Article
Biochemical Research Methods
Deniz Secilmis, Thomas Hillerton, Sven Nelander, Erik L. L. Sonnhammer
Summary: The study introduces an algorithm called IDEMAX to infer effective perturbation design from gene expression data, improving the accuracy of GRN inference in real data where noise often hides much of the signal.
Article
Oncology
Yanglan Gan, Xin Hu, Guobing Zou, Cairong Yan, Guangwei Xu
Summary: The article introduces a new computational algorithm BiRGRN for inferring gene regulatory networks (GRNs) from time-series single-cell RNA-seq data. The algorithm utilizes a bidirectional recurrent neural network to transform the inference of GRNs into a regression problem, and adopts bidirectional structure and prior knowledge filtering strategy to improve accuracy and stability.
FRONTIERS IN ONCOLOGY
(2022)
Article
Biochemical Research Methods
Yanping Zeng, Yongxin He, Ruiqing Zheng, Min Li
Summary: Gene regulatory network plays a crucial role in controlling biological processes. Deciphering complex gene regulatory networks remains challenging. Recent advances in single-cell RNA sequencing enable computational inference of cell-specific gene regulatory networks. Normi is a novel gene regulatory network inference method that addresses challenges of pseudo-time information and dropout data. Normi outperforms other methods and identifies key regulators and crucial biological processes.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Jonathan Tyler, Daniel Forger, Jae Kyoung Kim
Summary: The study developed a model-based inference method that successfully inferred positive and negative regulations within various oscillatory networks, outperforming popular inference methods.
Article
Biochemical Research Methods
Xiaohan Jiang, Xiujun Zhang
Summary: This study developed a new technique called RSNET for inferring gene regulatory networks. Experimental results showed that RSNET outperformed other methods in terms of sensitivity and accuracy. This research provides a useful tool for inferring clean networks.
BMC BIOINFORMATICS
(2022)
Article
Biochemical Research Methods
Jie Xu, Guanxue Yang, Guohai Liu, Hui Liu
Summary: Using gene expression data to infer gene regulatory networks is important, but reconstructing large-scale networks poses challenges due to high dimensionality and external noise. In this study, we propose a novel algorithm called ensemble path consistency algorithm based on conditional mutual information (EPCACMI), which dynamically adjusts the mutual information threshold. By decomposing the network into subnetworks using principal component analysis and removing unrelated nodes, we can infer the relationships among selected nodes and integrate the subnetworks to form the complete network structure. Compared to other algorithms, EPCACMI is more effective and robust for inferring gene regulatory networks with more nodes.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Biology
Yanglan Gan, Yongchang Xin, Xin Hu, Guobing Zou
Summary: Gene regulatory network models the interactions between transcription factors and target genes, and is crucial for understanding gene function. iMPRN, a computational method integrating multiple prior networks, can accurately infer and optimize regulatory networks leading to key insights into gene regulation.
COMPUTATIONAL BIOLOGY AND CHEMISTRY
(2021)
Article
Biochemistry & Molecular Biology
Madison Dautle, Shaoqiang Zhang, Yong Chen
Summary: scTIGER is a novel deep-learning-based method that uses the co-differential relationships of gene expression profiles in paired scRNA-seq datasets to infer gene regulatory networks (GRNs). It has been successfully applied to prostate cancer cells and neurons to identify dynamic regulatory networks and demonstrates robustness against dropout noise in scRNA-seq data.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2023)
Article
Biotechnology & Applied Microbiology
Guangyi Chen, Zhi-Ping Liu
Summary: Gene regulatory network provides valuable information for demonstrating pathology, predicting clinical outcomes, and identifying drug targets. However, existing machine learning methods lack interpretability in inferring gene regulatory networks. This article introduces a method that combines grey theory with an adaptive sliding window technique to capture gene interactions and transform them into causal relationships.
FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY
(2022)
Article
Biochemical Research Methods
Softya Sebastian, Swarup Roy, Jugal Kalita
Summary: Inference of large-scale gene regulatory networks is crucial for understanding gene interactions. Existing methods are limited to small networks, so parallel computing is proposed to construct large networks. A generic parallel framework is proposed which can infer large networks without re-engineering existing methods, and has been tested on various inference methods with good results. Finally, a gene network associated with Alzheimer's Disease was successfully inferred using the framework, revealing hub genes related to the disease.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Biochemical Research Methods
Softya Sebastian, Swarup Roy, Jugal Kalita
Summary: The inference of large-scale gene regulatory networks is important for understanding interactions among genes. Existing methods can only reconstruct limited networks, so parallel computing paradigms are necessary. We propose a generic parallel framework that allows any existing method to infer large networks in parallel without compromising quality. We test the framework on 15 inference methods using benchmarks and real-world expression matrices, and it successfully constructs a large network related to Alzheimer's Disease.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Johannes Hettich, J. Christof M. Gebhardt
Summary: This article introduces a method to simulate and infer gene regulatory networks (GRNs) at molecular detail. The method can approximate the solution of the deterministic differential equations associated with GRN and reproduce noise-induced bi-stability and oscillations in dynamically complex GRNs. It also allows for a simple consideration of deterministic delays and infers the relevant regulatory connections and steady-state parameters of GRNs. The associated framework, CaiNet, provides a user-friendly environment for setting up GRNs and using the simulation and inference method.
BMC BIOINFORMATICS
(2022)
Article
Biotechnology & Applied Microbiology
Oceane Cassan, Sophie Lebre, Antoine Martin
Summary: DIANE is a user interface designed for high-throughput transcriptomic datasets, providing functions such as normalization, differential expression, gene clustering, and gene regulatory network inference using Random Forests. It allows clear and reproducible analyses for exploring RNA-Seq data.
Article
Computer Science, Interdisciplinary Applications
Suman Mitra, Sriyankar Acharyya
Summary: This paper proposes a new variant of Cuckoo Search algorithm (PRDCS) that improves the performance of the search algorithm through the use of a repository and successive perturbations. The effectiveness of PRDCS is validated through experiments based on performance metrics. Furthermore, PRDCS is applied to gene regulatory network reconstruction and tested on real gene expression datasets.
JOURNAL OF COMPUTATIONAL SCIENCE
(2022)
Article
Biochemical Research Methods
Shuwen Hu, Yi Jing, Tao Li, You-Gan Wang, Zhenyu Liu, Jing Gao, Yu-Chu Tian
Summary: The new hybrid framework, CGRF, efficiently infers circadian gene regulatory relationships from rat gene expression data by addressing the challenges of high-dimensional data. By combining fuzzy C-means clustering algorithm with dynamic time warping distance, significant genes related to the target gene are identified and directed causal relationships based on partial correlation are revealed. The framework offers a comprehensive solution for understanding circadian gene regulation.
BMC BIOINFORMATICS
(2023)