TissueNexus: a bodymap of human gene networks across 49 tissues or cell lines may help understand diseases better


Mapping gene interactions within tissues/cell types plays a crucial role in understanding the genetic basis of human physiology and diseases. Functional gene networks (FGNs) try to map how various genes interact with each other. FGNs are much more than understanding gene co-expression. It is about developing a deeper insight into the interaction between various genes. Moreover, this interaction varies in different tissues. These are the questions of fundamental biology. Understanding them would ultimately help understand diseases and develop new kinds of drugs.

In a new study published in Nucleic Acids Research, Cui-Xiang Lin, Hong-Dong Li, Chao Deng and led by Professor Jianxin Wang at Central South University constructed the FGNs of 49 human tissues or cell lines using a large compendium of public RNA-seq data. They developed an innovative computational model, based on advanced artificial intelligence, to untangle the mystery of gene interactions in various tissues.

The research team found that most existing networks were based on Bayesian Classifiers  or few using Bayesian-based likelihood. However, the limitation of these existing models is that they cannot capture the non-linearity in data. Therefore, to improve existing FGNs, researchers decided to use state-of-the-art machine learning approach XGBoost to predict gene interaction in various tissues. Another reason why they chose XGBoost is that it is scalable to large datasets and can be readily applied to train millions of samples. First, researchers obtained multiple RNA-seq expression datasets for each tissue. Then, they constructed TissueNexus, a compendium of 49 tissue or cell line FGNs. Thus, what sets the their approach apart from existing ones is the sheer size of data and the use of XGBoost, resulting in more accurate networks. The final networks based on a total of 1,341 RNA-seq datasets and 52,097 samples are available for public access at https://www.diseaselinks.com/TissueNexus/.

When it comes to the practical application of the new study, it will help us better prioritize risk genes for complex diseases. For many diseases, their risk genes are incompletely understood and the prediction of novel genes are challenging. However, with TissueNexus, one can achieve significant improvement over previous networks. Thus, researchers analyzed a number of diseases representing a wide spectrum of pathological mechanisms, and showed that TissueNexus outperformed existing networks. In a statement to Medicine Innovates, Professor Jianxin Wang, the lead and corresponding author said the new TissueNexus will be an important resource for understanding diseases, has the advantage to prioritize risk genes of complex diseases, and demonstrates a better performance over existing networks.

TissueNexus: a bodymap of human gene networks across 49 tissues or cell lines may help understand diseases better - Medicine Innovates

About the author

Jianxin Wang, received his BEng and MEng degrees in computer science from Central South University of Technology, China, in 1992 and 1996, respectively, and the PhD degree in computer science from Central South University, China, in 2001. He is a professor and the dean in the School of Computer Science and Engineering at Central South University, Changsha, Hunan, P.R. China. His current research interests include bioinformatics and computer algorithms. His has published over 200 papers in bioinformatics in leading journals or conferences including Nature Communications and ISMB. His research has been cited over 13,000 times with H-index=54 (Google Scholar). He is a senior member of the IEEE, the chair of ACM Sigbio China and a senior member of China Computer Federation. He currently serves as editor of TCBB, IJDMB and  IJBRA. He served as program chair, co-chair or committee in several international conferences including FAW, ISBRA and BIBM.


Lin, C.-X., Li, H.-D., Deng, C., Guan, Y., & Wang, J. (2022). TissueNexus: A database of human tissue functional gene networks built with a large compendium of curated RNA-seq data. Nucleic Acids Research, 50(D1), D710–D718.

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