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Table representation of search results timeline featuring number of search results per year.

Year Number of Results
1951 1
1961 1
1963 3
1964 1
1965 2
1967 1
1968 1
1969 1
1972 2
1975 3
1976 1
1979 3
1980 2
1982 3
1983 1
1985 1
1986 1
1987 6
1988 9
1989 79
1990 151
1991 178
1992 378
1993 497
1994 921
1995 1248
1996 1428
1997 1669
1998 1778
1999 1848
2000 1776
2001 1871
2002 1751
2003 1994
2004 2310
2005 2458
2006 2614
2007 2879
2008 3568
2009 4622
2010 5654
2011 6298
2012 7130
2013 6723
2014 5862
2015 5731
2016 5405
2017 5456
2018 4548
2019 2202
2020 96
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79,606 results
Results by year
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Page 1
Hypothesis testing in functional linear models.
Su YR, et al. Biometrics 2017. PMID 28295175 Free PMC article.
While functional linear models (FLM) are widely used to address these questions, hypothesis testing for the functional association in the FLM framework remains challenging. ...
While functional linear models (FLM) are widely used to address these questions, hypothesis testing for the functional associa …
Linear effects models of signaling pathways from combinatorial perturbation data.
Szczurek E and Beerenwinkel N. Bioinformatics 2016. PMID 27307630 Free PMC article.
RESULTS: Here, we propose a linear effects model, which can be applied to solve both these problems from combinatorial perturbation data. ...
RESULTS: Here, we propose a linear effects model, which can be applied to solve both these problems from combinatorial perturb …
Analysis Of Nociceptive Evoked Potentials During Multi-Stimulus Experiments Using Linear Mixed Models.
van Berg BD and Buitenweg JR. Conf Proc IEEE Eng Med Biol Soc 2018. PMID 30441038
Linear mixed models use dependencies within the data to combine information from all data for the estimation of the evoked potential. In this work, it is shown that in multi-stimulus EEG data the quality of an evoked potential estimate can be improved by using a linear mixed model. ...
Linear mixed models use dependencies within the data to combine information from all data for the estimation of the evoked pot
On linear models and parameter identifiability in experimental biological systems.
Lamberton TO, et al. J Theor Biol 2014. PMID 24882792
The main results of the paper are to provide a general methodology for extracting parameters of linear models from an experimentally measured scalar function - the transfer function - and a framework for the identifiability analysis of complex model structures using linked models. Linked models are composed by letting the output of one model become the input to another model which is then experimentally measured. ...
The main results of the paper are to provide a general methodology for extracting parameters of linear models from an experime …
Interpreting encoding and decoding models.
Kriegeskorte N and Douglas PK. Curr Opin Neurobiol 2019 - Review. PMID 31039527 Free PMC article.
Encoding and decoding models typically include fitted linear-model components. Sometimes the weights of the fitted linear combinations are interpreted as reflecting, in an encoding model, the contribution of different sensory features to the representation or, in a decoding model, the contribution of different measured brain responses to a decoded feature. ...Many models must be tested and inferentially compared for analyses to drive theoretical progress....
Encoding and decoding models typically include fitted linear-model components. Sometimes the weights of the fitted l
Statistics: general linear models (a flexible approach).
Scott M, et al. J Small Anim Pract 2014. PMID 25134691
This article moves on to discuss a type of statistical testing different from those we have discussed previously, namely a General Linear Model. This system incorporates a number of other statistical models and is a powerful tool used widely in modern statistics....
This article moves on to discuss a type of statistical testing different from those we have discussed previously, namely a General Linear
[Functional linear models for region-based association analysis].
Svishcheva GR, et al. Genetika 2016. PMID 29369592 Russian.
Here we define a functional linear mixed model to test association on independent and structured samples. We demonstrate how to test fixed and random effects of a set of genetic variants in the region on quantitative trait. ...We suppose that new functional regression linear models facilitate identification of rare genetic variants controlling complex human and animal traits. ...
Here we define a functional linear mixed model to test association on independent and structured samples. We demonstrate how t …
Methodological quality and reporting of generalized linear mixed models in clinical medicine (2000-2012): a systematic review.
Casals M, et al. PLoS One 2014 - Review. PMID 25405342 Free PMC article.
BACKGROUND: Modeling count and binary data collected in hierarchical designs have increased the use of Generalized Linear Mixed Models (GLMMs) in medicine. ...The search strategy included the topic "generalized linear mixed models","hierarchical generalized linear models", "multilevel generalized linear model" and as a research domain we refined by science technology. ...
BACKGROUND: Modeling count and binary data collected in hierarchical designs have increased the use of Generalized Linear Mixe …
Gene expression inference with deep learning
Chen Y, et al. Bioinformatics 2016. PMID 26873929 Free PMC article.
However, the computational approach adopted by the LINCS program is currently based on linear regression (LR), limiting its accuracy since it does not capture complex nonlinear relationship between expressions of genes. ...We also tested the performance of our learned model on an independent RNA-Seq-based GTEx dataset, which consists of 2921 expression profiles. ...
However, the computational approach adopted by the LINCS program is currently based on linear regression (LR), limiting its accuracy …
79,606 results
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