Index of papers in PLOS Comp. Biol. that mention

**linear model**

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:

- cytokine (16)
- principal components (16)
- machine learning (13)

2. Transcriptional Dynamics Reveal Critical Roles for Non-coding RNAs in the Immediate-Early Response

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:

- time course (23)
- time series (19)
- transcription factors (15)

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:

- input-output (11)
- neural networks (8)
- simulation results (8)

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:

- spike trains (12)
- experimental data (10)
- decision-making (8)

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:

- effort costs (17)
- Bayesian model (11)
- parameter estimates (10)