[HTML][HTML] Network resilience
Many systems on our planet shift abruptly and irreversibly from the desired state to an
undesired state when forced across a “tipping point”. Some examples are mass extinctions …
undesired state when forced across a “tipping point”. Some examples are mass extinctions …
Big biological data: challenges and opportunities
In ''Omics''era of the life sciences, data is presented in many forms, which represent the
information at various levels of biological systems, including data about genome …
information at various levels of biological systems, including data about genome …
Meta learning with graph attention networks for low-data drug discovery
Finding candidate molecules with favorable pharmacological activity, low toxicity, and
proper pharmacokinetic properties is an important task in drug discovery. Deep neural …
proper pharmacokinetic properties is an important task in drug discovery. Deep neural …
Inference of gene regulatory network based on local Bayesian networks
F Liu, SW Zhang, WF Guo, ZG Wei… - PLoS computational …, 2016 - journals.plos.org
The inference of gene regulatory networks (GRNs) from expression data can mine the direct
regulations among genes and gain deep insights into biological processes at a network …
regulations among genes and gain deep insights into biological processes at a network …
Conditional mutual inclusive information enables accurate quantification of associations in gene regulatory networks
Mutual information (MI), a quantity describing the nonlinear dependence between two
random variables, has been widely used to construct gene regulatory networks (GRNs) …
random variables, has been widely used to construct gene regulatory networks (GRNs) …
Detecting causality from nonlinear dynamics with short-term time series
Quantifying causality between variables from observed time series data is of great
importance in various disciplines but also a challenging task, especially when the observed …
importance in various disciplines but also a challenging task, especially when the observed …
Identifying early-warning signals of critical transitions with strong noise by dynamical network markers
Identifying early-warning signals of a critical transition for a complex system is difficult,
especially when the target system is constantly perturbed by big noise, which makes the …
especially when the target system is constantly perturbed by big noise, which makes the …
Single-sample landscape entropy reveals the imminent phase transition during disease progression
Motivation The time evolution or dynamic change of many biological systems during disease
progression is not always smooth but occasionally abrupt, that is, there is a tipping point …
progression is not always smooth but occasionally abrupt, that is, there is a tipping point …
Individual-specific edge-network analysis for disease prediction
X Yu, J Zhang, S Sun, X Zhou, T Zeng… - Nucleic acids …, 2017 - academic.oup.com
Predicting pre-disease state or tipping point just before irreversible deterioration of health is
a difficult task. Edge-network analysis (ENA) with dynamic network biomarker (DNB) theory …
a difficult task. Edge-network analysis (ENA) with dynamic network biomarker (DNB) theory …
Hunt for the tipping point during endocrine resistance process in breast cancer by dynamic network biomarkers
Acquired drug resistance is the major reason why patients fail to respond to cancer
therapies. It is a challenging task to determine the tipping point of endocrine resistance and …
therapies. It is a challenging task to determine the tipping point of endocrine resistance and …