ReviewJournal of undergraduate neuroscience education : JUNE : a publication of FUN, Faculty for Undergraduate Neuroscience2018
On the Application of Multivariate Statistical and Data Mining Analyses to Data in Neuroscience.
Review in Journal of undergraduate neuroscience education : JUNE : a publication of FUN, Faculty for Undergraduate Neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Multivariate indicators of disease severity in COVID-19.Scientific reports · 2023Article
- From calcium imaging to graph topology.Network neuroscience (Cambridge, Mass.) · 2022Review
- Hearing loss versus vestibular loss as contributors to cognitive dysfunction.Journal of neurology · 2022Review
- Applications of Multivariate Statistical and Data Mining Analyses to the Search for Biomarkers of Sensorineural Hearing Loss, Tinnitus, and Vestibular Dysfunction.Frontiers in neurology · 2021Article
- A multivariate statistical analysis of the effects of styrene maleic acid encapsulated RL71 in a xenograft model of triple negative breast cancer.Journal of biological methods · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Research in neuroscience, whether at the level of genes, proteins, neurons or behavior, almost always involves the interaction of multiple variables, and yet many areas of neuroscience employ univariate statistical analyses almost exclusively. Since multiple variables often work together to produce a neuronal or behavioral effect, the use of univariate statistical procedures, analyzing one variable at a time, limits the ability of studies to reveal how interactions between different variables may determine a particular outcome. Multivariate statistical and data mining methods afford the opportunity to analyze many variables together, in order to understand how they function as a system, and how this system may change as a result of a disease or a drug. The aim of this review is to provide a succinct guide to 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, which have been used in circumscribed areas of neuroscience research, but which could be used more widely.
Indexed as
Identifiers
30057506PMC6057756What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.