Lei Hu^1,2,#, Meng Chen^2,3,#, Haiming Dai^2,3,4, Hongzhi Wang^2,3,4,*, Wulin Yang^2,3,4,*

Oncologie, Vol.24, No.4, pp. 803-822, 2022, DOI:10.32604/oncologie.2022.026419 - 31 December 2022

Abstract Background and Aim: Hundreds of consistently altered metabolic genes have been identified in breast cancer (BC), but their prognostic value remains to be explored. Therefore, we aimed to build a prediction model based on metabolism-related genes (MRGs) to guide BC prognosis. Methods: Current work focuses on constructing a novel MRGs signature to predict the prognosis of BC patients using MRGs derived from the Virtual Metabolic Human (VMH) database, and expression profiles and clinicopathological data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Results: The 3-MRGs-signature constructed by SERPINA1, QPRT and PXDNL was found… More >

YAJIE LIU, LINLIN FANG, CHUNXUE YANG^*

BIOCELL, Vol.46, No.11, pp. 2475-2488, 2022, DOI:10.32604/biocell.2022.021477 - 07 July 2022

Abstract Arbuscular mycorrhizal (AM) fungi are widely distributed in various habitats, and the community composition varies in response to the changing environmental conditions. To explore the response of community composition to the succession of saline-alkali land, soil samples were collected from three succession stages of Songnen saline-alkali grassland. Subsequently, the soil characteristics were determined and the AM fungi in soil samples were analyzed by high-throughput sequencing. Then, the response relationship between community composition and soil characteristics was studied by Canonical correlation and Pearson analyses. The soil properties improved with the succession of saline-alkali grassland. There was… More >

LEJUN GONG^1,2,*, SHEHAI ZHOU¹, JINGMEI CHEN¹, YONGMIN LI¹, LI ZHANG⁴, ZHIHONG GAO³

BIOCELL, Vol.46, No.4, pp. 951-960, 2022, DOI:10.32604/biocell.2022.016625 - 15 December 2021

Abstract Long non-coding RNAs (lncRNAs) play an important role in many life activities such as epigenetic material regulation, cell cycle regulation, dosage compensation and cell differentiation regulation, and are associated with many human diseases. There are many limitations in identifying and annotating lncRNAs using traditional biological experimental methods. With the development of high-throughput sequencing technology, it is of great practical significance to identify the lncRNAs from massive RNA sequence data using machine learning method. Based on the Bagging method and Decision Tree algorithm in ensemble learning, this paper proposes a method of lncRNAs gene sequence identification More >