Results 11 to 20 of about 5,700,897 (319)
Statistical Workflow for Feature Selection in Human Metabolomics Data. [PDF]
High-throughput metabolomics investigations, when conducted in large human cohorts, represent a potentially powerful tool for elucidating the biochemical diversity underlying human health and disease.
Antonelli, Joseph +17 more
core +3 more sources
Statistical Foundations of Prior-Data Fitted Networks [PDF]
Prior-data fitted networks (PFNs) were recently proposed as a new paradigm for machine learning. Instead of training the network to an observed training set, a fixed model is pre-trained offline on small, simulated training sets from a variety of tasks ...
T. Nagler
semanticscholar +1 more source
Microbiome studies have become routine in biomedical, agricultural and environmental sciences with diverse aims, including diversity profiling, functional characterization, and translational applications. The resulting complex, often multi-omics datasets
Yao Lu +5 more
semanticscholar +1 more source
It is incredibly essential that the current clinicians and researchers remain updated with findings of current biomedical literature for evidence-based medicine.
Hunny Sharma
semanticscholar +1 more source
BIOMEX: an interactive workflow for (single cell) omics data interpretation and visualization
The amount of biological data, generated with (single cell) omics technologies, is rapidly increasing, thereby exacerbating bottlenecks in the data analysis and interpretation of omics experiments.
Federico Taverna +10 more
semanticscholar +1 more source
Networks and Graphs Discovery in Metabolomics Data Analysis and Interpretation
Both targeted and untargeted mass spectrometry-based metabolomics approaches are used to understand the metabolic processes taking place in various organisms, from prokaryotes, plants, fungi to animals and humans. Untargeted approaches allow to detect as
A. Amara +9 more
semanticscholar +1 more source
Equivalent statistics and data interpretation [PDF]
Recent reform efforts in psychological science have led to a plethora of choices for scientists to analyze their data. A scientist making an inference about their data must now decide whether to report a p value, summarize the data with a standardized effect size and its confidence interval, report a Bayes Factor, or use other model comparison methods.
openaire +2 more sources
Comprehensive guidelines for appropriate statistical analysis methods in research [PDF]
Background The selection of statistical analysis methods in research is a critical and nuanced task that requires a scientific and rational approach. Aligning the chosen method with the specifics of the research design and hypothesis is paramount, as it ...
Jonghae Kim +2 more
doaj +1 more source
Common pitfalls in statistical analysis: Clinical versus statistical significance
In clinical research, study results, which are statistically significant are often interpreted as being clinically important. While statistical significance indicates the reliability of the study results, clinical significance reflects its impact on ...
Priya Ranganathan +2 more
doaj +1 more source
Testing Linear Regressions by StatsModel Library of Python for Oceanological Data Interpretation
The study area is focused on the Mariana Trench, west Pacific Ocean. The research aim is to investigate correlation between various factors, such as bathymetric depths, geomorphic shape, geographic location on four tectonic plates of the sampling points ...
Polina Lemenkova
semanticscholar +1 more source

