A comparison of random forest regression and multiple linear regression for prediction in neuroscience

@article{Smith2013ACO,
  title={A comparison of random forest regression and multiple linear regression for prediction in neuroscience},
  author={Paul F. Smith and Siva Ganesh and Ping Liu},
  journal={Journal of Neuroscience Methods},
  year={2013},
  volume={220},
  pages={85-91}
}
On the Application of Multivariate Statistical and Data Mining Analyses to Data in Neuroscience.
  • Paul F. Smith
  • Biology
    Journal of undergraduate neuroscience education : JUNE : a publication of FUN, Faculty for Undergraduate Neuroscience
  • 2018
TLDR
Methods such as linear discriminant analysis, support vector machines, principal component and factor analysis, cluster analysis, multiple linear regression, and random forest regression and classification are reviewed to provide a succinct guide to methods used in circumscribed areas of neuroscience research, but which could be used more widely.
Applications of Multivariate Statistical and Data Mining Analyses to the Search for Biomarkers of Sensorineural Hearing Loss, Tinnitus, and Vestibular Dysfunction
TLDR
The use of multivariate statistical and data mining methods provides the opportunity to analyse many variables together, in order to appreciate how they may function as a system of interacting variables, and how this system or network may change as a result of sensory disorders such as sensorineural hearing loss, tinnitus or different types of vestibular dysfunction.
Age-Related Neurochemical Changes in the Vestibular Nuclei
TLDR
It is concluded that, at present, it is difficult, if not impossible, to relate the neurochemical changes observed to the function of specific VNC neurons and whether the observed changes are the cause of a functional deficit in the VNC or an effect of it.
Quantifying the Reproducibility of Graph Neural Networks using Multigraph Brain Data
TLDR
A framework for GNN reproducibility assessment via the quantification of the most discriminative features shared between different models is proposed, which could pave the way for the development of biomarker trustworthiness and reliability assessment methods for computeraided diagnosis and prognosis tasks.
Multitrait machine‐ and deep‐learning models for genomic selection using spectral information in a wheat breeding program
TLDR
Overall, this study concluded that machine‐ and deep‐learning‐based MT‐GS models increased prediction accuracy and should be employed in large‐scale breeding programs.
Machine Learning Tools to Assess the Impact of COVID-19 Civil Measures in Atmospheric Pollution
TLDR
This work research the relation between the COVID-19 measures and the Air Quality Index (AQI), using four pollutant gases (CO, O3, NO2, SO2), using a variety of machine learning tools to estimate the accuracy of each method in the prediction of the concentration for each gas one week later.
Using Random Forest Regression to Determine Influential Force-Time Metrics for Countermovement Jump Height: A Technical Report
Abstract Merrigan, JJ, Stone, JD, Wagle, JP, Hornsby, WG, Ramadan, J, Joseph, M, and Hagen, JA. Using random forest regression to determine influential force-time metrics for countermovement jump
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 27 REFERENCES
Pathway analysis using random forests classification and regression
TLDR
A pathway-based classification and regression method using Random Forests to analyze gene expression data and can provide biological insight into the study of microarray data is described.
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition
TLDR
This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression and path algorithms for the lasso, non-negative matrix factorization, and spectral clustering.
Artificial neural network posturography detects the transition of vestibular neuritis to phobic postural vertigo
TLDR
The ANNW-posturography method was able to disclose the transition that a patient with acute vestibular neuritis underwent to phobic postural vertigo within months after disease onset and confirmed the patient’s recovery from static postural control despite the permanent unilateral Vestibular loss.
Hippocampal nitric oxide synthase and arginase and age‐associated behavioral deficits
TLDR
Correlation analysis revealed that animals' motor ability was associated with CA1 NOS and arginase, as well as hippocampal function, and a strong positive correlation between CA1 eNOS protein expression and swimming speed in the water maze task may reflect a relationship between the local cerebral blood flow and neuronal activity.
Nitric oxide synthase and arginase in the rat hippocampus and the entorhinal, perirhinal, postrhinal, and temporal cortices: Regional variations and age‐related changes
Increasing evidence suggests that nitric oxide synthase (NOS)/nitric oxide (NO) contributes to the aging process. By contrast, the role of arginase, which shares a common substrate with NOS, has not
...
1
2
3
...