作者: Tidious 時間: 2025-3-21 21:24
https://doi.org/10.1007/978-3-642-51994-9up-Lasso penalty in order to select interactions which operate both at the proteomic and at the transcriptomic level between two genes. We end up with a . network embedding information shared at multiple scales of the cell. We illustrate this method on two breast cancer data sets. An .-package is pu作者: 攝取 時間: 2025-3-22 02:19
Fols?ure und Ursodesoxychols?ureby combining gene expression data with protein–protein interaction networks and proteomic datasets. We conclude with a section on practical applications of a network inference algorithm to infer a genome-scale regulatory network.作者: 束以馬具 時間: 2025-3-22 06:58 作者: chemical-peel 時間: 2025-3-22 12:32 作者: Middle-Ear 時間: 2025-3-22 13:39 作者: Middle-Ear 時間: 2025-3-22 20:43 作者: ICLE 時間: 2025-3-22 22:28 作者: 現(xiàn)暈光 時間: 2025-3-23 01:29 作者: Pericarditis 時間: 2025-3-23 07:36
Integrative Approaches for Inference of Genome-Scale Gene Regulatory Networks,by combining gene expression data with protein–protein interaction networks and proteomic datasets. We conclude with a section on practical applications of a network inference algorithm to infer a genome-scale regulatory network.作者: DEAF 時間: 2025-3-23 12:56
Tree-Based Learning of Regulatory Network Topologies and Dynamics with Jump3,topology with a nonparametric method based on decision trees. We briefly review the theoretical and algorithmic foundations of Jump3, and then proceed to provide a step-by-step tutorial of the associated software usage.作者: bizarre 時間: 2025-3-23 14:31
Inferring Gene Regulatory Networks from Multiple Datasets,ods provide a comprehensive and flexible platform for inference from a diverse range of data, with applications in systems and synthetic biology, as well as spatiotemporal modelling of embryo development. In this chapter we provide an overview of GPDS approaches and highlight their applications in t作者: 內(nèi)向者 時間: 2025-3-23 19:02 作者: mettlesome 時間: 2025-3-23 22:21
Stability in GRN Inference, ground truth is available to compute a direct measure of the similarity between the inferred structure and the true network. The main ingredient here is a suite of indicators, called NetSI, providing statistics of distances between graphs generated by a given algorithm fed with different data subse作者: 不足的東西 時間: 2025-3-24 05:52 作者: Preamble 時間: 2025-3-24 09:24
Aus Wirthschaft und Wissenschaft,etworks (DBNs). We discuss the relationship of DBNs to models based on ordinary differential equations, and consider extensions to nonlinear time dynamics. We provide an introduction to time-varying DBN models, which allow for changes to the network structure and parameters over time. We also discus作者: 碎石頭 時間: 2025-3-24 14:36 作者: 排他 時間: 2025-3-24 16:22
https://doi.org/10.1007/978-3-642-51832-4t developments in speed and accuracy have enabled whole-transcriptome causal network inference on a personal computer. Here, we demonstrate this technique with program Findr on 3000 genes from the Geuvadis dataset. Subsequent analysis reveals major hub genes in the reconstructed network.作者: Nucleate 時間: 2025-3-24 20:56
https://doi.org/10.1007/978-3-476-05024-3 data sets. Indeed, biological entities are not isolated but are components of complex multilevel systems. We go one step further and advocate for the consideration of causal representations of the interactions in living systems. We present the causal formalism and bring it out in the context of bio作者: 船員 時間: 2025-3-25 00:26
https://doi.org/10.1007/978-3-642-51994-9a sets measured from diverse technologies all related to the same set of variables and individuals. This situation is becoming more and more common in molecular biology, for instance, when both proteomic and transcriptomic data related to the same set of “genes” are available on a given cohort of pa作者: 上腭 時間: 2025-3-25 05:07
Fols?ure und Ursodesoxychols?ure our ability to profile the different types of molecular components of cells under different conditions, we are now uniquely positioned to infer regulatory networks in diverse biological contexts such as different cell types, tissues, and time points. In this chapter, we cover two main classes of co作者: 散開 時間: 2025-3-25 09:59
Galenik der Mesalazine — Klinische Relevanz?based on decision trees that have been developed for the inference of gene regulatory networks (GRNs). Decision trees have indeed several nice properties that make them well-suited for tackling this problem: they are able to detect multivariate interacting effects between variables, are non-parametr作者: critic 時間: 2025-3-25 11:57 作者: 繁重 時間: 2025-3-25 16:26 作者: 貧困 時間: 2025-3-25 21:50 作者: 具體 時間: 2025-3-26 01:17
https://doi.org/10.1007/978-3-642-85915-1ent algorithms have been proposed to try to solve this issue, but it has been shown that different methods have some particular biases and strengths, and none of them is the best across all types of data and datasets. As a result, the idea of aggregating various network inferences through a consensu作者: Myocyte 時間: 2025-3-26 07:48
E. Lütjen-Drecoll,P. Steuhl,W. H. Arnoldf coregulated genes and their upstream regulatory programs from genome-wide gene expression and other omics data. Here, we review the basic theory of module network inference, present protocols for common gene regulatory network reconstruction scenarios based on the Lemon-Tree software, and show, us作者: 新星 時間: 2025-3-26 09:46
Conference proceedings 1987Latest editionrs. Despite an overwhelming number of algorithms proposed to solve the network inference problem either in the general scenario or in an ad-hoc tailored situation, assessing the stability of reconstruction is still an uncharted territory and exploratory studies mainly tackled theoretical aspects. We作者: Allure 時間: 2025-3-26 13:31 作者: 字的誤用 時間: 2025-3-26 16:51
https://doi.org/10.1007/978-3-662-63596-4 from experimental data, such mechanistic models can provide accurate predictions about the behavior of latent variables or the process under new experimental conditions. Complementarily, inference of model structure can be used to identify the most plausible model structure from a set of candidates作者: 孤僻 時間: 2025-3-26 21:15 作者: 馬籠頭 時間: 2025-3-27 05:09 作者: 嘲弄 時間: 2025-3-27 09:08
https://doi.org/10.1007/978-1-4939-8882-2Bayesian networks; Gaussian processes; data simulation; time series expression; single-cell transcriptom作者: jettison 時間: 2025-3-27 09:31
https://doi.org/10.1007/978-3-642-51832-4t developments in speed and accuracy have enabled whole-transcriptome causal network inference on a personal computer. Here, we demonstrate this technique with program Findr on 3000 genes from the Geuvadis dataset. Subsequent analysis reveals major hub genes in the reconstructed network.作者: TEN 時間: 2025-3-27 16:22 作者: 大吃大喝 時間: 2025-3-27 18:47 作者: 令人悲傷 時間: 2025-3-27 22:42 作者: parallelism 時間: 2025-3-28 05:00
Learning Differential Module Networks Across Multiple Experimental Conditions,module network inference, present protocols for common gene regulatory network reconstruction scenarios based on the Lemon-Tree software, and show, using human gene expression data, how the software can also be applied to learn differential module networks across multiple experimental conditions.作者: 較早 時間: 2025-3-28 08:10 作者: 壟斷 時間: 2025-3-28 12:56 作者: 裹住 時間: 2025-3-28 18:21 作者: legacy 時間: 2025-3-28 19:35
E. Lütjen-Drecoll,P. Steuhl,W. H. Arnoldmodule network inference, present protocols for common gene regulatory network reconstruction scenarios based on the Lemon-Tree software, and show, using human gene expression data, how the software can also be applied to learn differential module networks across multiple experimental conditions.作者: LAIR 時間: 2025-3-29 02:16
https://doi.org/10.1007/978-3-662-63874-3n of deterministic and stochastic frameworks, and the quantitative modelling of regulation. We particularly focus on the use of such models for the simulation of expression data that can serve as a benchmark for the testing of network inference algorithms.作者: bypass 時間: 2025-3-29 06:02
https://doi.org/10.1007/978-3-662-63596-4f such large-scale models, most algorithms require intractably high computation times. This chapter provides an overview of the state-of-the-art methods for parameter and model inference, with an emphasis on scalability.作者: 挖掘 時間: 2025-3-29 11:02 作者: 捕鯨魚叉 時間: 2025-3-29 13:05 作者: PAEAN 時間: 2025-3-29 17:57 作者: TEN 時間: 2025-3-29 22:41
,Aus der forstlichen Ger?thekammer,actical applications with pointers to publicly available software implementations are included. The chapter concludes with a comprehensive comparative benchmark study on simulated data and a real-work application taken from the current plant systems biology.作者: 神化怪物 時間: 2025-3-30 01:27 作者: 思想上升 時間: 2025-3-30 07:03 作者: Phagocytes 時間: 2025-3-30 10:36
Overview and Evaluation of Recent Methods for Statistical Inference of Gene Regulatory Networks froactical applications with pointers to publicly available software implementations are included. The chapter concludes with a comprehensive comparative benchmark study on simulated data and a real-work application taken from the current plant systems biology.作者: A簡潔的 時間: 2025-3-30 14:44 作者: 發(fā)怨言 時間: 2025-3-30 17:34
,Aus der forstlichen Ger?thekammer,ide an introduction to the basic concepts underpinning network inference tools, attempting a categorization which highlights commonalities and relative strengths. While the chapter is meant to be self-contained, the material presented should provide a useful background to the later, more specialized chapters of this book.作者: limber 時間: 2025-3-30 22:23 作者: 有惡臭 時間: 2025-3-31 02:26
Gene Regulatory Network Inference: An Introductory Survey,ide an introduction to the basic concepts underpinning network inference tools, attempting a categorization which highlights commonalities and relative strengths. While the chapter is meant to be self-contained, the material presented should provide a useful background to the later, more specialized chapters of this book.作者: 模范 時間: 2025-3-31 05:04
Causal Queries from Observational Data in Biological Systems via Bayesian Networks: An Empirical Stlogical networks, when the data is observational. We also discuss its ability to decipher the causal information flow as observed in gene expression. We also illustrate our exploration by experiments on small simulated networks as well as on a real biological data set.作者: Jargon 時間: 2025-3-31 12:26
Gene Regulatory Network Inference: An Introductory Survey,he late 1990s, reconstructing the structure of such networks has been a central computational problem in systems biology. While the problem is certainly not solved in its entirety, considerable progress has been made in the last two decades, with mature tools now available. This chapter aims to prov
