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Nested effects models for high-dimensional phenotyping screens
2007
Computer applications in the biosciences : CABIOS
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to the specific needs of large-scale and high-dimensional phenotyping screens. ...
Motivation: In high-dimensional phenotyping screens, a large number of cellular features is observed after perturbing genes by knockouts or RNA interference. ...
ACKNOWLEDGEMENTS We thank Edo Airoldi, Matthew Hibbs and Curtis Huttenhower (all LSI Princeton) for comments and helpful discussions. ...
doi:10.1093/bioinformatics/btm178
pmid:17646311
fatcat:zj7j3puxrzbqxdjthu22p77jee
Analyzing gene perturbation screens with nested effects models in R and bioconductor
2008
Bioinformatics
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Nested effects models (NEMs) are a class of probabilistic models introduced to analyze the effects of gene perturbation screens visible in high-dimensional phenotypes like microarrays or cell morphology ...
Our software implements several search algorithms for model fitting and is applicable to a wide range of different data types and representations. ...
Nested effects models (NEM) are a class of models introduced to analyze the effects of gene perturbation screens visible in high-dimensional phenotypes like microarrays or cell morphology. ...
doi:10.1093/bioinformatics/btn446
pmid:18718939
pmcid:PMC2732276
fatcat:ugxprnb665gsvjwa4x4msy4pvu
How to Understand the Cell by Breaking It: Network Analysis of Gene Perturbation Screens
2010
PLoS Computational Biology
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The first part of the review describes methods to analyze one- or low-dimensional phenotypes like viability or reporter activity; the second part concentrates on high-dimensional phenotypes showing global ...
Modern high-throughput gene perturbation screens are key technologies at the forefront of genetic research. ...
Acknowledgments I thank the organizers of the ISMB 2009 tutorial sessions for the opportunity to present this material. ...
doi:10.1371/journal.pcbi.1000655
pmid:20195495
pmcid:PMC2829042
fatcat:5xcvswgt45as3bjh6pmykrtlim
Computational identification of cellular networks and pathways
2007
Molecular Biosystems
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We discuss integrated analysis of microarray datasets, methods to combine heterogeneous data sources, the analysis of highdimensional phenotyping screens and describe efforts to establish a reliable and ...
unbiased gold standard for method comparison and evaluation. ...
to the specific needs of large-scale and high-dimensional phenotyping screens. ...
doi:10.1039/b617014p
pmid:17579773
fatcat:4vo2qkodsjhyflapkq7v3o23oy
Inferring modulators of genetic interactions with epistatic nested effects models
2017
PLoS Computational Biology
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We have extended the framework of Nested Effects Models (NEMs), a type of graphical model specifically tailored to analyze high-dimensional gene perturbation data, to incorporate logical functions that ...
We benchmark our approach in the controlled setting of a simulation study and show high accuracy in inferring the correct model. ...
Acknowledgments We thank Frank Holstege and Patrick Kemmeren for useful discussions. ...
doi:10.1371/journal.pcbi.1005496
pmid:28406896
pmcid:PMC5407847
fatcat:2j4nqaizbfeozaikidnxomv2e4
Improved pathway reconstruction from RNA interference screens by exploiting off-target effects
2018
Bioinformatics
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Another well characterized computational framework for inferring networks from high-dimensional RNAi screens are the nested effects models (NEMs) (Markowetz et al., 2005) . ...
Nested effects models (NEMs) are a class of probabilistic graphical models designed to reconstruct signalling pathways from high-dimensional observations resulting from perturbation experiments, such as ...
Our model overcomes the limitations of NEMs and general effects models by inferring DAGs from nested data. ...
doi:10.1093/bioinformatics/bty240
pmid:29950000
pmcid:PMC6022657
fatcat:xfhg4j5clzctnnuc2vdyppn6xu
Structure Learning in Nested Effects Models
2008
Statistical Applications in Genetics and Molecular Biology
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Nested Effects Models (NEMs) are a class of graphical models introduced to analyze the results of gene perturbation screens. ...
NEMs explore noisy subset relations between the high-dimensional outputs of phenotyping studies, e.g. the effects showing in gene expression profiles or as morphological features of the perturbed cell. ...
Acknowledgements AT would like to thank Olga Troyanskaya's lab in Princeton for the excellent hospitality during the preparation of this paper. ...
doi:10.2202/1544-6115.1332
pmid:18312214
fatcat:7taxe3ftfvgihjqkmvhoebwffi
Improved pathway reconstruction from RNA interference screens by exploiting off-target effects
[article]
2018
bioRxiv
pre-print
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Nested effects models (NEMs) are a class of probabilistic graphical models designed to reconstruct signalling pathways from high-dimensional observations resulting from perturbation experiments, such as ...
Here, we present an extension of NEMs called probabilistic combinatorial nested effects models (pc-NEMs), which capitalize on the ancillary siRNA off-target effects for network reconstruction from combinatorial ...
Our model overcomes the limitations of NEMs and general effects models by inferring DAGs from nested data. ...
doi:10.1101/258319
fatcat:bzbuecyhr5fudi3bhozru36z3a
A model-free approach for detecting interactions in genetic association studies
2013
Briefings in Bioinformatics
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In this study, we propose an efficient statistical procedure in a genetic model-free framework for detecting SNPs exhibiting main genetic effects as well as epistatic interactions. ...
Specifically, the association between phenotype and genotype is characterized by an unknown function to be estimated using nonparametric techniques, and a two-stage non-parametric independence screening ...
The authors acknowledge the investigators who contributed the phenotype, genotype and simulated data for this study. ...
doi:10.1093/bib/bbt082
pmid:24273216
pmcid:PMC4296135
fatcat:zckb7nofvfd57npnkdrt4abn6i
Transcription Factor Activity Mapping of a Tissue-Specific In Vivo Gene Regulatory Network
2015
Cell Systems
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effects modeling reveals information flow between transcription factors ...
network based on transcription factor activity d Promoters directly or indirectly regulated by a median of 18 transcription factors d Network contains cell-autonomous and non-autonomous regulation d Nested ...
ACKNOWLEDGMENTS We thank members of the A.J.M.W. laboratory and Sander van den Heuvel for discussions and critical reading of the manuscript. ...
doi:10.1016/j.cels.2015.08.003
pmid:26430702
pmcid:PMC4584425
fatcat:r7f2lxx2ango5llgytpjgiect4
Prediction of Treatment Outcome for Autism from Structure of the Brain Based On Sure Independence Screening
2019
2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)
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SIS is a theoretically and empirically validated method for ultra-high dimensional general linear models, and it achieves both predictive accuracy and correct feature selection by iterative feature selection ...
To select predictive features and build accurate models, we use the sure independence screening (SIS) method. ...
We introduce sure independence screening (SIS) [5] , a feature selection method for ultra-high dimensional general linear models. ...
doi:10.1109/isbi.2019.8759156
pmid:32256966
pmcid:PMC7119202
fatcat:xzweox2uinc3bg2v2qkba7t464
Noise reduction in genome-wide perturbation screens using linear mixed-effect models
2011
Computer applications in the biosciences : CABIOS
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with linear mixed-effects models. ...
Motivation: High-throughput perturbation screens measure the phenotypes of thousands of biological samples under various conditions. ...
Zheng for helpful discussions. ...
doi:10.1093/bioinformatics/btr359
pmid:21685046
pmcid:PMC3150043
fatcat:l65wr4mszneuvbyq3uvvsmn2yi
Prediction of treatment outcome for autism from structure of the brain based on sure independence screening
[article]
2019
arXiv
pre-print
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SIS is a theoretically and empirically validated method for ultra-high dimensional general linear models, and it achieves both predictive accuracy and correct feature selection by iterative feature selection ...
To select predictive features and build accurate models, we use the sure independence screening (SIS) method. ...
We introduce sure independence screening (SIS) [5] , a feature selection method for ultra-high dimensional general linear models. ...
arXiv:1810.07809v2
fatcat:gid67g6ekra5hd3qf47fphknc4
The Progress and Clinical Application of Breast Cancer Organoids
2020
International Journal of Stem Cells
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This model can not only study the occurrence and envolution of breast cancer, but is more prominent in clinical application. screening drugs by high-throughput, personalized treatment, textingtoxicity ...
As a techonlogy, obtaining patient-derived tumor cells, combined with three-dimensional culture technology, adding cytokines that promotes the proliferation of breast cancer stem cells and inhibit their ...
It acts as an effective model to study tumors and high-throughput screening drugs in vitro. ...
doi:10.15283/ijsc20082
pmid:32840232
pmcid:PMC7691857
fatcat:f7nsnsbqyfdx7b6lkax5eacs5u
Identifying rheumatoid arthritis susceptibility genes using high-dimensional methods
2009
BMC Proceedings
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We conclude that the three high-dimensional methods are useful as an initial screening for gene associations to identify promising genes for further modeling and additional replication studies. ...
The ability to screen the entire genome for association to complex diseases has great potential for identifying gene effects. ...
Thus, our study using real data demonstrates the ability of these high-dimensional screening methods to detect gene effects. ...
doi:10.1186/1753-6561-3-s7-s79
pmid:20018074
pmcid:PMC2795981
fatcat:mz7zysfyevayxjx4jsfvb3vpiy
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