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DCI: Learning Causal Differences between Gene Regulatory Networks
[article]
2020
bioRxiv
pre-print
Preserved Fulltext
Here, we propose an algorithm that efficiently learns the differences in gene regulatory mechanisms between different conditions. ...
AbstractSummaryDesigning interventions to control gene regulation necessitates modeling a gene regulatory network by a causal graph. ...
We address this problem by noting that it is often of interest to learn changes in causal (regulatory) relationships between two related gene regulatory networks corresponding to different conditions, ...
doi:10.1101/2020.05.13.093765
fatcat:fljdkgeisjh3dfgvflw4zjyyw4
Direct Estimation of Differences in Causal Graphs
[article]
2018
arXiv
pre-print
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This is of interest for example in genomics, where changes in the structure or edge weights of the underlying causal graphs reflect alterations in the gene regulatory networks. ...
We here provide the first provably consistent method for directly estimating the differences in a pair of causal DAGs without separately learning two possibly large and dense DAG models and computing their ...
directly learn the difference of two causal networks without having to estimate each network separately. ...
arXiv:1802.05631v3
fatcat:ca74znz3yzad3dcr3wxsspntfu
Scalable Causal Structure Learning: New Opportunities in Biomedicine
[article]
2021
arXiv
pre-print
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This paper gives a practical tutorial on popular causal structure learning models with examples of real-world data to help healthcare audiences understand and apply them. ...
We review prominent traditional, score-based and machine-learning based schemes for causal structure discovery, study some of their performance over some benchmark datasets, and discuss some of the applications ...
It uses a method called Difference Causal Inference (DCI) and compares it to a difference-based GES as a baseline. ...
arXiv:2110.07785v1
fatcat:3dk2kfkvzjdqhazuenuhvg5f7e
The Multiple Dimensions of Networks in Cancer: A Perspective
2021
Symmetry
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From agent-based modelling and mechano-biology to machine learning and predictive modelling, the perspective motivates a methodology well suited to mathematical and computational oncology and suggests ...
It instigates the community to explore new paths and synergies under the umbrella of the Special Issue "Networks in Cancer: From Symmetry Breaking to Targeted Therapy". ...
This includes gene regulatory networks, metabolic networks, and proteinprotein interactions (PPIs). ...
doi:10.3390/sym13091559
fatcat:tdlrnpbml5e3tjgpbicegn3mtu
Neuroblastoma, a Paradigm for Big Data Science in Pediatric Oncology
2016
International Journal of Molecular Sciences
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Computational strategies derived from big data science-network-and machine learning-based modeling and drug repositioning-hold the promise of shedding new light on the molecular mechanisms driving neuroblastoma ...
Also, due to limited detected driver mutations, it is difficult to benchmark key genes for drug development. ...
MNI was recently applied to decipher causal genes responsible for driving tumor progression in ductal carcinoma in situ (DCIS), a non-invasive lesion of breast cancer. ...
doi:10.3390/ijms18010037
pmid:28035989
pmcid:PMC5297672
fatcat:lyf4za4drzhbranotgao7334bi
Repositioning chloroquine and metformin to eliminate cancer stem cell traits in pre-malignant lesions
2011
Drug resistance updates
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First, we describe a signalling network signature that overrides bioenergetics stress-and oncogene-induced senescence (OIS) phenomena in CSCs residing at pre-invasive lesions. ...
Second, we functionally map the anti-malarial chloroquine and the anti-diabetic metformin ("old drugs") to their recently recognized CSC targets ("new uses") within the network. ...
A causal relationship between autophagy and cell survival in DCIS lesions has been illustrated by the fact that ATG6/Beclin-1 (a haploinsufficient tumour suppressor protein that is essential for autophagy ...
doi:10.1016/j.drup.2011.04.003
pmid:21600837
pmcid:PMC3742095
fatcat:vnuda22t2fepdij6wzaraawnta
Integrated Information as a Measure of Cognitive Processes in Coupled Genetic Repressilators
2019
Entropy
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In parallel, drawn originally from studies on neuroscience, integrated information proposes an approach to quantify the balance between integration and differentiation in the causal dynamics among the ...
This conveys information processing from intracellular signaling networks enabling self-organization between other cells, typically involving mechanisms associated with cognitive systems. ...
This is useful to the general picture of gene activation in regulatory networks, in which every
gene switches between a high or low state in terms of the interplay of activation and inhibition of the
chemical ...
doi:10.3390/e21040382
pmid:33267096
fatcat:dhitbmuvmjcybg6m66axktoq5u
Reconstructing the functional effect of TP53 somatic mutations on its regulon using causal signalling network modelling
[article]
2022
bioRxiv
pre-print
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Thus, to infer the impact of different TP53 mutations we compared the topological characteristics of the optimized and reconstructed (upwards of twenty thousand) gene networks. ...
To address this, we first proceed with a machine learning (ML) approach, and then propose an integrated computational network modelling approach that reconstructs signalling networks using a comprehensive ...
gene network approach enabling causal inference to reconstruct TP53 signalling networks in cancer samples and cell lines harbouring different TP53 mutations. ...
doi:10.1101/2022.06.23.497293
fatcat:wn2eux2adnh67ehwm63bktcb4q
Computational Medicine: Translating Models to Clinical Care
2012
Science Translational Medicine
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Statistical learning is applied to high-dimensional biomolecular data to create models that describe relationships between molecules and networks. ...
For example, when separating two disease phenotypes on the basis of a gene expression profile, a decision rule that is based on a regulatory pattern involving a small number of interacting genes can provide ...
doi:10.1126/scitranslmed.3003528
pmid:23115356
pmcid:PMC3618897
fatcat:3pzsoz3iyzdpvmtwqw6x6nlvtm
A Longitudinal Study of the Association between Mammographic Density and Gene Expression in Normal Breast Tissue
2019
Journal of mammary gland biology and neoplasia
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We obtained biopsies from breasts of healthy women at two different time points several years apart and performed microarray gene expression analysis. ...
We validated findings from our previous study; negative correlation between RBL1 and MD in post-menopausal women, indicating involvement of the TGFβ pathway. ...
Availability of Data and Material Gene expression data for the samples from the previous study (MDG1) are deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number ...
doi:10.1007/s10911-018-09423-x
pmid:30613869
fatcat:stt5ysdfkjhzpgkrnbkwr2jhy4
Systems virology: host-directed approaches to viral pathogenesis and drug targeting
2013
Nature Reviews Microbiology
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In the past decade, tools have become available to chart a new course, one directed at obtaining comprehensive systems-level views of the host response and the interplay between virus and host. ...
Here, we provide a snapshot of the evolution of systems virology, from global gene expression profiling and signatures of disease outcome, to geometry-based computational methods that promise to yield ...
Josset for generating the networks in figure 3 , and M. Heise and M. Ferris for providing the data used in box 3. ...
doi:10.1038/nrmicro3036
pmid:23728212
pmcid:PMC4028060
fatcat:yk674zd7hzevladttnwmdgrjdi
Panoptic View of Prognostic Models for Personalized Breast Cancer Management
2019
Cancers
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This review summarizes the prognostic and predictive insights provided by commercially available gene expression-based tests and other multivariate or clinical -omics-based prognostic/predictive models ...
patient subgroups delineated by these clinicopathologic characteristics, as currently used methodologies in diagnostic pathology lack the capability to discern deeper genotypic and subtler phenotypic differences ...
DNA, RNA, proteomic, copy number variant, and other information can now be seamlessly woven together using sophisticated big data analytics to generate probabilistic causal networks, improving the chances ...
doi:10.3390/cancers11091325
pmid:31500225
pmcid:PMC6770520
fatcat:auwccqlugvevnliouxc3p7icvi
Zi Shen Wan Fang Attenuates Neuroinflammation and Cognitive Function Via Remodeling the Gut Microbiota in Diabetes-Induced Cognitive Impairment Mice
2022
Frontiers in Pharmacology
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The DCI mice were randomly divided into the model group (DCI), the low- and high-ZSWF–dose groups (9.63 g/kg, 18.72 g/kg), the mixed antibiotic group (ABs), and the ZSWF combined with mixed antibiotic ...
In addition, ZSWF reshaped the imbalanced gut microbiota in DCI mice by reversing the abundance changes of a wide range of intestinal bacteria at the phyla and genus levels. ...
Furthermore, an antibiotic intervention was used to assess the possibility of a causal relation between ZSWF improving cognitive and gut microbiota. ...
doi:10.3389/fphar.2022.898360
pmid:35910371
pmcid:PMC9335489
doaj:24922726c0db405c928a1c43de060706
fatcat:2xbisbzuxjbgflvlixkakzqtpy
The econometrics of Bayesian graphical models: a review with financial application
2016
The Journal of Network Theory in Finance
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This paper presents a state-of-the-art review on the interface between statistics and econometrics in the inference and application of Bayesian graphical models. ...
Recent advances in empirical finance have shown that the adoption of network theory is critical in order to understand contagion and systemic vulnerabilities. ...
Also, the rank difference is the difference between the rank on the GCnet and that on the BGnet. ...
doi:10.21314/jntf.2016.016
fatcat:csl76y3sc5ahbfsrqwmzjdzpsi
Proteome, Phosphoproteome, and Hydroxyproteome of Liver Mitochondria in Diabetic Rats at Early Pathogenic Stages
2009
Molecular & Cellular Proteomics
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During the transition to T2D, mitochondria undergo extensive changes in gene expression and arrive at a new highly ...
The onset of T2D results from complex interactions between genetic and environmental factors. ...
FIG. 4 . 4 A, spectral counts of different mitochondrial proteins between replicates (rep.) and between different samples. ...
doi:10.1074/mcp.m900020-mcp200
pmid:19700791
pmcid:PMC2808256
fatcat:dqwryx6y6feoxhw2w5j55ispfi
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