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| 1 | Dysfunction of PLA2G6 and CYP2C44-associated network signals imminent carcinogenesis from chronic inflammation to hepatocellular carcinoma显示文摘很少长期的发炎怎么贡献 hepatocellular 癌(HCC ) 的前进被知道,特别癌症的开始。揭开从长期的发炎的批评转变到在网络水平的 HCC 和分子的机制,我们用我们的动态网络 biomarker (DNB ) 分析了土拨鼠肝炎 virus/c-myc 鼠标和匹配年龄的 wt-C57BL/6 鼠标的时间系列 proteomic 数据模型。DNB 分析显示在转基因的老鼠的出生以后的第 5 月是癌症开始的批评时期,就在批评转变前,它与临床的症状一致。同时,联系 DNB 的网络在批评转变前后显示出蛋白质表示和 coexpression 层次的激烈的倒置。DNB, PLA2G6 和 CYP2C44 的二个成员,与他们的联系差别一起表示了蛋白质,被发现导致 arachidonic 酸新陈代谢的机能障碍,进一步通过短暂受体潜力隧道的煽动性的调停人规定激活煽动性的回答,并且最后导致肝 detoxification 和恶意的转变的缺陷到癌症。作为一个 c-Myc 目标, PLA2G6 断然在表示与 c-Myc 相关,显示出从减少到在 carcinogenesis 期间增加的一个趋势,与在批评转变的最小的点或付小费给的点。相应 PLA2G6 和 c-Myc 的如此的趋势也在人的 hepatocarcinogenesis 期间被观察,与在高级 dysplastic 小瘤(就在 carcinogenesis 前的一个阶段) 的最小的点。我们的学习暗示 PLA2G6 可能在 hepatocarcinogenesis 期间作为象著名 c-Myc 一样的 oncogene 工作,当 PLA2G6 和 c-Myc 的 downregulation 能是显示逼近的 carcinogenesis 的一个警告信号时。 | Meiyi Li Chen Li Wei-Xin Liu Conghui Liu Jingru Cui Qingrun Li Hong Ni Yingcheng Yang Chaochao Wu Chunlei Chen Xing Zhen Tao Zeng Mujun zhao Lei Chen Jiarui Wu Rong Zeng Luonan Chen | 2017 | Journal of Molecular Cell Biology2017，9，6： | 12 |
| 2 | Detection for disease tipping points by landscape dynamic network biomarkers显示文摘A new model-free method has been developed and termed the landscape dynamic network biomarker(l-DNB) methodology. The method is based on bifurcation theory, which can identify tipping points prior to serious disease deterioration using only single-sample omics data. Here, we show that l-DNB provides early-warning signals of disease deterioration on a single-sample basis and also detects critical genes or network biomarkers(i.e. DNB members) that promote the transition from normal to disease states. As a case study, l-DNB was used to predict severe influenza symptoms prior to the actual symptomatic appearance in influenza virus infections. The l-DNB approach was then also applied to three tumor disease datasets from the TCGA and was used to detect critical stages prior to tumor deterioration using an individual DNB for each patient. The individual DNBs were further used as individual biomarkers in the analysis of physiological data, which led to the identification of two biomarker types that were surprisingly effective in predicting the prognosis of tumors. The biomarkers can be considered as common biomarkers for cancer, wherein one indicates a poor prognosis and the other indicates a good prognosis. | Xiaoping Liu Xiao Chang Siyang Leng Hui Tang Kazuyuki Aihara Luonan Chen | 2019 | National Science Review2019，6，4： | 12 |
| 3 | Toripalimab plus chemotherapy as second-line treatment in previously EGFR-TKI treated patients with EGFR-mutant-advanced NSCLC:a multicenter phase-II trial显示文摘This multicenter phase-II trial aimed to investigate the efficacy,safety,and predictive biomarkers of toripalimab plus chemotherapy as second-line treatment in patients with EGFR-mutant-advanced NSCLC.Patients who failed from first-line EGFR-TKIs and did not harbor T790M mutation were enrolled.Toripalimab plus carboplatin and pemetrexed were administrated every three weeks for up to six cycles,followed by the maintenance of toripalimab and pemetrexed.The primary endpoint was objective-response rate(ORR).Integrated biomarker analysis of PD-L1 expression,tumor mutational burden(TMB),CD8+tumor-infiltrating lymphocyte(TIL)density,whole-exome,and transcriptome sequencing on tumor biopsies were also conducted.Forty patients were enrolled with an overall ORR of 50.0%and disease-control rate(DCR)of 87.5%.The median progression free survival(PFS)and overall survival were 7.0 and 23.5 months,respectively.The most common treatment-related adverse effects were leukopenia,neutropenia,anemia,ALT/AST elevation,and nausea.Biomarker analysis showed that none of PD-L1 expression,TMB level,and CD8+TIL density could serve as a predictive biomarker.Integrated analysis of whole-exome and transcriptome sequencing data revealed that patients with DSPP mutation had a decreased M2 macrophage infiltration and associated with longer PFS than those of wild type.Toripalimab plus chemotherapy showed a promising anti-tumor activity with acceptable safety profiles as the second-line setting in patients with EGFR-mutant NSCLC.DSPP mutation might serve as a potential biomarker for this combination.A phase-III trial to compare toripalimab versus placebo in combination with chemotherapy in this setting is ongoing(NCT03924050). | Tao Jiang Pingyang Wang Jie Zhang Yanqiu Zhao Jianying Zhou Yun Fan Yongqian Shu Xiaoqing Liu Helong Zhang Jianxing He Guanghui Gao Xiaoqian Mu Zhang Bao Yanjun Xu Renhua Guo Hong Wang Lin Deng Ningqiang Ma Yalei Zhang Hui Feng Sheng Yao Jiarui Wu Luonan Chen Caicun Zhou Shengxiang Ren | 2021 | Signal Transduction and Targeted Therapy2021，6，11： | 8 |
| 4 | Hunt for the tipping point during endocrine resistance process in breast cancer by dynamic network biomarkers显示文摘Acquired drug resistance is the major reason why patients fail to respond to cancer therapies.It is a challenging task to deter.mine the tipping point of endocrine resistance and detect the associated molecules.Derived from new systems biology theory, the dynamic network biomarker (DNB) method is designed to quantitatively identify the tipping point of a drastic system transition and can theoretically identify DNB genes that play key roles in acquiring drug resistance.We analyzed time-course mRNA sequence data generated from the tamoxifen-treated estrogen receptor (ER)-positive MCF-7 cell line, and identified the tipping point of endocrine resistance with its leading molecules.The results show that there is interplay between gene mutations and DNB genes, in which the accumulated mutations eventually affect the DNB genes that subsequently cause the change of transcriptional landscape, enabling full-blown drug resistance. Survival analyses based on clinical datasets validated that the DNB genes were associated with the poor survival of breast cancer patients.The results provided the detection for the pre-resistance state or early signs of endocrine resistance.Our predictive method may greatly benefit the scheduling of treatments for complex diseases in which patients are exposed to considerably different drugs and may become drug resistant. | Rui Liu Jinzeng Wang Masao Ukai Ki Sewon Pei Chen Yutaka Suzuki Haiyun Wang Kazuyuki Aihara Mariko Okada-Hatakeyama Luonan Chen | 2019 | Journal of Molecular Cell Biology2019，11，8： | 7 |
| 5 | Edge biomarkers for classification and prediction of phenotypes显示文摘In general,a disease manifests not from malfunction of individual molecules but from failure of the relevant system or network,which can be considered as a set of interactions or edges among molecules.Thus,instead of individual molecules,networks or edges are stable forms to reliably characterize complex diseases.This paper reviews both traditional node biomarkers and edge biomarkers,which have been newly proposed.These biomarkers are classified in terms of their contained information.In particular,we show that edge and network biomarkers provide novel ways of stably and reliably diagnosing the disease state of a sample.First,we categorize the biomarkers based on the information used in the learning and prediction steps.We then briefly introduce conventional node biomarkers,or molecular biomarkers without network information,and their computational approaches.The main focus of this paper is edge and network biomarkers,which exploit network information to improve the accuracy of diagnosis and prognosis.Moreover,by extracting both network and dynamic information from the data,we can develop dynamical network and edge biomarkers.These biomarkers not only diagnose the immediate pre-disease state but also detect the critical molecules or networks by which the biological system progresses from the healthy to the disease state.The identified critical molecules can be used as drug targets,and the critical state indicates the critical point of disease control.The paper also discusses representative biomarker-based methods. | ZENG Tao ZHANG WanWei YU XiangTian LIU XiaoPing LI MeiYi LIU Rui CHEN LuoNan | 2014 | Science China(Life Sciences)2014，57，11： | 5 |
| 6 | Data-based prediction and causality inference of nonlinear dynamics显示文摘Natural systems are typically nonlinear and complex, and it is of great interest to be able to reconstruct a system in order to understand its mechanism, which cannot only recover nonlinear behaviors but also predict future dynamics. Due to the advances of modern technology, big data becomes increasingly accessible and consequently the problem of reconstructing systems from measured data or time series plays a central role in many scientific disciplines. In recent decades, nonlinear methods rooted in state space reconstruction have been developed, and they do not assume any model equations but can recover the dynamics purely from the measured time series data. In this review, the development of state space reconstruction techniques will be introduced and the recent advances in systems prediction and causality inference using state space reconstruction will be presented. Particularly, the cutting-edge method to deal with short-term time series data will be focused on.Finally, the advantages as well as the remaining problems in this field are discussed. | Huanfei Ma Siyang Leng Luonan Chen | 2018 | Science China Mathematics2018，61，3： | 5 |
| 7 | Dynamical network biomarkers for identifying critical transitions and their driving networks of biologic processes显示文摘 | Rui Liu Kazuyuki Aihara Luonan Chen | 2013 | Frontiers of Electrical and Electronic Engineering in China2013，8，2： | 5 |
| 8 | Dynamics-based data science in biology显示文摘Life science has long been a rich subject for research,and continues to develop at high speed.One of the major aims of life science is to study the mechanisms of various biological processes on the basis of biological big-data.Many statistics-based methods have been proposed to catch the essence by mining such data,including the popular category classification,variables regression,group clustering,statistical comparison,dimensionality reduction,and component analysis. | Jifan Shi Kazuyuki Aihara Luonan Chen | 2021 | National Science Review2021，8，5： | 4 |
| 9 | Big Biological Data:Challenges and Opportunities显示文摘In‘‘Omics’’era of the life sciences,data is presented in many forms,which represent the information at various levels of bio logical systems,including data about genome,transcriptome epigenome,proteome,metabolome,molecular imaging,molec ular pathways,different population of people and clinical/med ical records.The biological data is big,and its scale has already been well beyond petabyte(PB)even exabyte(EB). | Yixue Li Luonan Chen | 2014 | Genomics, Proteomics & Bioinformatics2014，12，5： | 3 |
| 10 | Predicting future dynamics from short-term time series using an Anticipated Learning Machine显示文摘Predicting time series has significant practical applications over different disciplines.Here,we propose an Anticipated Learning Machine(ALM)to achieve precise future-state predictions based on short-term but high-dimensional data.From non-linear dynamical systems theory,we show that ALM can transform recent correlation/spatial information of high-dimensional variables into future dynamical/temporal information of any target variable,thereby overcoming the small-sample problem and achieving multistep-ahead predictions.Since the training samples generated from high-dimensional data also include information of the unknown future values of the target variable,it is called anticipated learning.Extensive experiments on real-world data demonstrate significantly superior performances of ALM over all of the existing 12 methods.In contrast to traditional statistics-based machine learning,ALM is based on non-linear dynamics,thus opening a new way for dynamics-based machine learning. | Chuan Chen Rui Li Lin Shu Zhiyu He Jining Wang Chengming Zhang Huanfei Ma Kazuyuki Aihara Luonan Chen | 2020 | National Science Review2020，7，6： | 3 |
| 11 | Dynamic edge-based biomarker non-invasively predicts hepatocellular carcinoma with hepatitis B virus infection for individual patients based on blood testing显示文摘Hepatitis B virus (HBV)-induced hepatocellular carcinoma (HCC) is a major cause of cancer-related deaths in Asia and Africa. Developing effective and non-invasive biomarkers of HCC for individual patients remains an urgent task for early diagnosis and convenient monitoring. Analyzing the transcriptomic profiles of peripheral blood mononuclear cells from both healthy donors and patients with chronic HBV infection in different states (i.e. HBV carrier, chronic hepatitis B, cirrhosis, and HCC), we identified a set of 19 candidate genes according to our algorithm of dynamic network biomarkers. These genes can both characterize different stages during HCC progression and identify cirrhosis as the critical transition stage before carcinogenesis. The interaction effects (i.e. coexpressions) of candidate genes were used to build an accurate prediction model: the so-called edge-based biomarker. Considering the convenience and robustness of biomarkers in clinical applications, we performed functional analysis, validated candidate genes in other independent samples of our collected cohort, and finally selected COL5A1, HLA-DQB1, MMP2, and CDK4 to build edge panel as prediction models. We demonstrated that the edge panel had great performance in both diagnosis and prognosis in terms of precision and specificity for HCC, especially for patients with alpha-fetoprotein-negative HCC. Our study not only provides a novel edge-based biomarker for non-invasive and effective diagnosis of HBV-associated HCC to each individual patient but also introduces a new way to integrate the interaction terms of individual molecules for clinical diagnosis and prognosis from the network and dynamics perspectives. | Yiyu Lu Zhaoyuan Fang Meiyi Li Chen Qian Tao Zeng Lina Lu Qilong Chen Hui Zhang Qianmei Zhou Yan Sun Xuefeng Xue Yiyang Hu Luonan Chen Shibing Su | 2019 | Journal of Molecular Cell Biology2019，11，8： | 3 |
| 12 | Applicability and comparison of solar-air source heat pump systems between cold and warm regions of plateau by transient simulation and experiment显示文摘Solar-air source heat pump(solar-ASHP)system has a potential application in the field of hot water and space heating in residential buildings.Such system features the complementary advantages to solve the discontinuous operation of the single solar system and the frosting issue of the single ASHP system.This paper built the solar-ASHP systems in Kunming and Shangri-La,and tested the system performance under different weather conditions in these two regions of plateau.Meanwhile,the transient heat balance models of the system were established under the sunlight time and non-sunlight time and were verified by the experimental results.Moreover,the verified model was applied to reveal the energy balance performance between the energy supply and building heat demand.The law of the system performance affected by the ambient temperature,effective heat collecting area,and cumulative heating capacity of collector was explored by the validated model.The results indicate that when the ambient temperature decreases by 1℃during non-sunlight time,the energy efficiency ratio decreases by about 0.07.A square meter decline in the effective heat collecting area pushes an increase in the heating capacity of 5.75 MJ.Meanwhile,the cumulative heating capacity of collector increases by 5 MJ,and the ASHP energy consumption decreases by 0.54 kWh.The dynamic changes of the ambient temperature and instantaneous solar radiation are the main reasons of the heat balance errors.Therefore,both the developed system and model are feasible and reliable in different climate regions. | Luonan Xu Ming Li Ying Zhang Xi Luo | 2021 | Building Simulation2021，14，6： | 2 |
| 13 | Identification of Key Genes for the Ultrahigh Yield of Rice Using Dynamic Cross-tissue Network Analysis显示文摘Significantly increasing crop yield is a major and worldwide challenge for food supply and security.It is well-known that rice cultivated at Taoyuan in Yunnan of China can produce the highest yield worldwide.Yet,the gene regulatory mechanism underpinning this ultrahigh yield has been a mystery.Here,we systematically collected the transcriptome data for seven key tissues at different developmental stages using rice cultivated both at Taoyuan as the case group and at another regular rice planting place Jinghong as the control group.We identified the top 24 candidate high-yield genes with their network modules from these well-designed datasets by developing a novel computational systems biology method,i.e.,dynamic cross-tissue(DCT)network analysis.We used one of the candidate genes,Os SPL4,whose function was previously unknown,for gene editing experimental validation of the high yield,and confirmed that Os SPL4 significantly affects panicle branching and increases the rice yield.This study,which included extensive field phenotyping,cross-tissue systems biology analyses,and functional validation,uncovered the key genes and gene regulatory networks underpinning the ultrahigh yield of rice.The DCT method could be applied to other plant or animal systems if different phenotypes under various environments with the common genome sequences of the examined sample.DCT can be downloaded from http://gffzz188fe103f8f1460asbv599x0foq0w6n69.ffgz.tsg.suse.edu.cn/ztpub/DCT. | Jihong Hu Tao Zeng Qiongmei Xia Liyu Huang Yesheng Zhang Chuanchao Zhang Yan Zeng Hui Liu Shilai Zhang Guangfu Huang Wenting Wan Yi Ding Fengyi Hu Congdang Yang Luonan Chen Wen Wang | 2020 | Genomics, Proteomics & Bioinformatics2020，18，3： | 2 |
| 14 | Data-driven systems biology approaches显示文摘 | Luonan Chen | 2017 | Journal of Molecular Cell Biology2017，9，6： | 2 |
| 15 | Energy landscape decomposition for cell differentiation with proliferation effect显示文摘Complex interactions between genes determine the development and differentiation of cells.We establish a landscape theory for cell differentiation with proliferation effect,in which the developmental process is modeled as a stochastic dynamical system with a birth-death term.We find that two different energy landscapes,denoted U and V,collectively contribute to the establishment of non-equilibrium steady differentiation.The potential U is known as the energy landscape leading to the steady distribution,whose metastable states stand for cell types,while V indicates the differentiation direction from pluripotent to differentiated cells.This interpretation of cell differentiation is different from the previous landscape theory without the proliferation effect.We propose feasible numerical methods and a mean-field approximation for constructing landscapes U and V.Successful applications to typical biological models demonstrate the energy landscape decomposition’s validity and reveal biological insights into the considered processes. | Jifan Shi Kazuyuki Aihara Tiejun Li Luonan Chen | 2022 | National Science Review2022，9，8： | 2 |
| 16 | c-CSN:Single-cell RNA Sequencing Data Analysis by Conditional Cell-specific Network显示文摘t The rapid advancement of single-cell technologies has shed new light on the complex mechanisms of cellular heterogeneity.However,compared to bulk RNA sequencing(RNA-seq),single-cell RNA-seq(scRNA-seq)suffers from higher noise and lower coverage,which brings new computational difficulties.Based on statistical independence,cell-specific network(CSN)is able to quantify the overall associations between genes for each cell,yet suffering from a problem of overestimation related to indirect effects.To overcome this problem,we propose the c-CSN method,which can construct the conditional cell-specific network(CCSN)for each cell.c-CSN method can measure the direct associations between genes by eliminating the indirect associations.c-CSN can be used for cell clustering and dimension reduction on a network basis of single cells.Intuitively,each CCSN can be viewed as the transformation from less“reliable”gene expression to more“reliable”gene–gene associations in a cell.Based on CCSN,we further design network flow entropy(NFE)to estimate the differentiation potency of a single cell.A number of scRNA-seq datasets were used to demonstrate the advantages of our approach.1)One direct association network is generated for one cell.2)Most existing scRNA-seq methods designed for gene expression matrices are also applicable to c-CSN-transformed degree matrices.3)CCSN-based NFE helps resolving the direction of differentiation trajectories by quantifying the potency of each cell.c-CSN is publicly available at http://gffzz188fe103f8f1460asbv599x0foq0w6n69.ffgz.tsg.suse.edu.cn/LinLi-0909/c-CSN. | Lin Li Hao Dai Zhaoyuan Fang Luonan Chen | 2021 | Genomics, Proteomics & Bioinformatics2021，19，2： | 2 |
| 17 | Detecting direct associations in a network by information theoretic approaches显示文摘Detecting direct associations or inferring networks based on the observed data is an important issue in many fields, including biology, physics, engineering and social studies. In this work, we focus on the information theoretic approaches in the network reconstruction or the direct association detection, in particular,for biological networks. We not only review the traditional approaches or measurements on the associations among the observed variables, such as correlation coefficient, mutual information and conditional mutual information(CMI), but also summarize recently developed theories and methods. The new theoretic works include:information geometry to give a unified framework in detecting causality/association, the partial independence to alleviate the singularity of CMI, and multiscale analysis of CMI to avoid the underestimation issue of CMI.The new methods include part mutual information(PMI) and partial associations(PA), which improve the old measurements in avoiding both overestimation and underestimation. All those theories and methods make important contributions as major advances in the development of network inference. | Jifan Shi Juan Zhao Tiejun Li Luonan Chen | 2019 | Science China Mathematics2019，62，5： | 2 |
| 18 | Optimal operation of power systems constrained by transient stability显示文摘 | Luonan Chen Asako Ono Yasuyuki Tada | 2000 | Trans IEE Japan2000，120，12： | 1 |
| 19 | Chaotic Simulated Annealing by a Neural Network Model with Transient Chaos显示文摘 | Chen Luonan Aihara K | 1995 | Neural Network1995，8，6： | 1 |
| 20 | Visualization of Biomolecular Networks' Comparison on Cytoscape显示文摘Similarities and dissimilarities between biomolecular networks cannot be intuitively recognized even after the development of several comparison algorithms because of the lack of visualization tools. In this paper, an integrated tool kit named Biomolecular Network Match(BNMatch) is designed and developed based on Cytoscape—a popular and open-source tool for analyzing and visualizing networks. BNMatch integrates the comparison of the outputs of algorithms used for processing biomolecular networks and expresses the matching data between them by defining similar vertices and links with similar attributes. Moreover, in order to maintain consistency, their counterparts in other networks change when the nodes and edges in one of the compared networks are changed. It becomes easy for users to analyze similar networks by invoking comparison algorithms and visualizing the matching data between the networks using BNMatch. | Jiang Xie Zhonghua Zhou Kai Lu Luonan Chen Wu Zhang | 2013 | Tsinghua Science and Technology2013，18，5： | 1 |
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