Statistical and Machine Learning Approaches to Predict Gene Regulatory Networks From Transcriptome Datasets

Mochida, Keiichi; Koda, Satoru; Inoue, Komaki; Nishii, Ryuei

doi:10.3389/fpls.2018.01770

Cited by 63 publications

(52 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To predict the order of action of transcriptional regulators, there are several stand-alone methods which infer GRNs given processed data rather than raw sequence reads. These methods use time series and processed multi-omics data to establish which genes influence the expression of others downstream, providing information about the system dynamics (Mochida et al, 2018). Specifically, they address two main questions: 1) What genes are altered and exhibit dynamic expression changes?, 2) How are these genes within the GRN regulating each other?…”

Section: Current Stand-alone Grn Inference Methods Do Not Fully Levermentioning

confidence: 99%

TIMEOR: a web-based tool to uncover temporal regulatory mechanisms from multi-omics data

Conard

Goodman

et al. 2020

Preprint

View full text Add to dashboard Cite

SummaryUncovering how transcription factors (TFs) regulate their targets at the DNA, RNA and protein levels over time is critical to define gene regulatory networks (GRNs) in normal and diseased states. RNA-seq has become a standard method to measure gene regulation using an established set of analysis steps. However, none of the currently available pipeline methods for interpreting ordered genomic data (in time or space) use time series models to assign cause and effect relationships within GRNs, are adaptive to diverse experimental designs, or enable user interpretation through a web-based platform. Furthermore, methods which integrate ordered RNA-seq data with transcription factor binding data are urgently needed. Here, we present TIMEOR (Trajectory Inference and Mechanism Exploration with Omics data in R), the first web-based and adaptive time series multi-omics pipeline method which infers the relationship between gene regulatory events across time. TIMEOR addresses the critical need for methods to predict causal regulatory mechanism networks between TFs from time series multi-omics data. We used TIMEOR to identify a new link between insulin stimulation and the circadian rhythm cycle. TIMEOR is available at https://github.com/ashleymaeconard/TIMEOR.git.

show abstract

Section: Current Stand-alone Grn Inference Methods Do Not Fully Levermentioning

confidence: 99%

TIMEOR: a web-based tool to uncover temporal regulatory mechanisms from multi-omics data

Conard

Goodman

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The strategies to analyse time series discussed above are essentially statistical methods that aim at extracting patterns from time series without using prior knowledge. While machine learning and artificial intelligent approaches may be useful to detect patterns that humans might miss, and display remarkable successes in making predictions [84,40] it is highly challenging to extract useful information about underlying mechanisms from these models. Mechanistic models pursue the opposite approach.…”

Section: Mechanistic Modelsmentioning

confidence: 99%

From Sequence to Information

Popa

Oldenburg

Ebenhöh

2019

Preprint

View full text Add to dashboard Cite

Today massive amounts of sequenced metagenomic and -transcriptomic data from different ecological niches and environmental locations are available. Scientific progress depends critically on methods that allow extracting useful information from the various types of sequence data. Here, we will first discuss types of information contained in the various flavours of biological sequence data, and how this information can be interpreted to increase our scientific knowledge and understanding. We argue that a mechanistic understanding is required to consistently interpret experimental observations, and that this understanding is greatly facilitated by the generation and analysis of dynamic mathematical models. We conclude that, in order to construct mathematical models and to test mechanistic hypotheses, time-series data is of critical importance. We review diverse techniques to analyse time-series data and discuss various approaches by which time-series of biological sequence data was successfully used to derive and test mechanistic hypotheses. Analysing the bottlenecks of current strategies in the extraction of knowledge and understanding from data, we conclude that combined experimental and theoretical efforts should be implemented as early as possible during the planning phase of individual experiments and scientific research projects.

show abstract

“…Integrating sufficient regulatory information as a graph, GRNs are essential tools for elucidating gene functions, interpreting biological processes, and prioritizing candidate genes for molecular regulators and biomarkers in complex diseases and traits analyses (Marbach et al, 2012). While highthroughput sequencing and other post-genomics technologies enable statistical and machine learning methods to reconstruct GRN, inferring gene regulatory relationships between a set of TFs and a set of potential gene targets through gene expression data is still far from being resolved in bioinformatics (Mochida et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…With decades of efforts of inferring gene regulatory relationships from gene expression data, many machine learning and statistical methods have been proposed for reconstructing GRN (Mochida et al, 2018). Unsupervised methods dominate GRN inference.…”

Section: Introductionmentioning

confidence: 99%

Inductive Inference of Gene Regulatory Network Using Supervised and Semi-supervised Graph Neural Networks

Wang

et al. 2020

Preprint

View full text Add to dashboard Cite

Discovering gene regulatory relationships and reconstructing gene regulatory networks (GRN) based on gene expression data is a classical, long-standing computational challenge in bioinformatics. Computationally inferring a possible regulatory relationship between two genes can be formulated as a link prediction problem between two nodes in a graph. Graph neural network (GNN) provides an opportunity to construct GRN by integrating topological neighbor propagation through the whole gene network. We propose an end-to-end gene regulatory graph neural network (GRGNN) approach to reconstruct GRNs from scratch utilizing the gene expression data, in both a supervised and a semi-supervised framework. To get better inductive generalization capability, GRN inference is formulated as a graph classification problem, to distinguish whether a subgraph centered at two nodes contains the link between the two nodes. A linked pair between a transcription factor (TF) and a target gene, and their neighbors are labeled as a positive subgraph, while an unlinked TF and target gene pair and their neighbors are labeled as a negative subgraph. A GNN model is constructed with node features from both explicit gene expression and graph embedding. We demonstrate a noisy starting graph structure built from partial information, such as Pearson’s correlation coefficient and mutual information can help guide the GRN inference through an appropriate ensemble technique. Furthermore, a semi-supervised scheme is implemented to increase the quality of the classifier. When compared with established methods, GRGNN achieved state-of-the-art performance on the DREAM5 GRN inference benchmarks. GRGNN is publicly available at https://github.com/juexinwang/GRGNN.HighlightsWe present a novel formulation of graph classification in inferring gene regulatory relationships from gene expression and graph embedding.Our method leverages a powerful framework, gene regulatory graph neural network (GRGNN), which is flexible and powerful to ensemble statistical powers from a number of heuristic skeletons.Our results show GRGRNN outperforms previous supervised and unsupervised methods inductively on benchmarks.GRGNN can be interpreted and explained following the biological network motif hypothesis in gene regulatory networks.

show abstract

Statistical and Machine Learning Approaches to Predict Gene Regulatory Networks From Transcriptome Datasets

Cited by 63 publications

References 65 publications

TIMEOR: a web-based tool to uncover temporal regulatory mechanisms from multi-omics data

TIMEOR: a web-based tool to uncover temporal regulatory mechanisms from multi-omics data

From Sequence to Information

Inductive Inference of Gene Regulatory Network Using Supervised and Semi-supervised Graph Neural Networks

Contact Info

Product

Resources

About