Results 61 to 70 of about 5,161,693 (301)
Inference of Causal Networks Using a Topological Threshold
IEEE AccessWe propose a constraint-based algorithm, which automatically determines causal relevance thresholds, to infer causal networks from data. We call these topological thresholds.
Filipe Barroso +2 more
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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
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Network inference and biological dynamics [PDF]
The Annals of Applied Statistics, 2012 Published in at http://dx.doi.org/10.1214/11-AOAS532 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)
Oates, Chris. J., Mukherjee, Sach
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Design and analysis strategies for robust microbiome ageing research
FEBS Letters, EarlyView.The gut microbiome changes with age and associates with age‐related morbidity and mortality, establishing it as a potential biomarker and intervention target for ageing. Realising this potential requires methodological rigour, yet distinguishing biological signals from methodological artefacts remains challenging across cohorts. This review provides an
Mark Olenik +5 more
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MathematicsAdvancements 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
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Identification of Boolean Network Models From Time Series Data Incorporating Prior Knowledge
Frontiers in Physiology, 2018 Motivation: Mathematical models take an important place in science and engineering. A model can help scientists to explain dynamic behavior of a system and to understand the functionality of system components.
Thomas Leifeld, Zhihua Zhang, Ping Zhang
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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 +2 more
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Reconstructing enzyme evolution by protein engineering
FEBS Letters, EarlyView.Natural enzyme evolution can be retraced by protein engineering methods such as directed evolution, rational design, and ancestral sequence reconstruction. These approaches reveal how enzymes emerged from ligand‐binding scaffolds, developed varying substrate preferences, formed oligomeric complexes, adapted to environmental changes, and evolved novel ...
Lukas Drexler +2 more
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Frontiers in GeneticsGene 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 +17 more
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Microbiome‐blood–brain barrier interactions in aging — mechanisms and therapeutic potential
FEBS Letters, EarlyView.Aging reshapes the gut microbiome (↓SCFA‐producing commensals; ↑pro‐inflammatory outputs), shifting circulating metabolites (↓SCFAs; ↑LPS, ↑TMAO, ↑PAA) that act at the BBB to increase nonspecific transcytosis, alter transport, and promote astrocyte reactivity, heightening brain vulnerability.
Daniel Cuervo‐Zanatta +3 more
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