LU ZHANG^*, JINGUO CHU^*, YUSHAN YU

Oncology Research, Vol.32, No.4, pp. 703-716, 2024, DOI:10.32604/or.2023.030988

Abstract Background: Limited research has been conducted on the influence of autophagy-associated long non-coding RNAs (ARLncRNAs) on the prognosis of hepatocellular carcinoma (HCC). Methods: We analyzed 371 HCC samples from TCGA, identifying expression networks of ARLncRNAs using autophagy-related genes. Screening for prognostically relevant ARLncRNAs involved univariate Cox regression, Lasso regression, and multivariate Cox regression. A Nomogram was further employed to assess the reliability of Riskscore, calculated from the signatures of screened ARLncRNAs, in predicting outcomes. Additionally, we compared drug sensitivities in patient groups with differing risk levels and investigated potential biological pathways through enrichment analysis, using consensus clustering to identify subgroups… More >

GAOZHONG LI, FUXIN LI, NING WEI, QING JIA^*

BIOCELL, Vol.47, No.9, pp. 2081-2099, 2023, DOI:10.32604/biocell.2023.029986

Abstract Background: This study was aimed at identifying natural killer (NK) cell-related genes to design a risk prognosis model for the accurate evaluation of gastric cancer (GC) prognosis. Methods: We obtained NK cell-related genes from various databases, followed by Cox regression analysis and molecular typing to identify prognostic genes. Various immune algorithms and enrichment analyses were used to investigate the mutations, immune status, and pathway variations among different genotypes. The key prognostic genes were assessed using the least absolute shrinkage and selection operator (Lasso) regression analysis and univariate Cox regression analysis. Thereafter, the risk score (RS) prognosis model was constructed based… More > Graphic Abstract

A.S. Koyuncugil¹, N. Ozgulbas²

The International Conference on Computational & Experimental Engineering and Sciences, Vol.11, No.2, pp. 39-46, 2009, DOI:10.3970/icces.2009.011.039

Abstract In this paper, a data mining model for detecting financial and operational risk indicators by CHAID Decision Tree is presenting. The identification of the risk factors by clarifying the relationship between the variables defines the discovery of knowledge from the financial and operational variables. Automatic and estimation oriented information discovery process coincides the definition of data mining. During the formation of model; an easy to understand, easy to interpret and easy to apply utilitarian model that is far from the requirement of theoretical background is targeted by the discovery of the implicit relationships between the data and the identification of… More >