Results 61 to 70 of about 606,212 (231)

Node Metadata Can Produce Predictability Crossovers in Network Inference Problems

Physical Review X, 2022
Network inference is the process of learning the properties of complex networks from data. Besides using information about known links in the network, node attributes and other forms of network metadata can help solve network inference problems.
Oscar Fajardo-Fontiveros, Roger Guimerà, Marta Sales-Pardo   +2 more
doaj   +1 more source

Neural Natural Language Inference Models Enhanced with External Knowledge

, 2018
Modeling natural language inference is a very challenging task. With the availability of large annotated data, it has recently become feasible to train complex models such as neural-network-based inference models, which have shown to achieve the state-of-
Chen, Qian, Inkpen, Diana, Ling, Zhen-Hua, Wei, Si, Zhu, Xiaodan   +4 more
core   +1 more source

Inference in Bayesian Networks.

AI Mag., 1999
A Bayesian network is a compact, expressive representation of uncertain relationships among parameters in a domain. In this article, I introduce basic methods for computing with Bayesian networks, starting with the simple idea of summing the probabilities of events of interest. The article introduces major current methods for exact computation, briefly
openaire   +2 more sources

Structural inference for uncertain networks

Physical Review E, 2016
In the study of networked systems such as biological, technological, and social networks the available data are often uncertain. Rather than knowing the structure of a network exactly, we know the connections between nodes only with a certain probability. In this paper we develop methods for the analysis of such uncertain data, focusing particularly on
Travis Martin, Brian Ball, Mark E. J. Newman   +2 more
openaire   +4 more sources

Gut microbiome and aging—A dynamic interplay of microbes, metabolites, and the immune system

FEBS Letters, EarlyView.
Age‐dependent shifts in microbial communities engender shifts in microbial metabolite profiles. These in turn drive shifts in barrier surface permeability of the gut and brain and induce immune activation. When paired with preexisting age‐related chronic inflammation this increases the risk of neuroinflammation and neurodegenerative diseases.
Aaron Mehl, Eran Blacher
wiley   +1 more source

Harnessing Unsupervised Ensemble Learning for Biomedical Applications: A Review of Methods and Advances

Mathematics
Advancements in data availability and computational techniques, including machine learning, have transformed the field of bioinformatics, enabling the robust analysis of complex, high-dimensional, and heterogeneous biomedical data.
Mehmet Eren Ahsen
doaj   +1 more source

Applying causal discovery to single-cell analyses using CausalCell

eLife, 2023
Correlation between objects is prone to occur coincidentally, and exploring correlation or association in most situations does not answer scientific questions rich in causality.
Yujian Wen, Jielong Huang, Shuhui Guo, Yehezqel Elyahu, Alon Monsonego, Hai Zhang, Yanqing Ding, Hao Zhu   +7 more
doaj   +1 more source

Hyperosmotic stress induces PARP1‐mediated HPF1‐dependent mono(ADP‐ribosyl)ation

FEBS Letters, EarlyView.
Sorbitol‐induced hyperosmotic stress rapidly induces reversible mono(ADP‐ribosyl)ation (MARylation) on PARP1 without the signs of genotoxic signaling. We show that PARP1 autoMARylation is HPF1 dependent and forms hydroxylamine‐resistant O‐glycosidic linkages.
Anna Georgina Kopasz, Mihály Mérey, Rebeka Vásárhelyi, Ramóna Pék, Victor Imburchia, László Henn, Adrián Kószó, Nicholas D. Lakin, Ivan Ahel, Sébastien Huet, Ágnes Czibula, Gyula Timinszky   +11 more
wiley   +1 more source

GTAT-GRN: a graph topology-aware attention method with multi-source feature fusion for gene regulatory network inference

Frontiers in Genetics
Gene regulatory network (GRN) inference is a central task in systems biology. However, due to the noisy nature of gene expression data and the diversity of regulatory structures, accurate GRN inference remains challenging. We hypothesize that integrating
Shuran Wang, Shuran Wang, Shuran Wang, Lilian Zhang, Lilian Zhang, Lilian Zhang, Lutao Gao, Lutao Gao, Lutao Gao, Yao Rao, Yao Rao, Yao Rao, Jie Cui, Jie Cui, Jie Cui, Linnan Yang, Linnan Yang, Linnan Yang   +17 more
doaj   +1 more source

Inferring Topology of Networks With Hidden Dynamic Variables

IEEE Access, 2022
Inferring the network topology from the dynamics of interacting units constitutes a topical challenge that drives research on its theory and applications across physics, mathematics, biology, and engineering.
Raoul Schmidt, Hauke Haehne, Laura Hillmann, Jose Casadiego, Dirk Witthaut, Benjamin Schafer, Marc Timme   +6 more
doaj   +1 more source