Inferring Gene Regulatory Networks Using the Improved Markov Blanket Discovery Algorithm

Liu H, Ren G, Chen H, Liu Q, Yang Y, Zhao Q (2020) Predicting lncRNA–miRNA interactions based on logistic matrix factorization with neighborhood regularized. Knowl-Based Syst. https://doi.org/10.1016/j.knosys.2019.105261

Lv Y, Bao E (2009) Apoptosis induced in chicken embryo fibroblasts in vitro by a polyinosinic:polycytidylic acid copolymer. Toxicol In Vitro 23(7):1360–1364. https://doi.org/10.1016/j.tiv.2009.06.026

Altay G, Emmert-Streib F (2010) Inferring the conservative causal core of gene regulatory networks. BMC Syst Biol 4:132. https://doi.org/10.1186/1752-0509-4-132

Zhang L, Yang P, Feng H, Zhao Q, Liu H (2021) Using network distance analysis to predict lncRNA-miRNA interactions. Interdiscip Sci 13(3):535–545. https://doi.org/10.1007/s12539-021-00458-z

Elnitski L, Jin VX, Farnham PJ, Jones SJ (2006) Locating mammalian transcription factor binding sites: a survey of computational and experimental techniques. Genome Res 16(12):1455–1464. https://doi.org/10.1101/gr.4140006

Maetschke SR, Madhamshettiwar PB, Davis MJ, Ragan MA (2014) Supervised, semi-supervised and unsupervised inference of gene regulatory networks. Brief Bioinform 15(2):195–211. https://doi.org/10.1093/bib/bbt034

Bar-Joseph Z (2004) Analyzing time series gene expression data. Bioinformatics 20(16):2493–2503. https://doi.org/10.1093/bioinformatics/bth283

Huang S (1999) Gene expression profiling, genetic networks, and cellular states: an integrating concept for tumorigenesis and drug discovery. J Mol Med (Berl) 77(6):469–480. https://doi.org/10.1007/s001099900023

Lim CY, Wang H, Woodhouse S, Piterman N, Wernisch L, Fisher J, Gottgens B (2016) BTR: training asynchronous Boolean models using single-cell expression data. BMC Bioinformatics 17(1):355. https://doi.org/10.1186/s12859-016-1235-y

Zhou JX, Samal A, d’Herouel AF, Price ND, Huang S (2016) Relative stability of network states in Boolean network models of gene regulation in development. Biosystems 142–143:15–24. https://doi.org/10.1016/j.biosystems.2016.03.002

Tan M, Alshalalfa M, Alhajj R, Polat F (2011) Influence of prior knowledge in constraint-based learning of gene regulatory networks. IEEE/ACM Trans Comput Biol Bioinform 8(1):130–142. https://doi.org/10.1109/TCBB.2009.58

Shi M, Shen W, Wang HQ, Chong Y (2016) Adaptive modelling of gene regulatory network using Bayesian information criterion-guided sparse regression approach. IET Syst Biol 10(6):252–259. https://doi.org/10.1049/iet-syb.2016.0005

Alter O, Brown PO, Botstein D (2000) Singular value decomposition for genome-wide expression data processing and modeling. Proc Natl Acad Sci U S A 97(18):10101–10106. https://doi.org/10.1073/pnas.97.18.10101

Cantone I, Marucci L, Iorio F, Ricci MA, Belcastro V, Bansal M, Santini S, di Bernardo M, di Bernardo D, Cosma MP (2009) A yeast synthetic network for in vivo assessment of reverse-engineering and modeling approaches. Cell 137(1):172–181. https://doi.org/10.1016/j.cell.2009.01.055

Honkela A, Girardot C, Gustafson EH, Liu YH, Furlong EE, Lawrence ND, Rattray M (2010) Model-based method for transcription factor target identification with limited data. Proc Natl Acad Sci U S A 107(17):7793–7798. https://doi.org/10.1073/pnas.0914285107

Huppenkothen D, Heil LM, Hogg DW, Mueller A (2017) Using machine learning to explore the long-term evolution of GRS 1915+105. Mon Not R Astron Soc 466(2):2364–2377. https://doi.org/10.1093/mnras/stw3190

Betliński P, Ślęzak D (2012) The Problem of Finding the Sparsest Bayesian Network for an Input Data Set is NP-Hard, vol 7661. https://doi.org/10.1007/978-3-642-34624-8_3

Zhang X, Zhao J, Hao JK, Zhao XM, Chen L (2015) Conditional mutual inclusive information enables accurate quantification of associations in gene regulatory networks. Nucleic Acids Res 43(5):e31. https://doi.org/10.1093/nar/gku1315

Zhao J, Zhou Y, Zhang X, Chen L (2016) Part mutual information for quantifying direct associations in networks. Proc Natl Acad Sci U S A 113(18):5130–5135. https://doi.org/10.1073/pnas.1522586113

Faith JJ, Hayete B, Thaden JT, Mogno I, Wierzbowski J, Cottarel G, Kasif S, Collins JJ, Gardner TS (2007) Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biol 5(1):e8. https://doi.org/10.1371/journal.pbio.0050008

Margolin AA, Nemenman I, Basso K, Wiggins C, Stolovitzky G, Dalla Favera R, Califano A (2006) ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics 7(Suppl 1):S7. https://doi.org/10.1186/1471-2105-7-S1-S7

Meyer PE, Kontos K, Lafitte F, Bontempi G (2007) Information-theoretic inference of large transcriptional regulatory networks. EURASIP J Bioinform Syst Biol. https://doi.org/10.1155/2007/79879

Liu W, Zhu W, Liao B, Chen X (2016) Gene regulatory network inferences using a maximum-relevance and maximum-significance strategy. PLoS One 11(11):e0166115. https://doi.org/10.1371/journal.pone.0166115

Liu W, Zhu W, Liao B, Chen H, Ren S, Cai L (2017) Improving gene regulatory network structure using redundancy reduction in the MRNET algorithm. RSC Adv 7(37):23222–23233. https://doi.org/10.1039/c7ra01557g

Hasman A (1991) Probabilistic reasoning in intelligent systems: networks of plausible inference. Int J Biomed Comput 28(3):221–225. https://doi.org/10.1016/0020-7101(91)90056-k

Peña JM, Nilsson R, Björkegren J, Tegnér J (2007) Towards scalable and data efficient learning of Markov boundaries. Int J Approximate Reasoning 45(2):211–232. https://doi.org/10.1016/j.ijar.2006.06.008

Yaramakala S, Margaritis D (2005) Speculative Markov blanket discovery for optimal feature selection. Paper presented at the Data Mining, Fifth IEEE International Conference on. https://doi.org/10.1109/ICDM.2005.134.

Jain A, Zongker D (1997) Feature selection: evaluation, application, and small sample performance. IEEE Trans Pattern Anal Mach Intell 19(2):153–158. https://doi.org/10.1109/34.574797

Zhao F, Qiu HM, Pan LQ, Zhu H, Zhang YP, Guo ZG, Yin JH, Zhao XD, Xiao JQ (2008) Ferromagnetism analysis of Mn-doped CuO thin films. J Phys. https://doi.org/10.1088/0953-8984/20/42/425208

Zhang Y, Zhang Z, Liu K, Qian G (2010) An improved IAMB algorithm for Markov Blanket Discovery. J Comp. https://doi.org/10.4304/jcp.5.11.1755-1761

Tsamardinos I, Aliferis C, Statnikov A (2003) Time and Sample Efficient Discovery of Markov Blankets And Direct Causal Relations. https://doi.org/10.1145/956750.956838

Ramanan N, Natarajan S (2020) Causal learning from predictive modeling for observational data. Front Big Data 3:535976. https://doi.org/10.3389/fdata.2020.535976

Fu S, Fast Desmarais MC (2008) Markov Blanket Discovery Algorithm Via Local Learning within Single Pass. In, Berlin, Heidelberg. Adv Artif Intell. https://doi.org/10.1007/978-3-540-68825-9_10

Yang Y (2012) Elements of information theory. J Am Stat Assoc 103(481):429–429. https://doi.org/10.1198/jasa.2008.s218

Altay G, Emmert-Streib F (2010) Revealing differences in gene network inference algorithms on the network level by ensemble methods. Bioinformatics 26(14):1738–1744. https://doi.org/10.1093/bioinformatics/btq259

Samoilov M, Arkin A, Ross J (2001) On the deduction of chemical reaction pathways from measurements of time series of concentrations. Chaos 11(1):108–114. https://doi.org/10.1063/1.1336499

Marbach D, Prill RJ, Schaffter T, Mattiussi C, Floreano D, Stolovitzky G (2010) Revealing strengths and weaknesses of methods for gene network inference. Proc Natl Acad Sci U S A 107(14):6286–6291. https://doi.org/10.1073/pnas.0913357107

Ronen M, Rosenberg R, Shraiman BI, Alon U (2002) Assigning numbers to the arrows: parameterizing a gene regulation network by using accurate expression kinetics. Proc Natl Acad Sci U S A 99(16):10555–10560. https://doi.org/10.1073/pnas.152046799

Boughorbel S, Jarray F, El-Anbari M (2017) Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric. PLoS ONE 12(6):e0177678. https://doi.org/10.1371/journal.pone.0177678

Zhang X, Liu K, Liu ZP, Duval B, Richer JM, Zhao XM, Hao JK, Chen L (2013) NARROMI: a noise and redundancy reduction technique improves accuracy of gene regulatory network inference. Bioinformatics 29(1):106–113. https://doi.org/10.1093/bioinformatics/bts619