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您的检索式：作者名="Siyang Leng"
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| 1 | 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 |
| 2 | 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 |
| 3 | The complete reference genome for grapevine (Vitis vinifera L.) genetics and breeding显示文摘Grapevine is one of the most economically important crops worldwide.However,the previous versions of the grapevine reference genome tipically consist of thousands of fragments with missing centromeres and telomeres,limiting the accessibility of the repetitive sequences,the centromeric and telomeric regions,and the study of inheritance of important agronomic traits in these regions.Here,we assembled a telomere-to-telomere(T2T)gap-free reference genome for the cultivar PN40024 using PacBio HiFi long reads.The T2T reference genome(PN_T2T)is 69 Mb longer with 9018 more genes identified than the 12X.v0 version.We annotated 67%repetitive sequences,19 centromeres and 36 telomeres,and incorporated gene annotations of previous versions into the PN_T2T assembly.We detected a total of 377 gene clusters,which showed associations with complex traits,such as aroma and disease resistance.Even though PN40024 derives from nine generations of selfing,we still found nine genomic hotspots of heterozygous sites associated with biological processes,such as the oxidation–reduction process and protein phosphorylation.The fully annotated complete reference genome therefore constitutes an important resource for grapevine genetic studies and breeding programs. | Xiaoya Shi Shuo Cao Xu Wang Siyang Huang Yue Wang Zhongjie Liu Wenwen Liu Xiangpeng Leng Yanling Peng Nan Wang Yiwen Wang Zhiyao Ma Xiaodong Xu Fan Zhang Hui Xue Haixia Zhong Yi Wang Kekun Zhang Amandine Velt Komlan Avia Daniela Holtgräwe Jérôme Grimplet JoséTomás Matus Doreen Ware Xinyu Wu Haibo Wang Chonghuai Liu Yuling Fang Camille Rustenholz Zongming Cheng Hua Xiao Yongfeng Zhou | 2023 | Horticulture Research2023，10，5： | 3 |
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