نتائج البحث

Sparse connectivity for MAP inference in linear models using sister mitral cells.

• Authors : Tootoonian, Sina^1,2 (AUTHOR) ; Schaefer, Andreas T.² (AUTHOR); Latham, Peter E.¹ (AUTHOR)

• Source: PLoS Computational Biology. 1/31/2022, Vol. 18 Issue 1, p1-40. 40p. 4 Diagrams, 2 Charts, 9 Graphs.

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A robust transfer learning approach for high-dimensional linear regression to support integration of multi-source gene expression data.

• Authors : Pan L; Department of Biostatistics, School of Public Health, Fudan University, Shanghai, China.; Gao Q

Subjects: Algorithms* ; Computational Biology*/Computational Biology*/Computational Biology*/methods ; Genomics*/Genomics*/Genomics*/methods

• Source: PLoS computational biology [PLoS Comput Biol] 2025 Jan 10; Vol. 21 (1), pp. e1012739. Date of Electronic Publication: 2025 Jan 10 (Print Publisher: Public Library of Science Country of Publication: United States NLM ID: 101238922 Publication Model: eCollection Cited Medium: Internet

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Bayesian inference of structured latent spaces from neural population activity with the orthogonal stochastic linear mixing model

• Authors : Meng, Rui; Bouchard, Kristofer E

Subjects: Information and Computing Sciences; Machine Learning; Bioengineering

• Source: PLOS Computational Biology. 20(4)

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Linear-nonlinear cascades capture synaptic dynamics.

• Authors : Rossbroich J; Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland.; Trotter D

Subjects: Linear Models* ; Nonlinear Dynamics*; Synapses/Synapses/Synapses/*physiology

• Source: PLoS computational biology [PLoS Comput Biol] 2021 Mar 15; Vol. 17 (3), pp. e1008013. Date of Electronic Publication: 2021 Mar 15 (Print Publisher: Public Library of Science Country of Publication: United States NLM ID: 101238922 Publication Model: eCollection Cited Medium: Internet

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Linear-regression-based algorithms can succeed at identifying microbial functional groups despite the nonlinearity of ecological function.

• Authors : Zhao Y; School of Physics, Nanjing University, Nanjing, Jiangsu, the People's Republic of China.; Cordero OX

Subjects: Algorithms* ; Ecosystem* ; Computational Biology*/Computational Biology*/Computational Biology*/methods

• Source: PLoS computational biology [PLoS Comput Biol] 2024 Nov 13; Vol. 20 (11), pp. e1012590. Date of Electronic Publication: 2024 Nov 13 (Print Publisher: Public Library of Science Country of Publication: United States NLM ID: 101238922 Publication Model: eCollection Cited Medium: Internet

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FedGMMAT: Federated generalized linear mixed model association tests.

• Authors : Li W; McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, Texas, United States of America.; Chen H

Subjects: Information Dissemination*/Information Dissemination*/Information Dissemination*/methods; Humans ; Linear Models

• Source: PLoS computational biology [PLoS Comput Biol] 2024 Jul 24; Vol. 20 (7), pp. e1012142. Date of Electronic Publication: 2024 Jul 24 (Print Publisher: Public Library of Science Country of Publication: United States NLM ID: 101238922 Publication Model: eCollection Cited Medium: Internet

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Estimating repeat spectra and genome length from low-coverage genome skims with RESPECT.

• Authors : Sarmashghi, Shahab; Balaban, Metin; Rachtman, Eleonora

Subjects: Algorithms; Animals; Computational Biology

• Source: PLoS Computational Biology. 17(11)

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Fast and general tests of genetic interaction for genome-wide association studies.

• Authors : Frånberg M; PROCARDIS consortium

Subjects: Linear Models* ; Models, Genetic*; Chromosome Mapping/Chromosome Mapping/Chromosome Mapping/*methods

• Source: PLoS computational biology [PLoS Comput Biol] 2017 Jun 06; Vol. 13 (6), pp. e1005556. Date of Electronic Publication: 2017 Jun 06 (Print Publisher: Public Library of Science Country of Publication: United States NLM ID: 101238922 Publication Model: eCollection Cited Medium: Internet

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On the stability and dynamics of stochastic spiking neuron models: Nonlinear Hawkes process and point process GLMs.

• Authors : Gerhard F; Department of Neuroscience, Brown University, Providence, Rhode Island, United States of America.; Deger M

Subjects: Linear Models* ; Models, Neurological* ; Stochastic Processes*

• Source: PLoS computational biology [PLoS Comput Biol] 2017 Feb 24; Vol. 13 (2), pp. e1005390. Date of Electronic Publication: 2017 Feb 24 (Print Publisher: Public Library of Science Country of Publication: United States NLM ID: 101238922 Publication Model: eCollection Cited Medium: Internet

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Supervised learning and model analysis with compositional data.

• Authors : Huang S; Department of Mathematical Sciences, University of Copenhagen, Copenhagen, Denmark.; Ailer E

Subjects: Algorithms* ; Machine Learning*; Phylogeny

• Source: PLoS computational biology [PLoS Comput Biol] 2023 Jun 30; Vol. 19 (6), pp. e1011240. Date of Electronic Publication: 2023 Jun 30 (Print Publisher: Public Library of Science Country of Publication: United States NLM ID: 101238922 Publication Model: eCollection Cited Medium: Internet

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