Article
Biochemical Research Methods
Yan Guo, Hui Yu, Haocan Song, Jiapeng He, Olufunmilola Oyebamiji, Huining Kang, Jie Ping, Scott Ness, Yu Shyr, Fei Ye
Summary: The MetaGSCA tool allows for comprehensive meta-analyses of gene set differential coexpression data, identifying relevant pathways and visualizing them. The tool demonstrated its effectiveness in case studies of chronic kidney disease and non-small cell lung cancer, as well as in a pan-cancer analysis of 11 cancer types. Analysis with randomly generated gene sets showed low false positive rates, indicating the tool's specificity.
PLOS COMPUTATIONAL BIOLOGY
(2021)
Article
Multidisciplinary Sciences
Vasileios L. Zogopoulos, Georgia Saxami, Apostolos Malatras, Antonia Angelopoulou, Chih-Hung Jen, William J. Duddy, Gerasimos Daras, Polydefkis Hatzopoulos, David R. Westhead, Ioannis Michalopoulos
Summary: Gene coexpression analysis aims to identify sets of genes with similar expression patterns across multiple data sets, such as microarray experiments. The Arabidopsis Coexpression Tool (ACT) generates a coexpression tree from processed microarray data, revealing relationships between different genes. ACT offers a user-friendly interface for gene set enrichment analysis and is successful in identifying both ubiquitous and tissue-specific gene expressions.
Article
Biochemistry & Molecular Biology
Luyao Huang, Yao Min, Sarah Schiessl, Xinghua Xiong, Habib U. Jan, Xin He, Wei Qian, Chunyun Guan, Rod J. Snowdon, Wei Hua, Mei Guan, Lunwen Qian
Summary: This study identified multiple haplotype blocks associated with flowering time in rapeseed, with a focus on the role of the BnVIN3-C03 gene. Additionally, the study revealed that BnVIN3-C03 inhibits the expression of key genes, thereby influencing the regulatory mechanism of flowering time in Brassica napus.
Article
Multidisciplinary Sciences
Soudeh Ghafouri-Fard, Arash Safarzadeh, Mohammad Taheri, Elena Jamali
Summary: A co-expression network of differentially expressed genes (DEGs) related to colorectal cancer (CRC) and their target genes was constructed using the weighted gene co-expression network analysis (WGCNA) algorithm. GO and KEGG pathway analysis revealed that these genes were mainly involved in the regulation of hormone levels, extracellular matrix organization, and extracellular structure organization. The study also identified DKC1, PA2G4, LYAR and NOLC1 as the key hub genes of CRC.
SCIENTIFIC REPORTS
(2023)
Review
Immunology
Marta Vuerich, Na Wang, Ahmadreza Kalbasi, Jonathon J. Graham, Maria Serena Longhi
Summary: Autoimmune hepatitis (AIH) is a chronic inflammatory disorder characterized by hypergammaglobulinemia, presence of serum autoantibodies, and histological features of interface hepatitis. Corticosteroids, azathioprine, and other immunosuppressants are the main therapeutic options, but withdrawal of immunosuppression may lead to disease relapse. Understanding the pathogenesis of AIH is crucial for developing more effective treatments.
FRONTIERS IN IMMUNOLOGY
(2021)
Article
Biochemical Research Methods
Ruiqi Qin, Lei Duan, Huiru Zheng, Jesse Li-Ling, Kaiwen Song, Yidan Zhang
Summary: Identifying similar diseases is important for understanding disease etiology and pathogenesis in biomedicine. The study proposed a novel framework called RADAR which integrates disease similarity networks from multiple data sources to comprehensively evaluate disease similarities, demonstrating effective detection of similar diseases.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2021)
Article
Oncology
Huan Deng, Qingqing Hang, Dijian Shen, Yibi Zhang, Ming Chen
Summary: Our study identified 486 differentially coexpressed genes associated with LUAD, with ten hub genes closely correlated with overall survival. Through functional enrichment analysis and protein-protein interaction network establishment, we found that CHRDL1 and SPARCL1 may serve as potential therapeutic and prognostic indicators for LUAD.
CANCER CELL INTERNATIONAL
(2021)
Article
Mathematical & Computational Biology
Jian Yang, Liqi Shu, Huilong Duan, Haomin Li
Summary: This study proposes a phenotype-based differential diagnosis process for rare diseases, aiming to achieve rapid and accurate diagnosis by optimizing patient phenotype information. The process involves constructing a phenotype hierarchical network and a disease-phenotype differential network, calculating phenotype co-occurrence relationships, and designing a visual comparative analysis method to explore correlations and differences between disease phenotypes.
INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES
(2022)
Article
Biochemical Research Methods
Yuhao Chen, Yanshi Hu, Xiaotian Hu, Cong Feng, Ming Chen
Summary: This paper presents a contrastive learning framework CoGO, which uses deep learning models to extract and incorporate biological data for predicting disease similarity. Experimental results show that CoGO outperforms other methods in terms of prediction performance and provides highly credible disease similarity results compared to other studies.
Article
Computer Science, Artificial Intelligence
Xuegang Song, Feng Zhou, Alejandro F. Frangi, Jiuwen Cao, Xiaohua Xiao, Yi Lei, Tianfu Wang, Baiying Lei
Summary: This paper proposes three mechanisms to improve GCN, namely SAC-GCN, for predicting SMC and MCI. First, a similarity-aware graph is designed using different receptive fields to consider disease status. An adaptive mechanism is proposed to evaluate similarity, as well as a calibration mechanism to fuse fMRI and DTI information into edges.
MEDICAL IMAGE ANALYSIS
(2021)
Article
Biotechnology & Applied Microbiology
Aiswarya Jayaprakash, Abhijeet Roy, Raja Rajeswary Thanmalagan, Annamalai Arunachalam, P. T. Lakshmi
Summary: The study conducted a transcriptomic network analysis to understand the peanut defense mechanism against Aspergillus flavus. Functional enrichment analysis identified key genes involved in immune response against the pathogen, such as Protein P21, R genes, and Pattern Recognition Receptor genes. The interplay of resistance conferring genes and cell wall related genes were observed in response to pathogen infection.
Article
Biochemical Research Methods
Ningyi Zhang, Tianyi Zang
Summary: ImpAESim focuses on extracting a low-dimensional vector representation of features based on ncRNA regulation and gene-gene interaction network. Our method can significantly reduce the calculation bias resulted from the sparse disease associations derived from semantic associations.
BMC BIOINFORMATICS
(2022)
Article
Biochemistry & Molecular Biology
Haiyan Liu, Pingping Bing, Meijun Zhang, Geng Tian, Jun Ma, Haigang Li, Meihua Bao, Kunhui He, Jianjun He, Binsheng He, Jialiang Yang
Summary: In this study, a novel method called MNNMDA was proposed to predict microbe-disease associations by applying a Matrix Nuclear Norm method. The method constructed a heterogeneous information network by calculating Gaussian interaction profile kernel and functional similarity for diseases and microbes. The microbe-disease association prediction problem was formulated as a low-rank matrix completion problem, and the effectiveness of MNNMDA was validated through experiments on multiple datasets.
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
(2023)
Article
Biotechnology & Applied Microbiology
Junliang Shang, Yi Yang, Feng Li, Boxin Guan, Jin-Xing Liu, Yan Sun
Summary: This study proposes a method called BLNIMDA based on a weighted bi-level network for predicting hidden associations between miRNAs and diseases. The method defines different types of miRNA-disease associations and introduces affinity weights evaluation from bidirectional information distribution strategy and defined association types, ensuring comprehensive and accurate prediction of miRNA-disease associations. The results show that BLNIMDA outperforms other computational methods in terms of predictive performance.
Article
Multidisciplinary Sciences
C. Ricci-Tam, I Ben-Zion, J. Wang, J. Palme, A. Li, Y. Savir, M. Springer
Summary: Gene-regulatory networks achieve complex mappings of inputs to outputs through mechanisms that are poorly understood. In the galactose-responsive pathway in Saccharomyces cerevisiae, the decision to activate transcription of genes encoding pathway components is controlled independently from the expression level, resembling a mechanical dimmer switch. Hierarchical regulation of a single transcription factor enables dimmer switch gene regulation, allowing cells to fine-tune responses to multi-input environments on physiological and evolutionary time scales.