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From heterogeneous healthcare data to disease-specific biomarker networks: A hierarchical Bayesian network approach
2021
PLoS Computational Biology
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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
2020
Wiley Interdisciplinary Reviews: Systems Biology and Medicine
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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
2016
Journal of Biomedical Informatics
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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]
2019
arXiv
pre-print
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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
2020
Barcelona Respiratory Network
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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
2019
Frontiers in Genetics
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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
2016
EURASIP Journal on Bioinformatics and Systems Biology
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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
2018
European Respiratory Review
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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
2012
PLoS ONE
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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
2017
Sensors
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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
2018
Frontiers in Neuroscience
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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]
2020
arXiv
pre-print
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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
2016
2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
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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
2019
Angiology
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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]
2020
bioRxiv
pre-print
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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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