High-Resolution and Multidimensional Phenotypes Can Complement Genomics Data to Diagnose Diseases in the Neonatal Population

Alanazi IO, Alamery SF, Ebrahimie E, Mohammadi-Dehcheshmeh M (2022) Splice-disrupt genomic variants in prostate cancer. Mol Biol Rep 49(6):4237–4246. https://doi.org/10.1007/s11033-022-07257-9

Alasadi SA, Bhaya WS (2017) Review of data preprocessing techniques in data mining. J Eng Appl Sci 12(16):4102–4107. https://doi.org/10.36478/jeasci.2017.4102.4107

Almli CR, Rivkin MJ, McKinstry RC, Brain Development Cooperative G (2007) The NIH MRI study of normal brain development (Objective-2): newborns, infants, toddlers, and preschoolers. Neuroimage 35(1):308–325. https://doi.org/10.1016/j.neuroimage.2006.08.058

Azpurua H, Alvarado A, Mayobre F, Salom T, Copel JA, Guevara-Zuloaga F (2008) Metabolic assessment of the brain using proton magnetic resonance spectroscopy in a growth-restricted human fetus: case report. Am J Perinatol 25(5):305–309. https://doi.org/10.1055/s-2008-1076603

Baranger J, Demene C, Frerot A, Faure F, Delanoe C, Serroune H, Houdouin A, Mairesse J, Biran V, Baud O, Tanter M (2021a) Bedside functional monitoring of the dynamic brain connectivity in human neonates. Nat Commun 12(1):1080. https://doi.org/10.1038/s41467-021-21387-x

Baranger J, Villemain O, Wagner M, Vargas-Gutierrez M, Seed M, Baud O, Ertl-Wagner B, Aguet J (2021b) Brain perfusion imaging in neonates. Neuroimage Clin 31:102756. https://doi.org/10.1016/j.nicl.2021.102756

Baruteau AE, Kyndt F, Behr ER et al (2018) SCN5A mutations in 442 neonates and children: genotype-phenotype correlation and identification of higher-risk subgroups. Eur Heart J 39(31):2879–2887. https://doi.org/10.1093/eurheartj/ehy412

Bassingthwaighte JB (2000) Strategies for the physiome project. Ann Biomed Eng 28(8):1043–1058. https://doi.org/10.1114/1.1313771

Bastarache L, Hughey JJ, Hebbring S et al (2018) Phenotype risk scores identify patients with unrecognized Mendelian disease patterns. Science 359(6381):1233–1239. https://doi.org/10.1126/science.aal4043

Bastarache L, Denny JC, Roden DM (2022) Phenome-wide association studies. JAMA 327(1):75–76. https://doi.org/10.1001/jama.2021.20356

Basu SK, Pradhan S, du Plessis AJ, Ben-Ari Y, Limperopoulos C (2021) GABA and glutamate in the preterm neonatal brain: in-vivo measurement by magnetic resonance spectroscopy. Neuroimage 238:118215. https://doi.org/10.1016/j.neuroimage.2021.118215

Belgrave DCM, Granell R, Turner SW et al (2018) Lung function trajectories from pre-school age to adulthood and their associations with early life factors: a retrospective analysis of three population-based birth cohort studies. Lancet Respir Med 6(7):526–534. https://doi.org/10.1016/S2213-2600(18)30099-7

Benitz WE, Achten NB (2021) Technical assessment of the neonatal early-onset sepsis risk calculator. Lancet Infect Dis 21(5):e134–e140. https://doi.org/10.1016/S1473-3099(20)30490-4

Bilder RM, Sabb FW, Cannon TD et al (2009) Phenomics: the systematic study of phenotypes on a genome-wide scale. Neuroscience 164(1):30–42. https://doi.org/10.1016/j.neuroscience.2009.01.027

Bodenreider O, Cornet R, Vreeman DJ (2018) Recent developments in clinical terminologies—SNOMED CT, LOINC, and RxNorm. Yearb Med Inform 27(1):129–139. https://doi.org/10.1055/s-0038-1667077

Boehm KM, Khosravi P, Vanguri R, Gao J, Shah SP (2021) Harnessing multimodal data integration to advance precision oncology. Nat Rev Cancer 22(2):114–126. https://doi.org/10.1038/s41568-021-00408-3

Bosch A, de Steenhuijsen Piters WAA, van Houten MA et al (2017) Maturation of the infant respiratory microbiota, environmental drivers, and health consequences. A prospective cohort study. Am J Respir Crit Care Med 196(12):1582–1590. https://doi.org/10.1164/rccm.201703-0554OC

Cajot JF, Sordat B, Kruithof EK, Bachmann F (1986) Human primary colon carcinomas xenografted into nude mice. I. Characterization of plasminogen activators expressed by primary tumors and their xenografts. J Natl Cancer Inst 77(3):703–712. https://doi.org/10.1093/jnci/77.3.703

Cakmakci H, Pekcevik Y, Yis U, Unalp A, Kurul S (2010) Diagnostic value of proton MR spectroscopy and diffusion-weighted MR imaging in childhood inherited neurometabolic brain diseases and review of the literature. Eur J Radiol 74(3):e161–171. https://doi.org/10.1016/j.ejrad.2009.05.048

Campbell JP, Kim SJ, Brown JM et al (2021) Evaluation of a deep learning-derived quantitative retinopathy of prematurity severity scale. Ophthalmology 128(7):1070–1076. https://doi.org/10.1016/j.ophtha.2020.10.025

Chen H, Xue M, Mei Z, Bambang Oetomo S, Chen W (2016) A review of wearable sensor systems for monitoring body movements of neonates. Sensors (Basel) 16(12):2134. https://doi.org/10.3390/s16122134

Chen J, Dong G, Song L, Zhao X, Cao J, Luo X, Feng J, Zhao XM (2021) Integration of multimodal data for deciphering brain disorders. Annu Rev Biomed Data Sci 4:43–56. https://doi.org/10.1146/annurev-biodatasci-092820-020354

Chiera M, Cerritelli F, Casini A, Barsotti N, Boschiero D, Cavigioli F, Corti CG, Manzotti A (2020) Heart rate variability in the perinatal period: a critical and conceptual review. Front Neurosci 14:561186. https://doi.org/10.3389/fnins.2020.561186

Claessens NHP, Jansen NJG, Breur J et al (2019) Postoperative cerebral oxygenation was not associated with new brain injury in infants with congenital heart disease. J Thorac Cardiovasc Surg 158(3):867–877. https://doi.org/10.1016/j.jtcvs.2019.02.106

Clark MM, Stark Z, Farnaes L, Tan TY, White SM, Dimmock D, Kingsmore SF (2018) Meta-analysis of the diagnostic and clinical utility of genome and exome sequencing and chromosomal microarray in children with suspected genetic diseases. NPJ Genom Med 3:16. https://doi.org/10.1038/s41525-018-0053-8

Claussnitzer M, Cho JH, Collins R et al (2020) A brief history of human disease genetics. Nature 577(7789):179–189. https://doi.org/10.1038/s41586-019-1879-7

Dai D, Chen H, Dong X, Chen J, Mei M, Lu Y, Yang L, Wu B, Cao Y, Wang J, Zhou W, Qian L (2021) Bronchopulmonary Dysplasia Predicted by Developing a Machine Learning Model of Genetic and Clinical Information. Front Genet 12:689071. https://doi.org/10.3389/fgene.2021.689071

Deffieux T, Demene C, Tanter M (2021) Functional ultrasound imaging: a new imaging modality for neuroscience. Neuroscience 474:110–121. https://doi.org/10.1016/j.neuroscience.2021.03.005

Demene C, Pernot M, Biran V, Alison M, Fink M, Baud O, Tanter M (2014) Ultrafast Doppler reveals the mapping of cerebral vascular resistivity in neonates. J Cereb Blood Flow Metab 34(6):1009–1017. https://doi.org/10.1038/jcbfm.2014.49

Demene C, Baranger J, Bernal M, Delanoe C et al (2017) Functional ultrasound imaging of brain activity in human newborns. Sci Transl Med 9(411):eaah6756. https://doi.org/10.1126/scitranslmed.aah6756

Dessi A, Pintus R, Marras S, Cesare Marincola F, De Magistris A, Fanos V (2016) Metabolomics in necrotizing enterocolitis: the state of the art. Expert Rev Mol Diagn 16(10):1053–1058. https://doi.org/10.1080/14737159.2016.1211933

Diller GP, Arvanitaki A, Opotowsky AR et al (2021) Lifespan perspective on congenital heart disease research: JACC state-of-the-art review. J Am Coll Cardiol 77(17):2219–2235. https://doi.org/10.1016/j.jacc.2021.03.012

Dong X, Kong Y, Xu Y, Zhou Y, Wang X, Xiao T, Chen B, Lu Y, Cheng G, Zhou W (2021) Development and validation of auto-neo-electroencephalography (EEG) to estimate brain age and predict report conclusion for electroencephalography monitoring data in neonatal intensive care units. Ann Transl Med 9(16):1290. https://doi.org/10.2103/atm-21-1564

Duhamel A, Nuttens MC, Devos P, Picavet M, Beuscart R (2003) A preprocessing method for improving data mining techniques. Application to a large medical diabetes database. Stud Health Technol Inform 95:269–274

Evans AC, Brain Development Cooperative G (2006) The NIH MRI study of normal brain development. Neuroimage 30(1):184–202. https://doi.org/10.1016/j.neuroimage.2005.09.068

Favretto D, Cosmi E, Ragazzi E et al (2012) Cord blood metabolomic profiling in intrauterine growth restriction. Anal Bioanal Chem 402(3):1109–1121. https://doi.org/10.1007/s00216-011-5540-z

Freimer N, Sabatti C (2003) The human phenome project. Nat Genet 34(1):15–21. https://doi.org/10.1038/ng0503-15

Ganesan S, Galer PD, Helbig KL et al (2020) A longitudinal footprint of genetic epilepsies using automated electronic medical record interpretation. Genet Med 22(12):2060–2070. https://doi.org/10.1038/s41436-020-0923-1

Garvey AA, Dempsey EM (2018) Applications of near infrared spectroscopy in the neonate. Curr Opin Pediatr 30(2):209–215. https://doi.org/10.1097/MOP.0000000000000599

Ge MM, Gao YY, Wu BB, Yan K, Qin Q, Wang H, Zhou W, Yang L (2019) Relationship between phenotype and genotype of 102 Chinese newborns with Prader-Willi syndrome. Mol Biol Rep 46(5):4717–4724. https://doi.org/10.1007/s11033-019-04916-2

Geenjaar E, Lewis N, Fu Z, Venkatdas R, Plis S, Calhoun V (2021) Fusing multimodal neuroimaging data with a variational autoencoder. Annu Int Conf IEEE Eng Med Biol Soc 2021:3630–3633. https://doi.org/10.1109/EMBC46164.2021.9630806

Goel N, Cannell S, Davies G et al (2021) Implementation of an adapted sepsis risk calculator algorithm to reduce antibiotic usage in the management of early onset neonatal sepsis: a multicentre initiative in Wales, UK. Arch Dis Child Fetal Neonatal Ed 107(3):303–310. https://doi.org/10.1136/archdischild-2020-321489

Green BN, Johnson CD, Adams A (2006) Writing narrative literature reviews for peer-reviewed journals: secrets of the trade. J Chiropr Med 5(3):101–117. https://doi.org/10.1016/S0899-3467(07)60142-6

Montavon G, Samek W, Müller K-R (2018) Methods for interpreting and understanding deep neural networks. Digital Signal Process 73:1–15. https://doi.org/10.1016/j.dsp.2017.10.011

Gurovich Y, Hanani Y, Bar O, Nadav G, Fleischer N, Gelbman D, Basel-Salmon L, Krawitz PM, Kamphausen SB, Zenker M, Bird LM, Gripp KW (2019) Identifying facial phenotypes of genetic disorders using deep learning. Nat Med 25(1):60–64. https://doi.org/10.1038/s41591-018-0279-0

Helbig I, Lopez-Hernandez T, Shor O et al (2019) A recurrent missense variant in AP2M1 impairs Clathrin-mediated endocytosis and causes developmental and epileptic encephalopathy. Am J Hum Genet 104(6):1060–1072. https://doi.org/10.1016/j.ajhg.2019.04.001

Holshouser BA, Ashwal S, Shu S, Hinshaw DB Jr (2000) Proton MR spectroscopy in children with acute brain injury: comparison of short and long echo time acquisitions. J Magn Reson Imaging 11(1):9–19. https://doi.org/10.1002/(sici)1522-2586(200001)11:1%3c9::aid-jmri2%3e3.0.co;2-6

Hoodbhoy Z, Masroor Jeelani S, Aziz A, Habib MI, Iqbal B, Akmal W, Siddiqui K, Hasan B, Leeflang M, Das JK (2021) Machine learning for child and adolescent health: a systematic review. Pediatrics 147(1):e2020011833. https://doi.org/10.1542/peds.2020-011833

Horgan RP, Broadhurst DI, Walsh SK et al (2011) Metabolic profiling uncovers a phenotypic signature of small for gestational age in early pregnancy. J Proteome Res 10(8):3660–3673. https://doi.org/10.1021/pr2002897

Howell BR, Styner MA, Gao W et al (2019) The UNC/UMN baby connectome project (BCP): an overview of the study design and protocol development. Neuroimage 185:891–905. https://doi.org/10.1016/j.neuroimage.2018.03.049

Hsu JF, Chang YF, Cheng HJ et al (2021) Machine learning approaches to predict in-hospital mortality among neonates with clinically suspected sepsis in the neonatal intensive care unit. J Pers Med 11(8):695. https://doi.org/10.3390/jpm11080695

Huang YJ, LiPuma JJ (2016) The microbiome in cystic fibrosis. Clin Chest Med 37(1):59–67. https://doi.org/10.1016/j.ccm.2015.10.003

Hully M, Lo Barco T, Kaminska A et al (2021) Deep phenotyping unstructured data mining in an extensive pediatric database to unravel a common KCNA2 variant in neurodevelopmental syndromes. Genet Med 23(5):968–971. https://doi.org/10.1038/s41436-020-01039-z

Hyttel-Sorensen S, Pellicer A, Alderliesten T et al (2015) Cerebral near infrared spectroscopy oximetry in extremely preterm infants: phase II randomised clinical trial. BMJ 350:g7635. https://doi.org/10.1136/bmj.g7635

Investigators GPP, Smedley D, Smith KR et al (2021) 100,000 genomes pilot on rare-disease diagnosis in health care—preliminary report. N Engl J Med 385(20):1868–1880. https://doi.org/10.1056/NEJMoa2035790

Irles C, Gonzalez-Perez G, Carrera Muinos S et al (2018) Estimation of neonatal intestinal perforation associated with necrotizing enterocolitis by machine learning reveals new key factors. Int J Environ Res Public Health 15(11):2509. https://doi.org/10.3390/ijerph15112509

Jalali A, Buckley EM, Lynch JM, Schwab PJ, Licht DJ, Nataraj C (2014) Prediction of periventricular leukomalacia occurrence in neonates after heart surgery. IEEE J Biomed Health Inform 18(4):1453–1460. https://doi.org/10.1109/JBHI.2013.2285011

Kingsmore SF, Cakici JA, Clark MM et al (2019) A randomized, controlled trial of the analytic and diagnostic performance of singleton and trio, rapid genome and exome sequencing in Ill infants. Am J Hum Genet 105(4):719–733. https://doi.org/10.1016/j.ajhg.2019.08.009

Kohler S, Carmody L, Vasilevsky N et al (2019) Expansion of the human phenotype ontology (HPO) knowledge base and resources. Nucleic Acids Res 47(D1):D1018–D1027. https://doi.org/10.1093/nar/gky1105

Kohler S, Gargano M, Matentzoglu N et al (2021) The human phenotype ontology in 2021. Nucleic Acids Res 49(D1):D1207–D1217. https://doi.org/10.1093/nar/gkaa1043

Kopsidas I, Molocha NM, Kourkouni E et al (2021) Potential benefit from the implementation of the Kaiser Permanente neonatal early-onset sepsis calculator on clinical management of neonates with presumed sepsis. Eur J Pediatr 181(3):1001–1008. https://doi.org/10.1007/s00431-021-04282-x

Kwak SS, Yoo S, Avila R, Chung HU et al (2021) Skin-integrated devices with soft, holey architectures for wireless physiological monitoring, with applications in the neonatal intensive care unit. Adv Mater 33(44):e2103974. https://doi.org/10.1002/adma.202103974

Lander ES, Linton LM, Birren B et al (2001) Initial sequencing and analysis of the human genome. Nature 409(6822):860–921. https://doi.org/10.1038/35057062

Leach ST, Lui K, Naing Z, Dowd SE, Mitchell HM, Day AS (2015) Multiple opportunistic pathogens, but not pre-existing inflammation, may be associated with necrotizing enterocolitis. Dig Dis Sci 60(12):3728–3734. https://doi.org/10.1007/s10620-015-3830-6

Lei J, Sun T, Jiang Y, Wu P, Fu J, Zhang T, McGrath E (2021) Risk identification of bronchopulmonary dysplasia in premature infants based on machine learning. Front Pediatr 9:719352. https://doi.org/10.3389/fped.2021.719352

Lek M, Karczewski KJ, Minikel EV et al (2016) Analysis of protein-coding genetic variation in 60,706 humans. Nature 536(7616):285–291. https://doi.org/10.1038/nature19057

Li X, Dunn J, Salins D et al (2017) Digital health: tracking physiomes and activity using wearable biosensors reveals useful health-related information. PLoS Biol 15(1):e2001402. https://doi.org/10.1371/journal.pbio.2001402

Li G, Li Y, Han L, Wang D, Zhang Q, Xiao X, Qi L, Willi SM, Li M, Mi J, Gao S (2020) Interaction between early environment and genetic predisposition instigates the metabolically obese, normal weight phenotype in children: findings from the BCAMS study. Eur J Endocrinol 182(4):393–403. https://doi.org/10.1530/EJE-19-0755

Lin YC, Keenan K, Gong J et al (2021) Cystic fibrosis-related diabetes onset can be predicted using biomarkers measured at birth. Genet Med 23(5):927–933. https://doi.org/10.1038/s41436-020-01073-x

Lure AC, Du X, Black EW, Irons R, Lemas DJ, Taylor JA, Lavilla O, de la Cruz D, Neu J (2021) Using machine learning analysis to assist in differentiating between necrotizing enterocolitis and spontaneous intestinal perforation: a novel predictive analytic tool. J Pediatr Surg 56(10):1703–1710. https://doi.org/10.1016/j.jpedsurg.2020.11.008

Marinov M, Mosa AS, Yoo I, Boren SA (2011) Data-mining technologies for diabetes: a systematic review. J Diabetes Sci Technol 5(6):1549–1556. https://doi.org/10.1177/193229681100500631

McConnell MV, Shcherbina A, Pavlovic A et al (2017) Feasibility of obtaining measures of lifestyle from a smartphone app: the myheart counts cardiovascular health study. JAMA Cardiol 2(1):67–76. https://doi.org/10.1001/jamacardio.2016.4395

Miller SP, McQuillen PS, Hamrick S et al (2007) Abnormal brain development in newborns with congenital heart disease. N Engl J Med 357(19):1928–1938. https://doi.org/10.1056/NEJMoa067393

Montaldo P, Kaforou M, Pollara G et al (2019) Whole blood gene expression reveals specific transcriptome changes in neonatal encephalopathy. Neonatology 115(1):68–76. https://doi.org/10.1159/000492420

Mussap M, Antonucci R, Noto A, Fanos V (2013) The role of metabolomics in neonatal and pediatric laboratory medicine. Clin Chim Acta 426:127–138. https://doi.org/10.1016/j.cca.2013.08.020

Namjou B, Marsolo K, Caroll RJ et al (2014) Phenome-wide association study (PheWAS) in EMR-linked pediatric cohorts, genetically links PLCL1 to speech language development and IL5-IL13 to Eosinophilic Esophagitis. Front Genet 5:401. https://doi.org/10.3389/fgene.2014.00401

Nemati S, Holder A, Razmi F, Stanley MD, Clifford GD, Buchman TG (2018) An interpretable machine learning model for accurate prediction of sepsis in the ICU. Crit Care Med 46(4):547–553. https://doi.org/10.1097/CCM.0000000000002936

Nunes ML, Yozawitz EG, Zuberi S et al (2019) Neonatal seizures: is there a relationship between ictal electroclinical features and etiology? A critical appraisal based on a systematic literature review. Epilepsia Open 4(1):10–29. https://doi.org/10.1002/epi4.12298

Oz G, Alger JR, Barker PB et al (2014) Clinical proton MR spectroscopy in central nervous system disorders. Radiology 270(3):658–679. https://doi.org/10.1148/radiol.13130531

Pengelly RJ, Alom T, Zhang Z, Hunt D, Ennis S, Collins A (2017) Evaluating phenotype-driven approaches for genetic diagnoses from exomes in a clinical setting. Sci Rep 7(1):13509. https://doi.org/10.1038/s41598-017-13841-y

Perri A, Tiberi E, Giordano L, Sbordone A, Patti ML, Iannotta R, Pianini T, Cota F, Maggio L, Vento G (2021) Strict glycaemic control in very low birthweight infants using a continuous glucose monitoring system: a randomised controlled trial. Arch Dis Child Fetal Neonatal Ed 107(1):26–31. https://doi.org/10.1136/archdischild-2020-320540

Puopolo KM, Draper D, Wi S, Newman TB, Zupancic J, Lieberman E, Smith M, Escobar GJ (2011) Estimating the probability of neonatal early-onset infection on the basis of maternal risk factors. Pediatrics 128(5):e1155–1163. https://doi.org/10.1542/peds.2010-3464

Qin X, Yin D, Dong X, Chen D, Zhang S (2022) Survival prediction model for right-censored data based on improved composite quantile regression neural network. Math Biosci Eng 19(8):7521–7542. https://doi.org/10.3934/mbe.2022354

Rood JE, Regev A (2021) The legacy of the human genome project. Science 373(6562):1442–1443. https://doi.org/10.1126/science.abl5403

Saberi-Karimian M, Khorasanchi Z, Ghazizadeh H, Tayefi M, Saffar S, Ferns GA, Ghayour-Mobarhan M (2021) Potential value and impact of data mining and machine learning in clinical diagnostics. Crit Rev Clin Lab Sci 58(4):275–296. https://doi.org/10.1080/10408363.2020.1857681

Sanchez-Pinto LN, Stroup EK, Pendergrast T, Pinto N, Luo Y (2020) Derivation and validation of novel phenotypes of multiple organ dysfunction syndrome in critically Ill children. JAMA Netw Open 3(8):e209271. https://doi.org/10.1001/jamanetworkopen.2020.9271

Schilling CH, Edwards JS, Palsson BO (1999) Toward metabolic phenomics: analysis of genomic data using flux balances. Biotechnol Prog 15(3):288–295. https://doi.org/10.1021/bp9900357

Schork NJ (1997) Genetics of complex disease: approaches, problems, and solutions. Am J Respir Crit Care Med 156(4 Pt 2):S103–109. https://doi.org/10.1164/ajrccm.156.4.12-tac-5

Schussler-Fiorenza Rose SM, Contrepois K, Moneghetti KJ et al (2019) A longitudinal big data approach for precision health. Nat Med 25(5):792–804. https://doi.org/10.1038/s41591-019-0414-6

Scriver CR (2004) After the genome–the phenome? J Inherit Metab Dis 27(3):305–317. https://doi.org/10.1023/B:BOLI.0000031100.26546.6e

Shen X, Howard DM, Adams MJ et al (2020) A phenome-wide association and Mendelian randomisation study of polygenic risk for depression in UK Biobank. Nat Commun 11(1):2301. https://doi.org/10.1038/s41467-020-16022-0

Shetty AN, Lucke AM, Liu P, Sanz Cortes M, Hagan JL, Chu ZD, Hunter JV, Lu H, Lee W, Kaiser JR (2019) Cerebral oxygen metabolism during and after therapeutic hypothermia in neonatal hypoxic-ischemic encephalopathy: a feasibility study using magnetic resonance imaging. Pediatr Radiol 49(2):224–233. https://doi.org/10.1007/s00247-018-4283-9

Sima M, Michalickova D, Slanar O (2021) What is the best predictor of phenobarbital pharmacokinetics to use for initial dosing in neonates? Pharmaceutics 13(3):301. https://doi.org/10.3390/pharmaceutics13030301

Song W, Jung SY, Baek H, Choi CW, Jung YH, Yoo S (2020) A predictive model based on machine learning for the early detection of late-onset neonatal sepsis: development and observational study. JMIR Med Inform 8(7):e15965. https://doi.org/10.2196/15965

Song Y, Lally PJ, Yanez Lopez M et al (2021) Edited magnetic resonance spectroscopy in the neonatal brain. Neuroradiology 64(2):217–232. https://doi.org/10.1007/s00234-021-02821-9

Spagnoli C, Fusco C, Percesepe A, Leuzzi V, Pisani F (2021) Genetic neonatal-onset epilepsies and developmental/epileptic encephalopathies with movement disorders: a systematic review. Int J Mol Sci 22(8):4202. https://doi.org/10.3390/ijms22084202

Staartjes VE, Kernbach JM (2022) Foundations of machine learning-based clinical prediction modeling: part III-model evaluation and other points of significance. Acta Neurochir Suppl 134:23–31. https://doi.org/10.1007/978-3-030-85292-4_4

Story L, Damodaram MS, Allsop JM, McGuinness A, Wylezinska M, Kumar S, Rutherford MA (2011) Proton magnetic resonance spectroscopy in the fetus. Eur J Obstet Gynecol Reprod Biol 158(1):3–8. https://doi.org/10.1016/j.ejogrb.2010.03.003

Svefors P, Sysoev O, Ekstrom EC, Persson LA, Arifeen SE, Naved RT, Rahman A, Khan AI, Selling K (2019) Relative importance of prenatal and postnatal determinants of stunting: data mining approaches to the MINIMat cohort, Bangladesh. BMJ Open 9(8):e025154. https://doi.org/10.1136/bmjopen-2018-025154

Tsuji M, Saul JP, du Plessis A, Eichenwald E, Sobh J, Crocker R, Volpe JJ (2000) Cerebral intravascular oxygenation correlates with mean arterial pressure in critically ill premature infants. Pediatrics 106(4):625–632. https://doi.org/10.1542/peds.106.4.625

Vandenbosch M, van’t Ent D, Boomsma DI, Anokhin AP, Smit DJA (2019) EEG-based age-prediction models as stable and heritable indicators of brain maturational level in children and adolescents. Hum Brain Mapp 40(6):1919–1926. https://doi.org/10.1002/hbm.24501

Venter JC, Adams MD, Myers EW et al (2001) The sequence of the human genome. Science 291(5507):1304–1351. https://doi.org/10.1126/science.1058040

Verder H, Heiring C, Ramanathan R et al (2021) Bronchopulmonary dysplasia predicted at birth by artificial intelligence. Acta Paediatr 110(2):503–509. https://doi.org/10.1111/apa.15438

Wagner BD, Sontag MK, Harris JK, Miller JI, Morrow L, Robertson CE, Stephens M, Poindexter BB, Abman SH, Mourani PM (2017) Airway microbial community turnover differs by BPD severity in ventilated preterm infants. PLoS ONE 12(1):e0170120. https://doi.org/10.1371/journal.pone.0170120

Walker L, Chang LC, Nayak A et al (2016) The diffusion tensor imaging (DTI) component of the NIH MRI study of normal brain development (PedsDTI). Neuroimage 124(Pt B):1125–1130. https://doi.org/10.1016/j.neuroimage.2015.05.083

Walsh BH, Broadhurst DI, Mandal R, Wishart DS, Boylan GB, Kenny LC, Murray DM (2012) The metabolomic profile of umbilical cord blood in neonatal hypoxic ischaemic encephalopathy. PLoS ONE 7(12):e50520. https://doi.org/10.1371/journal.pone.0050520

Wang F, Li W, Wang G, Yu M, Zhong J, Xu C, Li D, Zhou Y (2019) Gas chromatography-mass spectrometry based serum metabolic analysis for premature infants and the relationship with necrotizing enterocolitis: a cross-sectional study. Ital J Pediatr 45(1):54. https://doi.org/10.1186/s13052-019-0646-6

Wang H, Qian Y, Lu Y, Qin Q, Lu G, Cheng G, Zhang P, Yang L, Wu B, Zhou W (2020) Clinical utility of 24-h rapid trio-exome sequencing for critically ill infants. NPJ Genom Med 5:20. https://doi.org/10.1038/s41525-020-0129-0

Weiss RJ, Bates SV, Song Y, Zhang Y, Herzberg EM, Chen YC, Gong M, Chien I, Zhang L, Murphy SN, Gollub RL, Grant PE, Ou Y (2019) Mining multi-site clinical data to develop machine learning MRI biomarkers: application to neonatal hypoxic ischemic encephalopathy. J Transl Med 17(1):385. https://doi.org/10.1186/s12967-019-2119-5

Win M, Beckett R, Thomson L, Thankamony A, Beardsall K (2022) Continuous glucose monitoring in the management of neonates with persistent hypoglycemia and congenital hyperinsulinism. J Clin Endocrinol Metab 107(1):e246–e253. https://doi.org/10.1210/clinem/dgab601

Wright CF, FitzPatrick DR, Firth HV (2018) Paediatric genomics: diagnosing rare disease in children. Nat Rev Genet 19(5):253–268. https://doi.org/10.1038/nrg.2017.116

Wu Y, Jiang D, Bardill A, Bayford R, Demosthenous A (2019) A 122 fps, 1 MHz bandwidth multi-frequency wearable EIT belt featuring novel active electrode architecture for neonatal thorax vital sign monitoring. IEEE Trans Biomed Circuits Syst 13(5):927–937. https://doi.org/10.1109/TBCAS.2019.2925713

Xu D, Vigneron D (2010) Magnetic resonance spectroscopy imaging of the newborn brain—a technical review. Semin Perinatol 34(1):20–27. https://doi.org/10.1053/j.semperi.2009.10.003

Xu K, Fujita Y, Lu Y, Honda S, Shiomi M, Arie T, Akita S, Takei K (2021) A wearable body condition sensor system with wireless feedback alarm functions. Adv Mater 33(18):e2008701. https://doi.org/10.1002/adma.202008701

Yan K, Xiao F, Jiang Y, Lu C, Zhang Y, Kong Y, Zhou J, Wang J, Lin C, Yang H, Zhang D, Cheng G, Qiao Z, Wang L, Qin Q, Zhou W (2021) Amplitude of low-frequency fluctuation may be an early predictor of delayed motor development due to neonatal hyperbilirubinemia: a fMRI study. Transl Pediatr 10(5):1271–1284. https://doi.org/10.2103/tp-20-447

Yang L, Kong Y, Dong X, Hu L, Lin Y, Chen X, Ni Q, Lu Y, Wu B, Wang H, Lu QR, Zhou W (2019) Clinical and genetic spectrum of a large cohort of children with epilepsy in China. Genet Med 21(3):564–571. https://doi.org/10.1038/s41436-018-0091-8

Yang L, Liu X, Li Z, Zhang P, Wu B, Wang H, Hu L, Cheng G, Wang L, Zhou W (2020) Genetic aetiology of early infant deaths in a neonatal intensive care unit. J Med Genet 57(3):169–177. https://doi.org/10.1136/jmedgenet-2019-106221

Yang L, Wei Z, Chen X et al (2022) Use of medical exome sequencing for identification of underlying genetic defects in NICU: experience in a cohort of 2303 neonates in China. Clin Genet 101(1):101–109. https://doi.org/10.1111/cge.14075

Zemojtel T, Kohler S, Mackenroth L et al (2014) Effective diagnosis of genetic disease by computational phenotype analysis of the disease-associated genome. Sci Transl Med 6(252):252123. https://doi.org/10.1126/scitranslmed.3009262

Zhang H, Wang S, Xu X, Chow TWS, Wu QMJ (2018) Tree2Vector: learning a vectorial representation for tree-structured data. IEEE Trans Neural Netw Learn Syst 29(11):5304–5318. https://doi.org/10.1109/TNNLS.2018.2797060

Zhao W, Li H, Hao Y, Hu G, Zhang Y, Frederick BB, Cong F (2022) An efficient functional magnetic resonance imaging data reduction strategy using neighborhood preserving embedding algorithm. Hum Brain Mapp 43(5):1561–1576. https://doi.org/10.1002/hbm.25742