Artificial Neural Networks, and Evolutionary Algorithms as a systems biology approach to a data-base on fetal growth restriction

Progress in Biophysics and Molecular Biology

Volumn 113, Issue 3, 2013, Pages 433-438

  Street, Maria E ^a   Buscema, Massimo ^b,c   Smerieri, Arianna ^a   Montanini, Luisa ^a   Grossi, Enzo ^b  

^a UNIVERSITY HOSPITAL OF PARMA   (Italy)

^b Semeion centro ricerche di scienze della communicazione   (Italy)

^c UNIVERSITY OF COLORADO   (United States)

Author keywords

ANNS; EA; Fetal growth; IGF; IGFBP; IGFBP 2; IL 6; IUGR; Systems biology

Indexed keywords

INTERLEUKIN 6; SOMATOMEDIN;

ALGORITHM; ANIMAL; ANNS; ARTICLE; ARTIFICIAL NEURAL NETWORK; EA; EVOLUTIONARY ALGORITHM; FACTUAL DATABASE; FETUS GROWTH; HUMAN; IGF; IGFBP; IGFBP-2; IL-6; INSULIN-LIKE GROWTH FACTOR BINDING PROTEIN; INTRA-UTERINE GROWTH RESTRICTION; INTRAUTERINE GROWTH RETARDATION; IUGR; METHODOLOGY; MOLECULAR EVOLUTION; SYSTEMS BIOLOGY;

ANNS; ARTIFICIAL NEURAL NETWORKS; EA; EVOLUTIONARY ALGORITHMS; FETAL GROWTH; IGF; IGFBP; IGFBP-2; IL-6; INSULIN-LIKE GROWTH FACTOR; INSULIN-LIKE GROWTH FACTOR BINDING PROTEIN; INTERLEUKIN-6; INTRA-UTERINE GROWTH RESTRICTION; IUGR; SYSTEMS BIOLOGY;

ALGORITHMS; ANIMALS; DATABASES, FACTUAL; EVOLUTION, MOLECULAR; FETAL GROWTH RETARDATION; HUMANS; NEURAL NETWORKS (COMPUTER); SYSTEMS BIOLOGY;

EID: 84888884168     PISSN: 00796107     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.pbiomolbio.2013.06.003     Document Type: Article

References (62)

1. Aguirre V., Werner E.D., Giraud J., Lee Y.H., Shoelson S.E., White M.F. Phosphorylation of Ser307 in insulin receptor substrate-1 blocks interactions with the insulin receptor and inhibits insulin action. J.Biol. Chem. 2002, 277:1531-1537.
2. Amalilyo G., Oren A., Mimouni F.B., Ochshorn Y., Deutsch V., Mandel D. Increased cord serum inflammatory markers in small-for-gestational-age neonates. J.Perinatol. 2011, 31:30-32.
3. Amu S., Hahn-Zoric M., Malik A., Ashraf R., Zaman S., Kjellmer I., Hagberg H., Padyukov L., Hanson L.A. Cytokines in the placenta of Pakistani newborns with and without intraueterine growth retardation. Pediatr. Res. 2006, 59:254-258.
4. Baldassarre D., Grossi E., Buscema M., Intraligi M., Amato M., Tremoli E., Pustina L., Castelnuovo S., Sanvito S., Gerosa L., Sirtori C.R. Recognition of patients with cardiovascular disease by artificial neural networks. Ann. Med. 2004, 36:630-640.
5. Baker J., Liu J.P., Robertson E.J., Efstratiadis A. Role of insulin-like growth factors in embryonic and postnatal growth. Cell 1993, 75:73-82.
6. Bartha J.L., Romero-Carmona R., Comino-Delgrado R. Inflammatory cytokines in intrauterine growth retardation. Acta Obstet. Gynecol. Scand. 2003, 82:1099-1102.
7. Buchman T.G., Kubos K.L., Seidler A.J., Siegforth M.J. Acomparison of statistical and connectionist models for the prediction of chronicity in a surgical intensive care unit. Crit. Care Med. 1994, 22:750-762.
8. Buscema M., Grossi E., Intraligi M., Garbagna N., Andriulli A., Breda M. An optimized experimental protocol based on neuro-evolutionary algorithms application to the classification of dyspeptic patients and to the prediction of the effectiveness of their treatment. Artif. Intell. Med. 2005, 34:279-305.
9. Buscema M., Grossi E. Anovel adapting method for emergent properties discovery in data bases: experience in medical field. Proceedings IEEE International Conference on Systems, Man, and Cybernetics (SMC 2007) 2007, 3457-3463.
10. Buscema M., Grossi E. The semantic connectivity map: an adapting self-organizing knowledge discovery method in data bases. Experience in gastro-oesophageal reflux disease. Int. J. Data Min. Bioinform. 2008, 2:362-404.
11. Casey S., Greene O.G., Troyanskaya Data-driven view of disease biology. (Chapter 2), PLOS Computational Biol 2012, Published 27 Dec 2012. 10.1371/journal.pcbi.1002816.
12. Cox B., Kotlyar M., Evangelou A.I., Ignatchenko V.L., Whiteley K., Jurisica I., Adamson S.L., Rossant J., Kislinger T. Comparative systems biology of human and mouse as a tool to guide the modeling of human placental pathology. Mol. Syst. Biol. 2009, 5:279-293.
13. DeChiara T.M., Robertson E.J., Efstratiadis A. Agrowth-deficient phenotype in heterozygous mice carrying an insulin-like growth factor-II gene disrupted by targeting. Nature 1990, 344:78-80.
14. DeGroff C.G., Bhatikar S., Hertzberg J., Shandas R., Valdes-Cruz L., Mahajan R.L. Artificial neural network-based method of screening heart murmurs in children. Circulation 2001, 103:2711-2716.
15. DiRusso S.M., Chahine A.A., Sullivan T., Risucci D., Nealon P., Cuff S., Savino J., Slim M. Development of a model for prediction of survival in pediatric trauma patients: comparison of artificial neural networks and logistic regression. J.Pediatr. Surg. 2002, 37:1098-1104.
16. Elfayomy A.K., Habib F.A., Almasry S.M., Safwat M.D., Eldomiaty M.A. Serum levels of adrenomedullin and inflammatory cytokines in women with term idiopathic intrauterine growth restriction. J.Obstet. Gynaecol. 2013, 33:135-139.
17. Farmer J.D., Foley D. The economy needs agent-based modeling. Nature 2009, 460:685-686.
18. Feltes B.C., de Faria Poloni J., Bonatto D. The developmental aging and origins of health and disease hypotheses explained by different protein networks. Biogerontology 2011, 12:293-308.
19. Feltes B.C., de Faria Poloni J., Notari D.L., Bonatto D. Toxicological effects of the different substances in tobacco smoke on human embryonic development by a systems chemo-biology approach. PLoS One 2013, 8:e61743.
20. Godfrey K.M., Barker D.J. Fetal nutrition and adult disease. Am. J. Clin. Nutr. 2000, 71:1344S-1352S.
21. Grossi E. Non linearity in medicine: a problem or an opportunity?. Br. Med. J. 2001, Online:. http://www.bmj.com/rapid-response/2011/10/28/non-linearity-medicine-problem-or-opportunity.
22. Grossi E., Buscema M. Artificial intelligence and outcome research. Drug Dev. Res. 2006, 67:227-244.
23. Grossi E., Buscema M. Introduction to artificial neural networks. Eur. J. Gastroenterol. Hepatol. 2007, 19:1046-1054.
24. Han V.K., Bassett N., Walton J., Challis J.R.G. The expression of insulin-like growth factor (IGF) and IGF-Binding Protein (IGFBP) genes in the human placenta and membranes: evidence for IGF-IGFBP interactions at the feto-maternal interface. J.Clin. Endocrinol. Metab. 1996, 81:2680-2693.
25. Hata T., Kawamura T., Inada K., Fujiwaki R., Ariyuki Y., Hata K., Kitao M. Interleukin-6, interleukin-8, and granulocyte elastase in newborns with fetal distress. Gynecol. Obstet. Invest. 1996, 42:174-177.
26. Jaing J.T., Sepulveda J.A., Casillas A.M. Novel computer-based assessment of asthma strategies in inner-city children. Ann. Allergy Asthma Immunol. 2001, 87:230-237.
27. Karlberg J., Albertsson-Wikland K. Growth in full-term small-for-gestational-age infants: from birth to final height. Pediatr. Res. 1995, 995(38):733-739.
28. Klover P.J., Zimmers T.A., Koniaris L.G., Mooney R.A. Chronic exposure to interleukin-6 causes hepatic insulin resistance in mice. Diabetes 2003, 52:2784-2789.
29. Lahner E., Grossi E., Intraligi M., Buscema M., Delle Fave G., Annibale B. Possible contribution of advanced statistical methods (artificial neural networks and linear discriminant analysis) in the recognition of patients with suspected atrophic body gastritis. World J. Gastroenterol. 2005, 11:5867-5873.
30. Lapuerta P., L'Italien G.J., Paul S., Hendel R.C., Leppo J.A., Fleischer L.A., Cohen M.C., Eaglek A., Giugliano R.P. Neural network assessment of perioperative cardiac risk in vascular surgery patients. Med. Decis. Making 1998, 18:70-75.
31. Li Y., Samuel D.J., Sundaraj K.P., Lopes-Virella M.F., Huang Y. IL-6 and high glucose synergistically upregulate MMP-1 expression by U937 mononuclear phagocytes via ERK 1/2 and JNK pathways and c-Jun. J.Cell. Biochem. 2010, 110:248-259.
32. Lisboa P.J. Areview of evidence of health benefit from artificial neural networks in medical intervention. Neural Netw. Jan. 2002, 15:11-39.
33. Liu J.P., Baker J., Perkins A.S., Robertson E.J., Efstratiadis A. Mice carrying null mutations of the genes encoding insulin-like growth factor-I (IGF-I) and Type-1 IGF Receptor (Igf1r). Cell 1993, 75:59-72.
34. Maldelbrot B.B. The Factal Geometry of Nature 1978, Freeman, New York.
35. Martos-Moreno G.A., Barrios V., Saenz de Pipaon M., Pozo J., Dorronsoro I., Martinez-Biarge M., Quero J., Argente J. Influence of prematurity and growth restriction on the adipokine profile, IGF1, and gherlino levels in cord blood: relationship with glucose metabolism. Eur. J. Endocrinol. 2009, 161:381-389.
36. Mecocci P., Grossi E., Buscema M., Intraligi M., Savare R., Rinaldi P.C., Cherubini A., Sanin U. Use of artificial networks in clinical trials: a pilot study to predict responsiveness to Donezepil in Alzheimer's disease. J.Am. Geriatr. Soc. 2002, 50:1857-1860.
37. Mueller M., Wagner C.L., Annibale D.J., Hulsey T.C., Knapp R.G., Almeida J.S. Predicting extubation outcome in preterm newborns: a comparison of neural networks with clinical expertise and statistical modelling. Pediatr. Res. 2004, 56:11-18.
38. Myrskylä M., Kohler H.P., Billari F.C. Advances in development reverse fertility declines. Nature 2009, 460:741-743.
39. Namasivayam A., Waldemar A.C. Comparison of the prediction of extremely low birth weight neonatal mortality by regression analysis and by neural networks. Early Hum. Dev. 2001, 65:123-137.
40. Nahum R., Brenner O., Zahalka M.A., Traub L., Quintana F., Moroz C. Blocking of the placental immune-modulatory ferritin activates Th1 type cytokines and affects placenta development, fetal growth and the pregnancy out come. Hum. Reprod. 2004, 19:715-722.
41. Ødegård R.A., Vatten L.J., Nilsen S.T., Salvesen K.A., Vefring H., Austgulen R. Umbilical cord plasma interleukin-6 and fetal growth restriction in preeclampsia: a prospective study in Norway. Obs. Gynecol. 2001, 98:289-294.
42. Ohlsson R., Holmgren L., Glaser A., Szpecht A., Pfeifer-Ohlsson S. Insulin-like growth factor 2 and short-range stimulatory loops in control of human placental growth. EMBO J. 1989, 8:1993-1999.
43. Ozes O.N., Akca H., Mayo L.D., Gustin J.A., Maehama T., Dixon J.E., Donner D.B. Aphosphatidylinositol 3-kinase/Akt/mTOR pathway mediates and PTEN antagonizes tumor necrosis factor inhibition of insulin signalling through insulin receptor substrate-1. Proc. Natl. Acad. Sci. U S A 2001, 98:4640-4645.
44. Penco S., Grossi E., Cheng S., Intraligi M., Maurelli G., Patrosso M.C., Marocchi A., Buscema M. Assessment of the role of genetic polymorphism in venous thrombosis through artificial neural networks. Ann. Hum. Gen. 2005, 69:693-706.
45. President's Council Of Advisors On Science And Technology Priorities for Personalized Medicine September 2008, Washington, USA.
46. Resnik R. Intrauterine growth restriction. Obs. Gynecol. 2002, 99:490-496.
47. Senn J.J., Klover P.J., Nowak I.A., Mooney R.A. Interleukin-6 induces cellular insulin resistance in hepatocytes. Diabetes 2002, 51:3391-3399.
48. Shen S.J., Wang C.Y., Nelson K.K., Jansen M., Ilan J. Expression of insulin-like growth factor II in human placentas from normal and diabetic pregnancies. Proc. Natl. Acad. Sci. U S A 1986, 83:9179-9182.
49. Smerieri A., Petraroli M., Ziveri M.A., Volta C., Bernasconi S., Street M.E. Effects of cord serum insulin, IGF-II, IGFBP-2, IL-6 and cortisol concentrations on human birth weight and length: pilot study. PLoS One 2011, 6:e29562.
50. Steeb W.H. The Non Linear Workbook 2006, World Scientific, NJ.
51. Street M.E., Seghini P., Fieni S., Ziveri M.A., Volta C., Martorana D., Gramellini D., Bernasconi S. Interleukin (IL)-6 and IGF-IGFBP relationships in placenta and cord blood: possibile determinants of fetal growth restrction (IUGR). Eur. J. Endocrinol. 2006, 155:567-574.
52. Street M.E., Ziveri M.A., Spaggiari C., Viani I., Volta C., Grzincich G.L., Virdis R., Bernasconi S. Inflammation is a modulator of the IGF-IGFBP system inducing reduced bioactivity of IGFs in cystic fibrosis. Eur. J. Endocrinol. 2006, 154:1-7.
53. Street M.E., Grossi E., Volta C., Faleschini E., Bernasconi S. Placental determinants of fetal growth: identification of key factors in the IGF and cytokine systems using artificial neural networks. BMC Pediatr. 2008, 8:24.
54. Street M.E., Volta C., Ziveri M.A., Zanacca C., Banchini G., Viani I., Rossi M., Virdis R., Bernasconi S. Changes and relationships of IGFs and IGFBPs and cytokines in coeliac disease at diagnosis and on gluten-free diet. Clin. Endocrinol. 2008, 68:22-28.
55. Tesfatsion L., Judd K. Handbook of Computational Economics 2006, Elsevier, Amsterdam.
56. Wang C.Y., Daimon M., Shen S.J., Engelman G.L., Ilan J. Insulin-like growth factor-I messenger ribonucleic acid in the developing human placenta and in term placenta of diabetics. Mol. Endocrinol. 1998, 2:217-229.
57. Witten I.H., Frank E. Data Mining 2005, Morgan Kaufmann Publishers, Elsevier, San Francsico.
58. Xochitl C., Morgan L., Huttenhower C. Human microbiome analysis. (Chapter 12), PLOS Computational Biol 2012, Published 27 Dec 2012. 10.1371/journal.pcbi.1002816.
59. Zernikow B., Holtmannspoetter K., Michel E., Theilhaber M., Pielemeier W., Hennecke K.H. Artificial neural network for predicting intracranial haemorrhage in preterm neonates. Acta Paediatr. 1998, 87:969-975.
60. Zimmermann H.J. Fuzzy Theory Set and Its Application 2006, Kluwer Academic Publisher, Boston.
61. Zollers W.G., Babischkin J.S., Pepe G.J., Albrecht E.D. Developmental regulation of placental insulin-like growth factor (IGF)-II and IGF-binding protein-1 and -2 messenger RNA expression during primate pregnancy. Biol. Reprod. 2001, 165:1208-1214.
62. Zou J., Han Y., So S.S. Overview of artificial neural networks. Methods Mol. Biol. 2008, 458:15-23.