Filters








6,181 Hits in 5.8 sec

Nested effects models for high-dimensional phenotyping screens

Florian Markowetz, Dennis Kostka, Olga G. Troyanskaya, Rainer Spang
2007 Computer applications in the biosciences : CABIOS  
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

H. Frohlich, T. Beissbarth, A. Tresch, D. Kostka, J. Jacob, R. Spang, F. Markowetz
2008 Bioinformatics  
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

Florian Markowetz, Fran Lewitter
2010 PLoS Computational Biology  
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

Florian Markowetz, Olga G. Troyanskaya
2007 Molecular Biosystems  
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

Martin Pirkl, Madeline Diekmann, Marlies van der Wees, Niko Beerenwinkel, Holger Fröhlich, Florian Markowetz, Quaid Morris
2017 PLoS Computational Biology  
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

Sumana Srivatsa, Jack Kuipers, Fabian Schmich, Simone Eicher, Mario Emmenlauer, Christoph Dehio, Niko Beerenwinkel
2018 Bioinformatics  
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

Achim Tresch, Florian Markowetz
2008 Statistical Applications in Genetics and Molecular Biology  
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]

Sumana Srivatsa, Jack Kuipers, Fabian Schmich, Simone Eicher, Mario Emmenlauer, Christoph Dehio, Niko Beerenwinkel
2018 bioRxiv   pre-print
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

J. Li, J. Dan, C. Li, R. Wu
2013 Briefings in Bioinformatics  
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

Lesley T. MacNeil, Carles Pons, H. Efsun Arda, Gabrielle E. Giese, Chad L. Myers, Albertha J.M. Walhout
2015 Cell Systems  
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

Juntang Zhuang, Nicha C. Dvornek, Qingyu Zhao, Xiaoxiao Li, Pamela Ventola, James S. Duncan
2019 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)  
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

Danni Yu, John Danku, Ivan Baxter, Sungjin Kim, Olena K. Vatamaniuk, David E. Salt, Olga Vitek
2011 Computer applications in the biosciences : CABIOS  
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]

Juntang Zhuang, Nicha C. Dvornek, Qingyu Zhao, Xiaoxiao Li, Pamela Ventola, James S. Duncan
2019 arXiv   pre-print
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

Jin Yu, Wei Huang
2020 International Journal of Stem Cells  
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

Xueying Liang, Ying Gao, Tram K Lam, Qizhai Li, Cathy Falk, Xiaohong R Yang, Alisa M Goldstein, Lynn R Goldin
2009 BMC Proceedings  
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
« Previous Showing results 1 — 15 out of 6,181 results