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A Bayesian framework for cell-level protein network analysis for multivariate proteomics image data

Violet N. Kovacheva, Korsuk Sirinukunwattana, Nasir M. Rajpoot, Metin N. Gurcan, Anant Madabhushi
2014 Medical Imaging 2014: Digital Pathology  
In this paper, we propose a Bayesian framework for celllevel network analysis allowing the identification of several protein pairs having significantly higher co-expression levels in cancerous tissue samples  ...  The recent development of multivariate imaging techniques, such as the Toponome Imaging System (TIS), has facilitated the analysis of multiple co-localisation of proteins.  ...  CONCLUSIONS We have presented a Bayesian framework for phenotyping cells according to their protein-protein dependence profiles.  ... 
doi:10.1117/12.2045028 dblp:conf/midp/KovachevaSR14 fatcat:ld5nabz2r5dzbpgj3yljr3iuty

Pathway and Network Approaches for Identification of Cancer Signature Markers from Omics Data

Jinlian Wang, Yiming Zuo, Yan-gao Man, Itzhak Avital, Alexander Stojadinovic, Meng Liu, Xiaowei Yang, Rency S. Varghese, Mahlet G Tadesse, Habtom W Ressom
2015 Journal of Cancer  
To address these challenges, a number of pathway and network based approaches have been introduced.  ...  from such massive data and to evaluate the findings.  ...  For example, Yang et al. [21] used a Bayesian network to construct HCC cell networks and identify functional modules and interactions between these modules.  ... 
doi:10.7150/jca.10631 pmid:25553089 pmcid:PMC4278915 fatcat:7ngrvqd2ijbixoxdhx5xusdyoq

Trials, Skills, and Future Standpoints of AI Based Research in Bioinformatics

2020 International journal of recent technology and engineering  
Computational biology, genomics, proteomics, Drug designing, gene expression level analysis are the major research areas in bioinformatics. These areas are also discussed in the paper.  ...  Such algorithms can learn over a period of time while in execution and improves its performance and continue learning.  ...  , Bayesian networks, Gaussian networks.  ... 
doi:10.35940/ijrte.a1920.059120 fatcat:hmqqdp5hx5a3fi52gydffn5ffy

Integrated Omics: Tools, Advances, and Future Approaches

Biswapriya B Misra, Carl D Langefeld, Michael Olivier, Laura A Cox
2018 Journal of Molecular Endocrinology  
data files on a daily basis.  ...  With the rapid adoption of high-throughput omic approaches to analyze biological samples such as genomics, transcriptomics, proteomics, and metabolomics, each analysis can generate tera- to peta-byte sized  ...  Based on current knowledge and tools, they conclude that for network-based applications, Bayesian network approaches are a useful compromise between network analysis and probability theory, where the Bayesian  ... 
doi:10.1530/jme-18-0055 pmid:30006342 fatcat:62c6xglxcbhhnkxgqo5gt7garq

Personalized Integrated Network Modeling of the Cancer Proteome Atlas

Min Jin Ha, Sayantan Banerjee, Rehan Akbani, Han Liang, Gordon B. Mills, Kim-Anh Do, Veerabhadran Baladandayuthapani
2018 Scientific Reports  
PRECISE is a general Bayesian framework for integrating existing interaction databases, data-driven de novo causal structures, and upstream molecular profiling data to estimate cancer-specific integrated  ...  For example, methods based on gene set enrichment analysis 7-9 use functional information that assesses the statistical overrepresentation of genes in a pre-selected list of interest from a reference list  ...  We developed a general Bayesian framework to estimate cancer-specific and patient-specific networks and elicit patient-level pathway scores by integrating data arising from multiple platforms and incorporating  ... 
doi:10.1038/s41598-018-32682-x fatcat:4ililf34pjeujjm3wg7ldthv7q

Resources for integrative systems biology: from data through databases to networks and dynamic system models

Aylwin Ng, Borisas Bursteinas, Qiong Gao, Ewan Mollison, Marketa Zvelebil
2006 Briefings in Bioinformatics  
systems-level research.  ...  Recent developments in high-throughput methodologies enable the analysis of the transcriptome, proteome, interactome, metabolome and phenome on a previously unprecedented scale, thus contributing to the  ...  Acknowledgements The authors thank Anne Ridley and Buzz Baum for very helpful discussions, David Sims and Konstantinos Lykostratis for suggesting several useful websites for inclusion into the list of  ... 
doi:10.1093/bib/bbl036 pmid:17040977 fatcat:lfu4hga62zegdalxxvex3ugfju

Profiling cell signaling networks at single-cell resolution

Xiaokang Lun, Bernd Bodenmiller
2020 Molecular & Cellular Proteomics  
We also describe the available computational tools for network characterization using single-cell data and discuss potential confounding factors that need to be considered in single-cell signaling network  ...  Signaling networks process intra- and extracellular information to modulate the functions of a cell.  ...  Multiple computational methods are now available to account for cellcycle effects in single-cell transcriptomic data, mass cytometry-based phosphorylation network analysis, and microscopic imaging analysis  ... 
doi:10.1074/mcp.r119.001790 pmid:32132232 pmcid:PMC7196580 fatcat:v3u4d7ymuvcg7nvejnybv54equ

A semi-supervised Bayesian approach for simultaneous protein sub-cellular localisation assignment and novelty detection

Oliver M. Crook, Aikaterini Geladaki, Daniel J. H. Nightingale, Owen Vennard, Kathryn S. Lilley, Laurent Gatto, Paul D. W. Kirk, Rita Casadio
2020 PLoS Computational Biology  
Moreover, using sub-cellular proteomics data from Saccharomyces cerevisiae, we uncover a novel group of proteins trafficking from the ER to the early Golgi apparatus.  ...  Inference in our model is performed in a Bayesian framework, allowing us to quantify uncertainty in the allocation of proteins to new sub-cellular niches, as well as in the number of newly discovered compartments  ...  Breckels of the Cambridge Centre for Proteomics for critical reading of the manuscript.  ... 
doi:10.1371/journal.pcbi.1008288 pmid:33166281 fatcat:q64tbwutrbdypfliyaakpclhye

Computational strategies for single-cell multi-omics integration

Nigatu Adossa, Sofia Khan, Kalle T Rytkönen, Laura L Elo
2021 Computational and Structural Biotechnology Journal  
networks across cells and tissues.  ...  Finally, we explore the challenges and prospective future directions of single-cell multi-omics data integration, including examples of adopting multi-view analysis approaches used in other disciplines  ...  For instance, Dirichlet mixture model can be used to construct a context-dependent Bayesian clustering framework that can be used for clustering multiple omics datasets on the level of individual omics  ... 
doi:10.1016/j.csbj.2021.04.060 pmid:34025945 pmcid:PMC8114078 fatcat:qap257yttzdetjrqs4aijcwaq4

Reverse engineering biomolecular systems using -omic data: challenges, progress and opportunities

C. F. Quo, C. Kaddi, J. H. Phan, A. Zollanvari, M. Xu, M. D. Wang, G. Alterovitz
2012 Briefings in Bioinformatics  
'Data-driven' approaches, i.e. data mining, can be used to extract patterns from large volumes of biochemical data at molecular-level resolution while 'design-driven' approaches, i.e. systems modeling,  ...  However, there exist several challenges in this fast growing field of reverse engineering biomolecular systems: (i) to integrate heterogeneous biochemical data for data mining, (ii) to combine top^down  ...  This work was supported in part by grants from the National Institutes of Health Bioengineering Research Partnership R01CA108468, Center for Cancer Nanotechnology Excellence U54CA119338, and 1RC2CA148265  ... 
doi:10.1093/bib/bbs026 pmid:22833495 pmcid:PMC3404400 fatcat:rctmpuedxbcbxh6ipuloiy4kji

Integrating Heterogeneous omics Data via Statistical Inference and Learning Techniques

Ashar Ahmad, Holger Fröhlich
2016 Genomics and Computational Biology  
Different classes of algorithms are discussed for both application tasks. Existing and future challenges for data integration methods are pointed out.  ...  In the second part of our article we ask, in which way integrated omics data could be used for better personalized patient treatment in a supervised as well as unsupervised learning setting.  ...  The authors first looked for differentially expressed proteins at the proteome level.  ... 
doi:10.18547/gcb.2016.vol2.iss1.e32 fatcat:xmdsdhzdj5czvljgfhvwqlypbm

Review of Statistical Learning Methods in Integrated Omics Studies (An Integrated Information Science)

Irene Sui Lan Zeng, Thomas Lumley
2018 Bioinformatics and Biology Insights  
Acknowledgements The authors would like to express their gratitude to Ms Vivian Ward who helped to visualize the analytical processes for the review.  ...  Kamburov et al 36 presented a Web-based tool IMPaLA for joint pathway analysis of transcriptomic, proteomic, and metabolomic data from multiple data sets.  ...  Bayesian modeling provides the essential framework to incorporate known information in analysis.  ... 
doi:10.1177/1177932218759292 pmid:29497285 pmcid:PMC5824897 fatcat:nbknjl4qq5awrldy7natmg3h6y

Network biomarkers, interaction networks and dynamical network biomarkers in respiratory diseases

Xiaodan Wu, Luonan Chen, Xiangdong Wang
2014 Clinical and Translational Medicine  
Protein-based DNB will provide more information to define the differences between the normal and pre-disease stages, which might point to early diagnosis for patients.  ...  The present review headlined the definition, significance, research and potential application for network biomarkers, interaction networks and dynamical network biomarkers (DNB).  ...  The combination of physical and biological factors with a graphical Bayesian network framework was found Figure 2 Disease states and biomarkers.  ... 
doi:10.1186/2001-1326-3-16 pmid:24995123 pmcid:PMC4072888 fatcat:qxjchyqltndxhdsyf3rihr5ce4

From systems biology to P4 medicine: applications in respiratory medicine

Guillaume Noell, Rosa Faner, Alvar Agustí
2018 European Respiratory Review  
In any case, embracing a holistic scientific approach (as opposed to the reductionist research strategy used traditionally) for the understanding of human health and disease is a unique (and mandatory)  ...  It stems from advancements in medical diagnostics, "omics" data and bioinformatic computing power.  ...  Acknowledgements The authors thank the two anonymous reviewers of our manuscript for their very helpful and constructive criticisms and suggestions.  ... 
doi:10.1183/16000617.0110-2017 pmid:29436404 fatcat:77lqnvbhsfarthggrvmxe44q34

Statistical contributions to bioinformatics: Design, modelling, structure learning and integration

Jeffrey S. Morris, Veerabhadran Baladandayuthapani
2017 Statistical Modelling  
These technologiees yield highly structured big data, whose analysis poses significant quantitative challenges.  ...  Statisticians, with their deep understanding of variability and uncertainty quantification, play a key role in these efforts.  ...  et al. (2013) introduced integrative Bayesian analysis of genomics data (iBAG), a unified framework for integrating information across genomic, transcriptomic and epigenemic data as well clinical outcomes  ... 
doi:10.1177/1471082x17698255 pmid:29129969 pmcid:PMC5679480 fatcat:nhyi5e2nqrh4pdatrnfnt7h6p4
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