Identifying critical states of hepatocellular carcinoma based on single-sample dynamic network biomarkers combined with simulated annealing algorithm

2021 ◽  
Vol 16 ◽  
Author(s):  
Hongqian Zhao ◽  
Jie Gao ◽  
Yichen Sun ◽  
Yujie Wang ◽  
Tianhao Guan ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Simulated Annealing ◽  
Critical State ◽  
Simulated Annealing Algorithm ◽  
Dynamic Network ◽  
Disease State ◽  
Statistical Characteristics ◽  
Low Grade ◽  
Network Biomarkers ◽  
Annealing Algorithm

Background: Hepatocellular carcinoma(HCC) is one of the most common malignant tumors. Due to the insidious onset and poor prognosis, most patients have reached the advanced stage at the time of diagnosis. Objective: Studies have shown thatdynamic network biomarkers (DNB) can effectively identify the critical state of complex diseases such as HCC from normal state to disease state. Therefore, it is very important to detect DNB efficiently and reliably. Methods: This paper selects a dataset containing eight HCC disease states. First, anindividual-specific network is constructed for each sample and features are extracted. In the context of this network, a simulated annealing algorithm is used to search for potential dynamic network biomarker modules, and the evolution of HCC is determined. Results: In fact, in the period of low-grade dysplasia (LGD) and high-grade dysplasia (HGD), DNB will send an indicative warning signal, which means that liver dysplasia is a very important critical state in the development of HCC disease. Compared with landscape dynamic network biomarkers method (L-DNB), our method can not only describe the statistical characteristics of each disease state, but also yield better results including getting more DNBs enriched in HCC related pathways. Conclusion: The results of this study may be of great significance to the prevention and early diagnosis of HCC.

scholarly journals Low-Grade Dysplastic Nodules Revealed as the Tipping Point during Multistep Hepatocarcinogenesis by Dynamic Network Biomarkers

Genes ◽  
2017 ◽  
Vol 8 (10) ◽  
pp. 268 ◽  
Author(s):  
Lina Lu ◽  
Zhonglin Jiang ◽  
Yulin Dai ◽  
Luonan Chen
Keyword(s):  
Cell Adhesion ◽  
Signaling Pathway ◽  
Dynamic Network ◽  
Tipping Point ◽  
Disease State ◽  
Janus Kinase ◽  
Low Grade ◽  
Type I ◽  
Dysplastic Nodules ◽  
Network Biomarkers

Hepatocellular carcinoma (HCC) is a complex disease with a multi-step carcinogenic process from preneoplastic lesions, including cirrhosis, low-grade dysplastic nodules (LGDNs), and high-grade dysplastic nodules (HGDNs) to HCC. There is only an elemental understanding of its molecular pathogenesis, for which a key problem is to identify when and how the critical transition happens during the HCC initiation period at a molecular level. In this work, for the first time, we revealed that LGDNs is the tipping point (i.e., pre-HCC state rather than HCC state) of hepatocarcinogenesis based on a series of gene expression profiles by a new mathematical model termed dynamic network biomarkers (DNB)—a group of dominant genes or molecules for the transition. Different from the conventional biomarkers based on the differential expressions of the observed genes (or molecules) for diagnosing a disease state, the DNB model exploits collective fluctuations and correlations of the observed genes, thereby predicting the imminent disease state or diagnosing the critical state. Our results show that DNB composed of 59 genes signals the tipping point of HCC (i.e., LGDNs). On the other hand, there are a large number of differentially expressed genes between cirrhosis and HGDNs, which highlighted the stark differences or drastic changes before and after the tipping point or LGDNs, implying the 59 DNB members serving as the early-warning signals of the upcoming drastic deterioration for HCC. We further identified the biological pathways responsible for this transition, such as the type I interferon signaling pathway, Janus kinase–signal transducers and activators of transcription (JAK–STAT) signaling pathway, transforming growth factor (TGF)-β signaling pathway, retinoic acid-inducible gene I (RIG-I)-like receptor signaling pathway, cell adhesion molecules, and cell cycle. In particular, pathways related to immune system reactions and cell adhesion were downregulated, and pathways related to cell growth and death were upregulated. Furthermore, DNB was validated as an effective predictor of prognosis for HCV-induced HCC patients by survival analysis on independent data, suggesting a potential clinical application of DNB. This work provides biological insights into the dynamic regulations of the critical transitions during multistep hepatocarcinogenesis.

A New Solution for Maxterm Problem in Trigonometric Functions by Simulated Annealing Algorithm

2013 ◽  
Vol 4 (2) ◽  
pp. 20-28
Author(s):  
Farhad Soleimanian Gharehchopogh ◽  
Hadi Najafi ◽  
Kourosh Farahkhah
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Trigonometric Functions ◽  
Matlab Software ◽  
Annealing Algorithm ◽  
Do So

The present paper is an attempt to get total minimum of trigonometric Functions by Simulated Annealing. To do so the researchers ran Simulated Annealing. Sample trigonometric functions and showed the results through Matlab software. According the Simulated Annealing Solves the problem of getting stuck in a local Maxterm and one can always get the best result through the Algorithm.

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