Index of papers in PLOS Comp. Biol. that mention
  • linear model
Ickwon Choi, Amy W. Chung, Todd J. Suscovich, Supachai Rerks-Ngarm, Punnee Pitisuttithum, Sorachai Nitayaphan, Jaranit Kaewkungwal, Robert J. O'Connell, Donald Francis, Merlin L. Robb, Nelson L. Michael, Jerome H. Kim, Galit Alter, Margaret E. Ackerman, Chris Bailey-Kellogg
Discussion
The penalized generalized linear models are generally very good, and provide the added advantage of easy interpretation and relatively low model complexity; as noted in the previous paragraph, a softer regularization might be beneficial in the future.
Results
As a linear transformation, the standardization does not affect linear models , though the additional preprocessing truncation to 60 has an appropriate impact on outliers.
Supervised learning: Classification
To assess how much this discrimination depends on the classification approach utilized rather than the underlying information content in the data, we employed three different representative classification techniques: penalized logistic regression (a regularized generalized linear model based on Lasso), regularized random forest (a tree-based model), and support vector machine (a kernel-based model).
Supervised learning: Regression
As With classification, the linear model dominates, and all methods perform similarly well With any of the input feature sets.
Supervised learning: Regression
Once again the linear model dominates the nonlinear models, particularly for ADCC.
linear model is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Stuart Aitken, Shigeyuki Magi, Ahmad M. N. Alhendi, Masayoshi Itoh, Hideya Kawaji, Timo Lassmann, Carsten O. Daub, Erik Arner, Piero Carninci, Alistair R. R. Forrest, Yoshihide Hayashizaki, Levon M. Khachigian, Mariko Okada-Hatakeyama, Colin A. Semple , the FANTOM Consortium
Definition of kinetic signatures
The linear model is parameterised by the expression at time 0 (p 1) and the change in expression (p 2) from which the rate of increase or decrease can be calculated.
Definition of kinetic signatures
The inference of model parameters from CAGE data for the early peak and linear models using nested sampling and the 11 based likelihood is illustrated in Fig 1C.
Definition of kinetic signatures
CAGE clusters are assigned to one of the exponential kinetic signatures if log Z for that signature is greater than 10 times log Z for the linear model and log Z minus its standard deviation (sd) is greater than log Z plus the estimated sd for any other eXponential signature (nested sampling computes log Z for parameters mapped to O..1 and we used the resulting log Z for the unit cube for model comparison).
Results
CAGE clusters were assigned to one of the exponential kinetic signatures or to the linear model according to the value of log Z.
Results
An example of fitting early peak and linear models to an EGR1 time course is presented in Fig 1C.
linear model is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Tamar Friedlander, Avraham E. Mayo, Tsvi Tlusty, Uri Alon
Retina problem
The fitness was defined as the difference between the network output and the desired output, in similarity to the linear model and then averaged over all possible input/ output pairs.
Simulations of multi-layered network models evolving towards input-output goals
We begin with a simple linear model of a multilayered network and later extend this framework to nonlinear models as well.
Simulations of multi-layered network models evolving towards input-output goals
In the linear model , the total input-output relationship of the network is given by the product of the matrices A1, A2,.
linear model is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Adrien Wohrer, Christian K. Machens
Experimental measures of behavior and neural activities
The linear model builds a continuous-valued, internal percept§ of stimulus value by the animal on each trial.
Experimental measures of behavior and neural activities
To emulate the discrimination tasks, we also need to model the animal’s decision policy, which converts the continuous percept§ into a binary choice c. While the linear model is rather universal, the decision model will depend on the specifics of each experimental task.
The linear readout assumption
Even if the real percept formation departs from linearity, fitting a linear model will most likely retain meaningful estimates for the coarse information (temporal scales, number of neurons involved) that we seek to estimate in our work.
linear model is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Miriam C. Klein-Flügge, Steven W. Kennerley, Ana C. Saraiva, Will D. Penny, Sven Bestmann
Effort discounting is concave and differs from delay discounting
Other work has suggested or implicitly used a linear model of effort discounting [31,37], and, more recently, a quadratic function [40].
Effort discounting is concave and differs from delay discounting
Critically, we note that previous studies did not directly compare the performance of the hyperbolic or linear model to any alternative models, and did not dissociate choices involving delay and effort costs.
Exclusion of participants
The second model previously suggested to describe effort discounting [37] is a simple linear model , which implies a constant integration of effort independent of reward amount, i.e., an additional fixed cost AC devalues a reward by the same amount, regardless of whether it is added to a small or a large preexisting effort level:
linear model is mentioned in 3 sentences in this paper.
Topics mentioned in this paper: