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Inferring gene regulatory networks
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Ebook "Prokaryotic systems biology (Advances in experimental medicine and biology, Volume 883)" focuses on innovative experimental and computational approaches for charting interaction networks in bacterial species.
326p
duongthandue0501
28-02-2024
3
1
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The application of cell-specific construction of transcription regulatory networks (TRNs) to identify their master regulators (MRs) in EMP2 induced vascular proliferation disorders has been largely unexplored.
10p
vihagrid
30-01-2023
6
3
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Transcription factor (TF) binding to regulatory DNA sites is a key determinant of cell identity within multi-cellular organisms and has been studied extensively in relation to site affinity and chromatin modifications. There has been a strong focus on the inference of TF-gene regulatory networks and TF-TF physical interaction networks.
23p
vigalileogalilei
27-02-2022
15
1
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Diverse molecular alterations associated with smoking in normal and precursor lung cancer cells have been reported, yet their role in lung cancer etiology remains unclear. A prominent example is hypomethylation of the aryl hydrocarbon-receptor repressor (AHRR) locus, which is observed in blood and squamous epithelial cells of smokers, but not in lung cancer.
18p
vialfrednobel
29-01-2022
5
0
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SCANPY is a scalable toolkit for analyzing single-cell gene expression data. It includes methods for preprocessing, visualization, clustering, pseudotime and trajectory inference, differential expression testing, and simulation of gene regulatory networks.
5p
vialfrednobel
29-01-2022
6
0
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It has proven exceedingly difficult to ascertain rare copy number alterations (CNAs) that may have strong effects in individual tumors. We show that a regulatory network inferred from gene expression and gene copy number data of 768 human cancer cell lines can be used to quantify the impact of patient-specific CNAs on survival signature genes.
25p
viaristotle
29-01-2022
7
0
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We present a novel approach, the Local Edge Machine, for the inference of regulatory interactions directly from time-series gene expression data. We demonstrate its performance, robustness, and scalability on in silico datasets with varying behaviors, sizes, and degrees of complexity. Moreover, we demonstrate its ability to incorporate biological prior information and make informative predictions on a well-characterized in vivo system using data from budding yeast that have been synchronized in the cell cycle.
13p
viaristotle
29-01-2022
9
0
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MicroRNA regulation is fundamentally responsible for fine-tuning the whole gene network in human and has been implicated in most physiological and pathological conditions. Studying regulatory impact of microRNA on various cellular and disease processes has resulted in numerous computational tools that investigate microRNA-mRNA interactions through the prediction of static binding site highly dependent on sequence pairing.
11p
vitzuyu2711
29-09-2021
14
1
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Single-cell gene expression measurements offer opportunities in deriving mechanistic understanding of complex diseases, including cancer. However, due to the complex regulatory machinery of the cell, gene regulatory network (GRN) model inference based on such data still manifests significant uncertainty.
11p
visilicon2711
20-08-2021
6
1
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The filamentous fungus Fusarium graminearum causes devastating crop diseases and produces harmful mycotoxins worldwide. Understanding the complex F. graminearum transcriptional regulatory networks (TRNs) is vital for effective disease management
14p
vijeeni2711
24-07-2021
15
0
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Genes are regulated by various types of regulators and most of them are still unknown or unobserved. Current gene regulatory networks (GRNs) reverse engineering methods often neglect the unknown regulators and infer regulatory relationships in a local and sub-optimal manner.
14p
vijeeni2711
30-06-2021
14
1
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High-throughput transcriptomic datasets are often examined to discover new actors and regulators of a biological response. To this end, graphical interfaces have been developed and allow a broad range of users to conduct standard analyses from RNA-seq data, even with little programming experience.
15p
vilichoo2711
23-06-2021
10
1
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In any gene regulatory network (GRN), the complex interactions occurring amongst transcription factors and target genes can be either instantaneous or time-delayed. However, many existing modeling approaches currently applied for inferring GRNs are unable to represent both these interactions simultaneously.
22p
viwyoming2711
16-12-2020
14
1
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Gene regulatory network inference remains a challenging problem in systems biology despite the numerous approaches that have been proposed. When substantial knowledge on a gene regulatory network is already available, supervised network inference is appropriate.
14p
viwyoming2711
16-12-2020
10
1
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MicroRNAs (miRNAs) are a class of endogenous small regulatory RNAs. Identifications of the dys-regulated or perturbed miRNAs and their key target genes are important for understanding the regulatory networks associated with the studied cellular processes.
13p
vikentucky2711
26-11-2020
8
1
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Predication of gene regularity network (GRN) from expression data is a challenging task. There are many methods that have been developed to address this challenge ranging from supervised to unsupervised methods. Most promising methods are based on support vector machine (SVM).
7p
vikentucky2711
26-11-2020
12
1
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Decoding the temporal control of gene expression patterns is key to the understanding of the complex mechanisms that govern developmental decisions during heart development. High-throughput methods have been employed to systematically study the dynamic and coordinated nature of cardiac differentiation at the global level with multiple dimensions.
16p
vikentucky2711
26-11-2020
8
1
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In the last decade, a great number of methods for reconstructing gene regulatory networks from expression data have been proposed. However, very few tools and datasets allow to evaluate accurately and reproducibly those methods. Hence, we propose here a new tool, able to perform a systematic, yet fully reproducible, evaluation of transcriptional network inference methods.
15p
vikentucky2711
24-11-2020
8
1
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Inferring gene regulatory network (GRN) has been an important topic in Bioinformatics. Many computational methods infer the GRN from high-throughput expression data. Due to the presence of time delays in the regulatory relationships, High-Order Dynamic Bayesian Network (HO-DBN) is a good model of GRN.
28p
vioklahoma2711
19-11-2020
13
2
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Many algorithms have been developed to infer the topology of gene regulatory networks from gene expression data. These methods typically produce a ranking of links between genes with associated confidence scores, after which a certain threshold is chosen to produce the inferred topology.
16p
vioklahoma2711
19-11-2020
16
1
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