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DM-BLD: differential methylation detection using a hierarchical Bayesian model exploiting local dependency

Xiao Wang, Jinghua Gu, Leena Hilakivi-Clarke, Robert Clarke, Jianhua Xuan
2016 Bioinformatics  
genes based on a Bayesian framework.  ...  Specifically, the local dependency is modeled by Leroux conditional autoregressive structure; the dependency of methylation changes is modeled by a discrete Markov random field.  ...  A discrete Markov random field is then used to model the dependency of methylation change (via differential states) of neighboring CpG sites.  ... 
doi:10.1093/bioinformatics/btw596 pmid:27616707 pmcid:PMC5254079 fatcat:bx25i6qg7vbuvjmwt5vrtpyxwa

Approximate Bayesian bisulphite sequencing analysis (ABBA) for analysis of WGBS in disease [article]

Owen J.L. Rackham, Sarah R. Langley, Thomas Oates, Eleni Vradi, Nathan Harmston, Prashant K. Srivastava, Jacques Behmoaras, Petros Dellaportas, Leonardo Bottolo, Enrico Petretto
2016 bioRxiv   pre-print
Investigation of these DMRs revealed differential DNA methylation localized to a 600bp region in the promoter of the Ifitm3 gene.  ...  Here, we present a novel Bayesian smoothing approach (called ABBA) to detect differentially methylated regions (DMRs) from whole-genome bisulphite sequencing (WGBS).  ...  ABBA is a Bayesian structured generalized mixed additive model with a latent Gaussian field (i.e., the unobserved methylation profile), controlled by a few hyperparameters, and with a non--Gaussian response  ... 
doi:10.1101/041715 fatcat:7k6gyjpzivdu7obqiugyjuym5m

A Bayesian Approach for Analysis of Whole-Genome Bisulfite Sequencing Data Identifies Disease-Associated Changes in DNA Methylation

Owen J. L. Rackham, Sarah R. Langley, Thomas Oates, Eleni Vradi, Nathan Harmston, Prashant K. Srivastava, Jacques Behmoaras, Petros Dellaportas, Leonardo Bottolo, Enrico Petretto
2017 Genetics  
Investigation of these DMRs revealed differential DNA methylation localized to a 600 bp region in the promoter of the Ifitm3 gene.  ...  Here, we present a novel Bayesian smoothing approach (called ABBA) to detect differentially methylated regions (DMRs) from whole-genome bisulfite sequencing (WGBS).  ...  Acknowledgments The authors are thankful to the two anonymous referees whose meticulous attention to their refereeing task has resulted in substantial improvements in the presentation.  ... 
doi:10.1534/genetics.116.195008 pmid:28213474 pmcid:PMC5378105 fatcat:mi3rd4ttkfgr3ebivdsccr4u4m

Heterogeneous Reciprocal Graphical Models [article]

Yang Ni, Peter Mueller, Yitan Zhu, Yuan Ji
2018 arXiv   pre-print
We develop novel hierarchical reciprocal graphical models to infer gene networks from heterogeneous data.  ...  In the case of data that can be naturally divided into known groups, we propose to connect graphs by introducing a hierarchical prior across group-specific graphs, including a correlation on edge strengths  ...  Bayesian model selection is implemented with a thresholding prior and used to obtain sparse networks.  ... 
arXiv:1612.06045v3 fatcat:ilrbcw4djrbqtdxvktlrgyrmra

Computational dynamic approaches for temporal omics data with applications to systems medicine

Yulan Liang, Arpad Kelemen
2017 BioData Mining  
Modeling and predicting biological dynamic systems and simultaneously estimating the kinetic structural and functional parameters are extremely important in systems and computational biology.  ...  However, the delineation of the possible associations and causalities of genes, proteins, metabolites, cells and other biological entities from high throughput time course omics data is challenging for  ...  Availability of data and materials Data sharing not applicable to this article as no datasets were generated or analysed during the current study.  ... 
doi:10.1186/s13040-017-0140-x pmid:28638442 pmcid:PMC5473988 fatcat:rscvtjlpgrf53fbwlt6t4i22em

Exploring Patterns of Epigenetic Information with Data Mining Techniques

Vanessa Aguiar-Pulido, Jose A. Seoane, Marcos Gestal, Julian Dorado
2013 Current pharmaceutical design  
Part of these data may contain patterns of epigenetic information which are mitotically and/or meiotically heritable determining gene expression and cellular differentiation, as well as cellular fate.  ...  Data mining, a part of the Knowledge Discovery in Databases process (KDD), is the process of extracting patterns from large data sets by combining methods from statistics and artificial intelligence with  ...  ACKNOWLEDGEMENTS Vanessa Aguiar-Pulido acknowledges the funding support for a research position by the "Plan I2C" program from Xunta de Galicia (Spain), being also co-funded by FEDER.  ... 
doi:10.2174/138161213804581936 pmid:23016855 fatcat:swwfvwhusvblpcyyqnlxdo6cqu

Nonparametric Bayes Differential Analysis for Dependent Multigroup Data with Application to DNA Methylation Analyses in Cancer [article]

Chiyu Gu, Veerabhadran Baladandayuthapani, Subharup Guha
2022 arXiv   pre-print
We propose BayesDiff, a nonparametric Bayesian approach based on a novel class of first order mixture models, called the Sticky Poisson-Dirichlet process or multicuisine restaurant franchise.  ...  In simulation studies, we demonstrate the effectiveness of the BayesDiff procedure relative to existing techniques for differential DNA methylation.  ...  These methods rely on HMMs to model the methylation data for the entire genome and detect differentially methylated sites based on the inferred hidden states.  ... 
arXiv:1710.10713v6 fatcat:jrwmtbzwo5b4xb2ujvln4ygdaa

Network-Based Identification of Biomarkers Coexpressed with Multiple Pathways

Nancy Lan Guo, Ying-Wooi Wan
2014 Cancer Informatics  
Specifically, we evaluated implication networks, Boolean networks, Bayesian networks, and Pearson's correlation networks in constructing gene coexpression networks for identifying lung cancer diagnostic  ...  networks, coexpression with signaling pathways, lung cancer biomarkers SUPPLEMENT: network and Pathway analysis of Cancer susceptibility (a)  ...  Markov network, also known as Markov random field, is a statistical framework to analyze and visualize conditional relationships between sets of random variables.  ... 
doi:10.4137/cin.s14054 pmid:25392692 pmcid:PMC4218687 fatcat:kgxtqqip7nazpjckldlieaipgu

The International Conference on Intelligent Biology and Medicine (ICIBM) 2019: bioinformatics methods and applications for human diseases

Zhongming Zhao, Yulin Dai, Chi Zhang, Ewy Mathé, Lai Wei, Kai Wang
2019 BMC Bioinformatics  
The conference included 12 scientific sessions, five tutorials or workshops, one poster session, four keynote talks and four eminent scholar talks that covered a wide range of topics in bioinformatics,  ...  Acknowledgments Our heartfelt thanks to all the reviewers for reviewing a large number of manuscripts submitted to ICIBM 2019 and the related special issues.  ...  We would like to thank all the session chairs for seamlessly moderating the scientific sessions and many volunteers for the local support.  ... 
doi:10.1186/s12859-019-3240-4 pmid:31861973 pmcid:PMC6924135 fatcat:244ceco3djhh7fq4goelfalykm

Identification of Differentially Methylated Sites with Weak Methylation Effects

Hong Tran, Hongxiao Zhu, Xiaowei Wu, Gunjune Kim, Christopher Clarke, Hailey Larose, David Haak, Shawn Askew, Jacob Barney, James Westwood, Liqing Zhang
2018 Genes  
To address these issues, the wavelet-based functional mixed model (WFMM) was introduced to detect DMCs.  ...  To further examine the performance of WFMM in detecting weak differential methylation events, we used both simulated and empirical data and compare WFMM performance to a popular DMC detection tool methylKit  ...  Genes 2018, 9, 75  ... 
doi:10.3390/genes9020075 pmid:29419727 pmcid:PMC5852571 fatcat:aym3pmkh3zajdmktptiustxnxi

The Reasonable Effectiveness of Randomness in Scalable and Integrative Gene Regulatory Network Inference and Beyond

Michael Banf, Thomas Hartwig
2021 Computation  
As an example, one of the top performing algorithms in a community challenge on gene regulatory network inference from transcriptomic data is based on a random forest regression model.  ...  With respect to scalability, randomized approaches have proven to be a promising alternative to deterministic methods in computational biology.  ...  [130] adopt a common ordinary differential equation (ODE) based model to represent the gene regulatory network as a dynamical system.  ... 
doi:10.3390/computation9120146 fatcat:nqojyucoezehvatapdqlmbabb4

Zodiac: A Comprehensive Depiction of Genetic Interactions in Cancer by Integrating TCGA Data

Yitan Zhu, Yanxun Xu, Donald L. Helseth, Kamalakar Gulukota, Shengjie Yang, Lorenzo L. Pesce, Riten Mitra, Peter Müller, Subhajit Sengupta, Wentian Guo, Jonathan C. Silverstein, Ian Foster (+3 others)
2015 Journal of the National Cancer Institute  
It is an evolution of existing knowledge by treating it as a prior graph, integrating it with a likelihood model derived by Bayesian graphical model based on TCGA data, and producing a posterior graph  ...  Methods: We introduce Zodiac, a computational tool and resource to integrate existing knowledge about cancer genetic interactions with new information contained in TCGA data.  ...  A functional network involving multiple modalities of a gene or gene pair is treated as a Markov random field and Markov chain Monte Carlo simulations are used for statistical estimation (Supplementary  ... 
doi:10.1093/jnci/djv129 pmid:25956356 pmcid:PMC4554190 fatcat:a5n75kepnral5p6lg232yftema

Integrating Heterogeneous omics Data via Statistical Inference and Learning Techniques

Ashar Ahmad, Holger Fröhlich
2016 Genomics and Computational Biology  
In the first part of our article, we focus on techniques to identify a relevant biological sub-system based on combined omics data.  ...  Multi-omics studies are believed to provide a more comprehensive picture of a complex biological system than traditional studies with one omics data source.  ...  This type of analysis can be extended to more than two data modalities: Sun et al. looked for overlaps between differentially expressed and differentially methylated genes as well as for regions with statistically  ... 
doi:10.18547/gcb.2016.vol2.iss1.e32 fatcat:xmdsdhzdj5czvljgfhvwqlypbm

Integrative network analysis of TCGA data for ovarian cancer

Qingyang Zhang, Joanna E Burdette, Ji-Ping Wang
2014 BMC Systems Biology  
We built a Bayesian network model with a logit link function to quantify the causal relationships among these features and discovered a set of 13 hub genes including ARID1A, C19orf53, CSKN2A1 and COL5A2  ...  The seed genes were then fed into a stepwise correlation-based selector to identify 271 additional features including 177 genes, 82 copy number variation sites, 11 methylation sites and 1 somatic mutation  ...  The SCBS procedure is model-free and computationally efficient and it can be applied to other graphical models such as Markov Random Field (MRF, undirected graph) and gene-gene or protein-protein interaction  ... 
doi:10.1186/s12918-014-0136-9 pmid:25551281 pmcid:PMC4331442 fatcat:72icob6bnngzxnkmblnggnbrcu

Bayesian integrative analysis of epigenomic and transcriptomic data identifies Alzheimer's disease candidate genes and networks

Hans-Ulrich Klein, Martin Schäfer, David A. Bennett, Holger Schwender, Philip L. De Jager, Donna K. Slonim
2020 PLoS Computational Biology  
For statistical inference, a Bayesian hierarchical model is used to study the distribution of the integrative coefficient.  ...  The model employs a conditional autoregressive prior to integrate a functional gene network and to share information between genes known to be functionally related.  ...  In Bayesian models, conditionally autoregressive (CAR) Markov random field priors were frequently used to incorporate gene networks into genome-wide data analyses [25, [30] [31] [32] .  ... 
doi:10.1371/journal.pcbi.1007771 pmid:32255787 fatcat:q4xm5udeqbhjlofwbexegyf73u
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