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From heterogeneous healthcare data to disease-specific biomarker networks: A hierarchical Bayesian network approach

Ann-Kristin Becker, Marcus Dörr, Stephan B. Felix, Fabian Frost, Hans J. Grabe, Markus M. Lerch, Matthias Nauck, Uwe Völker, Henry Völzke, Lars Kaderali, Alison Marsden
2021 PLoS Computational Biology  
In this work, we introduce an entirely data-driven and automated approach to reveal disease-associated biomarker and risk factor networks from heterogeneous and high-dimensional healthcare data.  ...  We present an optimization algorithm for the adaptive refinement of such group Bayesian networks to account for a specific target variable, like a disease.  ...  In this work, we introduced a novel algorithm to infer Bayesian biomarker and risk factor networks from heterogeneous and high-dimensional healthcare data.  ... 
doi:10.1371/journal.pcbi.1008735 pmid:33577591 fatcat:u32rw5bvcjg2pkdueyvrhf4lki

Molecular networks in Network Medicine: Development and applications

Edwin K Silverman, Harald H H W Schmidt, Eleni Anastasiadou, Lucia Altucci, Marco Angelini, Lina Badimon, Jean-Luc Balligand, Giuditta Benincasa, Giovambattista Capasso, Federica Conte, Antonella Di Costanzo, Lorenzo Farina (+14 others)
2020 Wiley Interdisciplinary Reviews: Systems Biology and Medicine  
Network Medicine applies network science approaches to investigate disease pathogenesis.  ...  Network Medicine applies these integrated approaches to Omics Big Data (including genetics, epigenetics, transcriptomics, metabolomics, and proteomics) using computational biology tools and, thereby, has  ...  | Bayesian networks Bayesian networks are powerful models whose structure is determined directly from data that measure the values of variables across a series of samples, conditions, or states.  ... 
doi:10.1002/wsbm.1489 pmid:32307915 fatcat:ps5v2yzn3ncidcf7avay6f5xli

Network inference from multimodal data: A review of approaches from infectious disease transmission

Bisakha Ray, Elodie Ghedin, Rumi Chunara
2016 Journal of Biomedical Informatics  
The purpose of this review is to provide an in-depth analysis of multimodal infectious disease transmission network inference methods with a specific focus on Bayesian inference.  ...  Multi-domain data can now be used to improve the robustness and reliability of recovered networks from unimodal data.  ...  After a full-text review to exclude these and focus on methodological approaches, 8 articles resulted which use Bayesian inference to recover transmission networks from multimodal data for infectious diseases  ... 
doi:10.1016/j.jbi.2016.09.004 pmid:27612975 pmcid:PMC7106161 fatcat:tpsn75h7lnf3td6xplygemx3ca

Network Medicine in the age of biomedical big data [article]

Abhijeet R. Sonawane, Scott T. Weiss, Kimberly Glass, Amitabh Sharma
2019 arXiv   pre-print
More importantly, we delve into ways in which the network medicine approach, aided by phenotype-specific biomedical data, can be gainfully applied.  ...  Marrying the two, network medicine with biomedical data provides a framework to build meaningful models and extract impactful results at a network level.  ...  (a) Identification of disease associated network components within the interactome, (b) Co-expression based network modeling to identify disease biomarkers, (c) Constructing phenotype-specific GRNs to  ... 
arXiv:1903.05449v1 fatcat:7v4pvoxezjd55ex5ei2jbteswm

Artificial Intelligence in COPD: New Venues to Study a Complex Disease

Raúl San José Estépar
2020 Barcelona Respiratory Network  
Chronic obstructive pulmonary disease (COPD) is a complex and heterogeneous disease that can benefit from novel approaches to understanding its evolution and divergent trajectories.  ...  Artificial intelligence (AI) has revolutionized how we can use clinical, imaging, and molecular data to understand and model complex systems.  ...  The COPD community can benefit from this frenetic activity as novel approaches to redefine disease from rich datasets are proposed. Better phenotypes are essential to grasp disease heterogeneity.  ... 
doi:10.23866/brnrev:2019-0014 pmid:33521399 pmcid:PMC7842269 fatcat:f4vtut2ckvfxvnn7uvcamwhy5a

Network Medicine in the Age of Biomedical Big Data

Abhijeet R. Sonawane, Scott T. Weiss, Kimberly Glass, Amitabh Sharma
2019 Frontiers in Genetics  
More importantly, we delve into ways in which the network medicine approach, aided by phenotype-specific biomedical data, can be gainfully applied.  ...  Marrying the two, network medicine with biomedical data provides a framework to build meaningful models and extract impactful results at a network level.  ...  ACKNOWLEDGMENTS AS would like to thank John Quackenbush for inspiration of the three paradigms discussed above, along with Trevor R.  ... 
doi:10.3389/fgene.2019.00294 pmid:31031797 pmcid:PMC6470635 fatcat:qro34cgpuzg6bj72h23yjdylm4

Biomedical informatics with optimization and machine learning

Shuai Huang, Jiayu Zhou, Zhangyang Wang, Qing Ling, Yang Shen
2016 EURASIP Journal on Bioinformatics and Systems Biology  
Fast-growing biomedical and healthcare data have encompassed multiple scales ranging from molecules, individuals, to populations and have connected various entities in healthcare systems (providers, pharma  ...  There is thus a compelling demand for novel algorithms, including machine learning, data mining, and optimization that specifically tackle the unique challenges associated with the biomedical and healthcare  ...  Acknowledgements This special issue would not have been possible without the excellent people we are fortunate to work with.  ... 
doi:10.1186/s13637-017-0058-0 pmid:28246526 pmcid:PMC5306246 fatcat:dv7fc6lx5vb5rn2c5wlb72ygle

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.  ...  [55, 56]; hierarchical bottom-up [57]; hierarchical top-down (divisive analysis clustering (DIANA)) [58] Coexpression networks From the dataset builds a correlation network to identify groups  ... 
doi:10.1183/16000617.0110-2017 pmid:29436404 fatcat:77lqnvbhsfarthggrvmxe44q34

Cell Cycle Gene Networks Are Associated with Melanoma Prognosis

Li Wang, Daniel G. Hurley, Wendy Watkins, Hiromitsu Araki, Yoshinori Tamada, Anita Muthukaruppan, Louis Ranjard, Eliane Derkac, Seiya Imoto, Satoru Miyano, Edmund J. Crampin, Cristin G. Print (+1 others)
2012 PLoS ONE  
Analysis of this data using unsupervised hierarchical clustering and Bayesian gene networks identified proliferation-association RNA clusters, which were co-ordinately expressed across the A375 cells and  ...  The gene expression modules identified here, and the RNAs predicted by Bayesian network inference to be upstream of these modules, are potential prognostic biomarkers and drug targets.  ...  Acknowledgments We wish to thank Mr. Liam Williams for microarray hybridisation and GNI Ltd for access to their Dharmacon siRNA reagent library.  ... 
doi:10.1371/journal.pone.0034247 pmid:22536322 pmcid:PMC3335030 fatcat:rzon42nkezethjdm4jzy57oj5y

Context Relevant Prediction Model for COPD Domain Using Bayesian Belief Network

Hamid Mcheick, Lokman Saleh, Hicham Ajami, Hafedh Mili
2017 Sensors  
In the last three decades, researchers have examined extensively how context-aware systems can assist people, specifically those suffering from incurable diseases, to help them cope with their medical  ...  In this paper, we focus on the Bayesian belief network technique to select relevant attributes and use it to predict e exacerbation that suffers from the uncertainty in COPD area.  ...  To avoid these challenges, the creation of Bayesian network based on data can facilitate and speed up the construction of that hierarchical grid.  ... 
doi:10.3390/s17071486 pmid:28644419 pmcid:PMC5539774 fatcat:5yfvmltv3nhnzni2c3i2mrtk7m

Taxonomy Meets Neurology, the Case of Amyotrophic Lateral Sclerosis

Giovanna Morello, Antonio Gianmaria Spampinato, Francesca Luisa Conforti, Sebastiano Cavallaro
2018 Frontiers in Neuroscience  
Recent landmark publications from our research group outline a transformative approach to defining, studying and treating amyotrophic lateral sclerosis (ALS).  ...  Rather than approaching ALS as a single entity, we advocate targeting therapies to distinct "clusters" of patients based on their specific genomic and molecular features.  ...  However, in the multi-dimensional interaction scenario of a complex human disease, more advanced computational methods (i.e., machine learning approach, network-based methods and Bayesian analysis) are  ... 
doi:10.3389/fnins.2018.00673 pmid:30319346 pmcid:PMC6168652 fatcat:tvojawstmjde5ajdfqffgsuwui

Multi-layer Trajectory Clustering: A Network Algorithm for Disease Subtyping [article]

Sanjukta Krishnagopal
2020 arXiv   pre-print
This work present a novel, data-driven, network-based Trajectory Clustering (TC) algorithm for identifying Parkinson's subtypes based on disease trajectory.  ...  This generalizable and robust method can easily be extended to other progressive multi-variate disease datasets, and can effectively assist in targeted subtype-specific treatment in the field of personalized  ...  Acknowledgments The author wishes to sincerely thank Prof. Michelle Girvan, Dr. Lisa M. Shulman, MD and Dr. Rainer von Coelln, MD for helpful discussions and suggestions.  ... 
arXiv:2005.14472v2 fatcat:7yj5lvual5cvljjskdgw6kaomy

Inference and differential analysis of Extended Core Networks: A way to study anti-sense regulation

Marc Legeay, Beatrice Duval, Jean-Pierre Renou
2016 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)  
His main research is focused on clinical bioinformatics, disease-specific biomarkers, lung chronic diseases, cancer immunology, and molecular & cellular therapies.  ...  Recent development in mapping technologies for chromatin interactions has led to a rapid increase in this kind of interaction data, revealing a hierarchical organization of the 3D genome, from large compartments  ... 
doi:10.1109/bibm.2016.7822532 dblp:conf/bibm/LegeayDR16 fatcat:k33tacfd25flplfykeqfwkmzg4

Direct Anticoagulants and Risk of Myocardial Infarction, a Multiple Treatment Network Meta-Analysis

Péter Kupó, Zsolt Szakács, Margit Solymár, Tamás Habon, László Czopf, Lidia Hategan, Beáta Csányi, János Borbás, Annamária Tringer, Gábor Varga, Márta Balaskó, Róbert Sepp (+3 others)
2019 Angiology  
A search of the medical literature was performed from inception until May 31, 2019.  ...  Rivaroxaban was associated with a 21% reduction in the relative risk of MI when compared to placebo (relative risk [RR]: 0.79 [95% credible interval, CrI: 0.65-0.94]) and a 31% reduction (RR: 0.70 [95%  ...  The risk of MI was analyzed in a hierarchical Bayesian mixed-treatment comparison meta-analysis.  ... 
doi:10.1177/0003319719874255 pmid:31533437 fatcat:eepg4p3jvnf73pnt3qzsjnndou

Unsupervised cellular phenotypic hierarchy enables spatial intratumor heterogeneity characterization, recurrence-associated microdomains discovery, and harnesses network biology from hyperplexed in-situ fluorescence images of colorectal carcinoma [article]

Samantha Furman, Andrew Stern, Shikhar Uttam, D. Lansing Taylor, Filippo Pullara, S. Chakra Chennubhotla
2020 bioRxiv   pre-print
The LEAPH framework, when combined with microdomain discovery and microdomain-specific network biology, has the potential to provide insights into pathophysiological mechanisms, identify novel drug targets  ...  We applied LEAPH to hyperplexed (51 biomarkers) immunofluorescence images of colorectal carcinoma primary tumors (N=213).  ...  To integrate data from batch processing each biomarker is normalized to a control median.  ... 
doi:10.1101/2020.10.02.322529 fatcat:2hntj3rfavbxxky6wz7zj4q6ce
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