COMBAI computational biology and artificial intelligence

Two networks including normal and cancer network were based on our study entitled

"Noncoding RNAs endogenously rule the cancerous regulatory realm while proteins govern the normal"
as detailed below

Cancers evolve from normal tissues and share an endogenous regulatory realm distinctive from that of normal human tissues. Unearthing such an endogenous realm faces challenges due to heterogeneous biology data. This study computes petabyte level data and reveals the endogenous regulatory networks of normal and cancers and then unearths the most important endogenous regulators for normal and cancerous realm. In normal, proteins dominate the entire realm and trans-regulate their targets across chromosomes and ribosomal proteins serve as the most important drivers. However, in cancerous realm, noncoding RNAs dominate the whole realm and pseudogenes work as the most important regulators that cis-regulate their neighbors, in which they primarily regulate their targets within 1 million base pairs but they rarely regulate their cognates with complementary sequences as thought. Therefore, two distinctive mechanisms rule the normal and cancerous realm separately, in which noncoding RNAs endogenously regulate cancers, instead of proteins as currently conceptualized. This establishes a fundamental avenue to understand the basis of cancerous and normal physiology.

This study generated two regulatory networks, normal human network and cancer network, which can be searched by listed below

Normal network Cancer network

References

Anyou Wang. 2024. Conceptual breakthroughs of the long noncoding RNA functional system and its endogenous regulatory role in the cancerous regime. Explor Target Antitumor Ther. 2024;5:170–186 DOI: https://doi.org/10.37349/etat.2024.00211 Anyou Wang. 2022. Noncoding RNAs endogenously rule the cancerous regulatory realm while proteins govern the normal. Computational and Structural Biotechnology Journal.https://doi.org/10.1016/j.csbj.2022.04.015 Wang, A. & Hai, R. FINET: Fast Inferring NETwork. BMC Res Notes 13, 521 (2020). https://doi.org/10.1186/s13104-020-05371-0 Anyou Wang.2022. Distinctive functional regime of endogenous lncRNAs in dark regions of human genome.Computational and Structural Biotechnology Journal. https://doi.org/10.1016/j.csbj.2022.05.020