, Shkura K, Langley SR, Delahaye-Duriez A, Srivastava P, Hill WD, et al. Systems genetics identifies a convergent gene network for cognition and neurodevelopmental disease. Nat Neurosci. 2016;19:223–32.
Article
CAS
PubMed
Google Scholar
Kimura R, Swarup V, Tomiwa K, Gandal MJ, Parikshak NN, Funabiki Y, et al. Integrative network analysis reveals biological pathways associated with Williams syndrome. J Child Psychol Psychiatry. 2019;60:585–98.
Article
PubMed
Google Scholar
Torkamani A, Dean B, Schork NJ, Thomas EA. Coexpression network analysis of neural tissue reveals perturbations in developmental processes in schizophrenia. Genome Res. 2010;20:403–12.
Article
CAS
PubMed
PubMed Central
Google Scholar
Gulsuner S, Walsh T, Watts AC, Lee MK, Thornton AM, Casadei S, et al. Spatial and temporal mapping of de novo mutations in schizophrenia to a fetal prefrontal cortical network. Cell. 2013;154:518–29.
Article
CAS
PubMed
PubMed Central
Google Scholar
Chen C, Cheng L, Grennan K, Pibiri F, Zhang C, Badner JA, et al. Two gene co-expression modules differentiate psychotics and controls. Mol Psychiatry. 2013;18:1308–14.
Article
CAS
PubMed
Google Scholar
Greene CS, Krishnan A, Wong AK, Ricciotti E, Zelaya RA, Himmelstein DS, et al. Understanding multicellular function and disease with human tissue-specific networks. Nat Genet. 2015;47:569–76.
Article
CAS
PubMed
PubMed Central
Google Scholar
Wang B, Mezlini AM, Demir F, Fiume M, Tu Z, Brudno M, et al. Similarity network fusion for aggregating data types on a genomic scale. Nat Methods. 2014;11:333–7.
Article
CAS
PubMed
Google Scholar
Jacobs GR, Voineskos AN, Hawco C, Stefanik L, Forde NJ, Dickie EW, et al. Integration of brain and behavior measures for identification of data-driven groups cutting across children with ASD, ADHD, or OCD. Neuropsychopharmacology. 2021;46:643–53.
Article
PubMed
Google Scholar
Park B, Hong S-J, Valk SL, Paquola C, Benkarim O, Bethlehem RAI, et al. Differences in subcortico-cortical interactions identified from connectome and microcircuit models in autism. Nat Commun. 2021;12:2225.
Park B, Bethlehem RAI, Paquola C, Larivière S, Cruces RR, de Wael RV, et al. An expanding manifold in transmodal regions characterizes adolescent reconfiguration of structural connectome organization. 2021.
Google Scholar
Liu R, Mancuso CA, Yannakopoulos A, Johnson KA, Krishnan A. Supervised learning is an accurate method for network-based gene classification. Bioinformatics. 2020;36:3457–65.
Article
CAS
PubMed
PubMed Central
Google Scholar
Krishnan A, Zhang R, Yao V, Theesfeld CL, Wong AK, Tadych A, et al. Genome-wide prediction and functional characterization of the genetic basis of autism spectrum disorder. Nat Neurosci. 2016;19:1454–62.
Article
CAS
PubMed
PubMed Central
Google Scholar
Himmelstein DS, Baranzini SE. Heterogeneous network edge prediction: a data integration approach to prioritize disease-associated genes. PLoS Comput Biol. 2015;11:e1004259.
Article
PubMed
PubMed Central
CAS
Google Scholar
Lee I, Blom UM, Wang PI, Shim JE, Marcotte EM. Prioritizing candidate disease genes by network-based boosting of genome-wide association data. Genome Res. 2011;21:1109–21.
Article
CAS
PubMed
PubMed Central
Google Scholar
Himmelstein DS, Lizee A, Hessler C, Brueggeman L, Chen SL, Hadley D, et al. Systematic integration of biomedical knowledge prioritizes drugs for repurposing. eLife. 2017;6:e26726.
Article
PubMed
PubMed Central
Google Scholar
Barabási A-L, Gulbahce N, Loscalzo J. Network medicine: a network-based approach to human disease. Nat Rev Genet. 2011;12:56–68.
Article
PubMed
PubMed Central
CAS
Google Scholar
Xu J, Zhang P, Huang Y, Zhou Y, Hou Y, Bekris LM, et al. Multimodal single-cell/nucleus RNA sequencing data analysis uncovers molecular networks between disease-associated microglia and astrocytes with implications for drug repurposing in Alzheimer’s disease. Genome Res. 2021;31:1900–12.
Article
PubMed
PubMed Central
Google Scholar
Fang J, Zhang P, Zhou Y, Chiang C-W, Tan J, Hou Y, et al. Endophenotype-based in silico network medicine discovery combined with insurance record data mining identifies sildenafil as a candidate drug for Alzheimer’s disease. Nat Aging. 2021;1:1175–88.
Article
Google Scholar
Abraham A, Pedregosa F, Eickenberg M, Gervais P, Mueller A, Kossaifi J, et al. Machine learning for neuroimaging with scikit-learn. Front Neuroinform. 2014;8:14.
Article
PubMed
PubMed Central
Google Scholar
Hahn S, Yuan DK, Thompson WK, Owens M, Allgaier N, Garavan H. Brain Predictability toolbox: a Python library for neuroimaging-based machine learning. Bioinformatics. 2021;37:1637–8.
Article
CAS
PubMed
Google Scholar
Liu M, Liu T, Wang Y, Feng Y, Xie Y, Yan T, et al. BrainSort: a machine learning toolkit for brain connectome data analysis and visualization. J Sign Process Syst. 2020. https://doi.org/10.1007/s11265-020-01583-6.
Zhou Z, Kuo H-C, Peng H, Long F. DeepNeuron: an open deep learning toolbox for neuron tracing. Brain Inform. 2018;5:3.
Article
PubMed
PubMed Central
Google Scholar
Arac A, Zhao P, Dobkin BH, Carmichael ST, Golshani P. DeepBehavior: a deep learning toolbox for automated analysis of animal and human behavior imaging data. Front Syst Neurosci. 2019;13:20.
Article
PubMed
PubMed Central
Google Scholar
Schirrmeister RT, Springenberg JT, Fiederer LDJ, Glasstetter M, Eggensperger K, Tangermann M, et al. Deep learning with convolutional neural networks for EEG decoding and visualization. Hum Brain Mapp. 2017;38:5391–420.
Article
PubMed
PubMed Central
Google Scholar
Lundervold AS, Lundervold A. An overview of deep learning in medical imaging focusing on MRI. Z Med Phys. 2019;29:102–27.
Article
PubMed
Google Scholar
Bilgen I, Guvercin G, Rekik I. Machine learning methods for brain network classification: application to autism diagnosis using cortical morphological networks. J Neurosci Methods. 2020;343:108799.
Article
PubMed
Google Scholar
Piñero J, Ramírez-Anguita JM, Saüch-Pitarch J, Ronzano F, Centeno E, Sanz F, et al. The DisGeNET knowledge platform for disease genomics: 2019 update. Nucleic Acids Res. 2020;48:D845–55.
PubMed
Google Scholar
Reynolds RJ, Day SM. The growing role of machine learning and artificial intelligence in developmental medicine. Dev Med Child Neurol. 2018;60:858–9.
Article
PubMed
Google Scholar
Cravedi E, Deniau E, Giannitelli M, Pellerin H, Czernecki V, Priou T, et al. Disentangling Tourette syndrome heterogeneity through hierarchical ascendant clustering. Dev Med Child Neurol. 2018;60:942–50.
Article
PubMed
Google Scholar
Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 2019;17:195.
Article
PubMed
PubMed Central
CAS
Google Scholar
Ghassemi M, Naumann T, Schulam P, Beam AL, Chen IY, Ranganath R. A review of challenges and opportunities in machine learning for health. AMIA Jt Summits Transl Sci Proc. 2020;2020:191–200.
PubMed
PubMed Central
Google Scholar
Nguyen ND, Jin T, Wang D. Varmole: a biologically drop-connect deep neural network model for prioritizing disease risk variants and genes. Bioinformatics. 2020;37:1772–5.
Article
PubMed Central
CAS
Google Scholar
Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making. 2006;26:565–74.
Article
PubMed
PubMed Central
Google Scholar
Khullar S, Wang D. Predicting gene regulatory networks from multi-omics to link genetic risk variants and neuroimmunology to Alzheimer’s disease phenotypes; 2021.
Book
Google Scholar
Zhang Q, Ma J, Lou J, Xiong L, Jiang X. Towards training robust private aggregation of teacher ensembles under noisy labels. In: 2020 IEEE international conference on big data (big data); 2020. p. 1103–10.
Chapter
Google Scholar
Price WN, Gerke S, Cohen IG. Potential liability for physicians using artificial intelligence. JAMA. 2019;322:1765–6.
Article
PubMed
Google Scholar
Aboy M, Liddell K, Crespo C, Cohen IG, Liddicoat J, Gerke S, et al. How does emerging patent case law in the US and Europe affect precision medicine? Nat Biotechnol. 2019;37:1118–25.
Article
CAS
PubMed
Google Scholar
Wachter S, Mittelstadt B, Floridi L. Why a right to explanation of automated decision-making does not exist in the general data protection regulation. Int Data Priv Law. 2017;7:76–99.
Article
Google Scholar
Price WN, Kaminski ME, Minssen T, Spector-Bagdady K. Shadow health records meet new data privacy laws. Science. 2019;363:448–50.
Article
CAS
PubMed
PubMed Central
Google Scholar
Gerke S, Yeung S, Cohen IG. Ethical and legal aspects of ambient intelligence in hospitals. JAMA. 2020;323:601–2.
Article
PubMed
Google Scholar
Haibe-Kains B, Adam GA, Hosny A, Khodakarami F, Waldron L, Wang B, et al. Transparency and reproducibility in artificial intelligence. Nature. 2020;586:E14–6.
Article
CAS
PubMed
PubMed Central
Google Scholar
Stodden V, McNutt M, Bailey DH, Deelman E, Gil Y, Hanson B, et al. Enhancing reproducibility for computational methods. Science. 2016;354:1240–1.
Article
CAS
PubMed
Google Scholar
Nosek BA, Alter G, Banks GC, Borsboom D, Bowman SD, Breckler SJ, et al. Promoting an open research culture. Science. 2015;348:1422–5.
Article
CAS
PubMed
PubMed Central
Google Scholar
McNutt M, Lehnert K, Hanson B, Nosek BA, Ellison AM, King JL. Liberating field science samples and data. Science. 2016;351:1024–6.
Article
CAS
PubMed
Google Scholar