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Results for conditional dependency

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12 papers · ranked by Valyu relevance

01
BbioRxiv20 Feb 2020
Inferring directional relationships in microbial communities using signed Bayesian networks
Musfiqur Sazal, Kalai Mathee, Daniel Ruiz-Perez, Trevor Cickovski + 1 more
Microbe-microbe and host-microbe interactions in a microbiome play a vital role in both health and disease. However, the structure of the microbial community and the colonization patterns are highly complex to infer even under controlled wet laboratory conditions. In this study, we investigate what information, if any…
72relevance
02
BbioRxiv14 Oct 2025
Bayesian Network Structure Learning: The New Calibrated Minimum Uncertainty Criterion and Discriminative Power Evaluation Framework
Grigoriy Gogoshin, Andrei S Rodin
Bayesian network (BN) structure learning (BNSL) from heterogeneous data is a classical problem in probabilistic machine learning and knowledge discovery. A variety of computational methods exist for score-based BNSL, but all inherit limitations imposed by the underlying model selection paradigms. With finite, often…
71relevance
03
BbioRxiv29 Mar 2022
CoCoA: Conditional Correlation Models with Association Size
Danni Tu, Bridget Mahony, Tyler M. Moore, Maxwell A. Bertolero + 6 more
Many scientific questions can be formulated as hypotheses about conditional correlations. For instance, in tests of cognitive and physical performance, the trade-off between speed and accuracy motivates study of the two variables together. A natural question is whether speed-accuracy coupling depends on other…
66relevance
04
BbioRxiv19 Jan 2026
A predicted cancer dependency map for paralog pairs
Narod Kebabci, Hamda Ajmal, David J. Adams, Colm J. Ryan
Genome-wide CRISPR screening has enabled the development of dependency maps in hundreds of cancer cell lines, facilitating the identification of genetic vulnerabilities associated with specific biomarkers. Paralogs, despite being common drug targets, are often missed in these screens as their individual disruption…
63relevance
05
BbioRxiv6 Feb 2024
The role of prospective contingency in the control of behavior and dopamine signals during associative learning
Lechen Qian, Mark Burrell, Jay A. Hennig, Sara Matias + 3 more
Associative learning depends on contingency, the degree to which a stimulus predicts an outcome. Despite its importance, the neural mechanisms linking contingency to behavior remain elusive. Here we examined the dopamine activity in the ventral striatum – a signal implicated in associative learning – in a Pavlovian…
63relevance
06
BbioRxiv19 Mar 2024
*Id1, Spp1* and *Pak3* are biomarkers of *Smad4* and TGF-β1 dependency in conditional intestinal adenoma, organoids and colorectal cancer
Mirvat Surakhy, Julia Matheson, David Barnes, Emma J. Carter + 7 more
The evolutionarily conserved TGF-β signalling pathway suppresses cell growth, yet loss of function results in cancer phenotypes. Here, we evaluate loss of the TGF-β pathway signal transduction regulator, Mothers against decapentaplegic homolog 4 (Smad4) with respect to intestinal adenoma phenotypes and specific TGF-β…
61relevance
07
BbioRxiv10 Jul 2019
Extended Bayesian inference incorporating symmetry bias
Shuji Shinohara, Nobuhito Manome, Kouta Suzuki, Ung-il Chung + 4 more
In this study, we start by proposing a causal induction model that incorporates symmetry bias. This model has two parameters that control the strength of symmetry bias and includes conditional probability and conventional models of causal induction as special cases. It can reproduce causal induction of human judgment…
60relevance
08
BbioRxiv29 Nov 2019
Perceptual decisions are biased toward relevant prior choices
Helen Feigin, Shira Baror, Moshe Bar, Adam Zaidel
Perceptual decisions are biased by recent perceptual history – a phenomenon termed ‘serial-dependence.’ Using a visual location discrimination task, we investigated what aspects of perceptual decisions lead to serial dependence, and disambiguated the influences of low-level sensory information, prior choices and motor…
58relevance
09
BbioRxiv22 Feb 2017
A Mixture Copula Bayesian Network Model for Multimodal Genomic Data
Qingyang Zhang, Xuan Shi
Gaussian Bayesian networks have become a widely used framework to estimate directed associations between joint Gaussian variables, where the network structure encodes decomposition of multivariate normal density into local terms. However, the resulting estimates can be inaccurate when normality assumption is moderately…
57relevance
10
BbioRxiv2 Dec 2019
A new method of Bayesian causal inference in non-stationary environments
Shuji Shinohara, Nobuhito Manome, Kouta Suzuki, Ung-il Chung + 5 more
Bayesian inference is a process of narrowing down hypotheses (causes) to one that best explains observational data (effects). To accurately estimate a cause, a considerable amount of data is required to be observed for as long as possible. However, the object of inference is not always constant. In this case, a method…
56relevance
11
BbioRxiv4 Jul 2020
A fast, accurate, and generalisable heuristic-based negation detection algorithm for clinical text
Luke T Slater, William Bradlow, Dino FA Motti, Robert Hoehndorf + 2 more
Negation detection is an important task in biomedical text mining. Particularly in clinical settings, it is of critical importance to determine whether findings mentioned in text are present or absent. Rule-based negation detection algorithms are a common approach to the task, and more recent investigations have…
54relevance
12
BbioRxiv28 Feb 2024
Modeling cancer dependency with deep graph models
Hengyi Fu, Bojin Zhao, Peng Wang
A fundamental premise for precision oncology is a catalog of diverse actionable targets that could enable personalized treatment. Large scale Genome-wide lost-of-function screens such as cancer dependency map have systematically identified single gene vulnerabilities in numerous cell lines. However, it remains…
54relevance

Results for conditional dependency

Inferring directional relationships in microbial communities using signed Bayesian networks

Bayesian Network Structure Learning: The New Calibrated Minimum Uncertainty Criterion and Discriminative Power Evaluation Framework

CoCoA: Conditional Correlation Models with Association Size

A predicted cancer dependency map for paralog pairs

The role of prospective contingency in the control of behavior and dopamine signals during associative learning

Id1, Spp1 and Pak3 are biomarkers of Smad4 and TGF-β1 dependency in conditional intestinal adenoma, organoids and colorectal cancer

Extended Bayesian inference incorporating symmetry bias

Perceptual decisions are biased toward relevant prior choices

A Mixture Copula Bayesian Network Model for Multimodal Genomic Data

A new method of Bayesian causal inference in non-stationary environments

A fast, accurate, and generalisable heuristic-based negation detection algorithm for clinical text

Modeling cancer dependency with deep graph models

Inferring directional relationships in microbial communities using signed Bayesian networks

Bayesian Network Structure Learning: The New Calibrated Minimum Uncertainty Criterion and Discriminative Power Evaluation Framework

CoCoA: Conditional Correlation Models with Association Size

A predicted cancer dependency map for paralog pairs

The role of prospective contingency in the control of behavior and dopamine signals during associative learning

Id1, Spp1 and Pak3 are biomarkers of Smad4 and TGF-β1 dependency in conditional intestinal adenoma, organoids and colorectal cancer

Extended Bayesian inference incorporating symmetry bias

Perceptual decisions are biased toward relevant prior choices

A Mixture Copula Bayesian Network Model for Multimodal Genomic Data

A new method of Bayesian causal inference in non-stationary environments

A fast, accurate, and generalisable heuristic-based negation detection algorithm for clinical text

Modeling cancer dependency with deep graph models

Results for conditional dependency

Inferring directional relationships in microbial communities using signed Bayesian networks

Bayesian Network Structure Learning: The New Calibrated Minimum Uncertainty Criterion and Discriminative Power Evaluation Framework

CoCoA: Conditional Correlation Models with Association Size

A predicted cancer dependency map for paralog pairs

The role of prospective contingency in the control of behavior and dopamine signals during associative learning

*Id1, Spp1* and *Pak3* are biomarkers of *Smad4* and TGF-β1 dependency in conditional intestinal adenoma, organoids and colorectal cancer

Extended Bayesian inference incorporating symmetry bias

Perceptual decisions are biased toward relevant prior choices

A Mixture Copula Bayesian Network Model for Multimodal Genomic Data

A new method of Bayesian causal inference in non-stationary environments

A fast, accurate, and generalisable heuristic-based negation detection algorithm for clinical text

Modeling cancer dependency with deep graph models

Inferring directional relationships in microbial communities using signed Bayesian networks

Bayesian Network Structure Learning: The New Calibrated Minimum Uncertainty Criterion and Discriminative Power Evaluation Framework

CoCoA: Conditional Correlation Models with Association Size

A predicted cancer dependency map for paralog pairs

The role of prospective contingency in the control of behavior and dopamine signals during associative learning

*Id1, Spp1* and *Pak3* are biomarkers of *Smad4* and TGF-β1 dependency in conditional intestinal adenoma, organoids and colorectal cancer

Extended Bayesian inference incorporating symmetry bias

Perceptual decisions are biased toward relevant prior choices

A Mixture Copula Bayesian Network Model for Multimodal Genomic Data

A new method of Bayesian causal inference in non-stationary environments

A fast, accurate, and generalisable heuristic-based negation detection algorithm for clinical text

Modeling cancer dependency with deep graph models

Id1, Spp1 and Pak3 are biomarkers of Smad4 and TGF-β1 dependency in conditional intestinal adenoma, organoids and colorectal cancer

Id1, Spp1 and Pak3 are biomarkers of Smad4 and TGF-β1 dependency in conditional intestinal adenoma, organoids and colorectal cancer