Abstract
Biology has become a data driven science largely due to the technological advances that have generated large volumes of data. To extract meaningful information from these data sets requires the use of sophisticated modeling approaches. Toward that, artificial neural network (ANN) based modeling is increasingly playing a very important role. The “black box” nature of ANNs acts as a barrier in providing biological interpretation of the model. Here, the basic steps toward building models for biological systems and interpreting them using calliper randomization approach to capture complex information are described.
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References
Weinberg R (2010) Point: hypotheses first. Nature 464:678
Golub T (2010) Counterpoint: data first. Nature 464:679
Groffen J, Stephenson JR, Heisterkamp N et al (1984) Philadelphia chromosomal breakpoints are clustered within a limited region, bcr, on chromosome 22. Cell 36:93–99
Nowell PC (1962) The minute chromosome (Phl) in chronic granulocytic leukemia. Blut 8:65–66
Nowell PC (2007) Discovery of the Philadelphia chromosome: a personal perspective. J Clin Invest 117:2033–2035
Salesse S, Verfaillie CM (2002) BCR/ABL: from molecular mechanisms of leukemia induction to treatment of chronic myelogenous leukemia. Oncogene 21:8547–8559
Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323:533–536
Westfall PH (1997) Multiple testing of general contrasts using logical constraints and correlations. J Am Stat Assoc 92:299–306
Nair TM (2012) Analysis of isoform expression from splicing array using multiple comparisons. Methods Mol Biol 802:113–121
Urbanowicz RJ, Meeker M, La Cava W et al (2018) Relief-based feature selection: introduction and review. J Biomed Inform 85:189–203
Liang S, Ma A, Yang S et al (2018) A review of matched-pairs feature selection methods for gene expression data analysis. Comput Struct Biotechnol J 16:88–97
Liu H, Wong L (2003) Data mining tools for biological sequences. J Bioinforma Comput Biol 1:139–167
Bergmeir C, Benítez JM (2012) Neural networks in R using the stuttgart neural network simulator: RSNNS. J Stat Softw 1(7)
Swets JA, Dawes RM, Monahan J (2000) Better decisions through science. Sci Am 283:82–87
Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27:861–874
Bewick V, Cheek L, Ball J (2004) Statistics review 13: receiver operating characteristic curves. Crit Care 8:508–512
Griffith F (1928) The significance of pneumococcal types. J Hyg (Lond) 27:113–159
Avery OT, Macleod CM, McCarty M (1944) Studies on the chemical nature of the substance inducing transformation of pneumococcal types : induction of transformation by a desoxyribonucleic acid fraction isolated from pneumococcus type iii. J Exp Med 79:137–158
Nair TM, Tambe SS, Kulkarni BD (1994) Application of artificial neural networks for prokaryotic transcription terminator prediction. FEBS Lett 346:273–277
Nair TM (2018) Statistical and artificial neural network-based analysis to understand complexity and heterogeneity in preeclampsia. Comput Biol Chem 75:222–230
Nair TM (1997) Calliper randomization: an artificial neural network based analysis of E. coli ribosome binding sites. J Biomol Struct Dyn 15:611–617
Acknowledgments
I would like to thank IUSB for funding this work. This work is also supported partly by NSF award 1726218.
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Nair, T.M. (2021). Building and Interpreting Artificial Neural Network Models for Biological Systems. In: Cartwright, H. (eds) Artificial Neural Networks. Methods in Molecular Biology, vol 2190. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0826-5_8
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DOI: https://doi.org/10.1007/978-1-0716-0826-5_8
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