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Related Concept Videos

Types of Selection01:46

Types of Selection

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Natural selection influences the frequencies of particular alleles and phenotypes within populations in several different ways. Primarily, natural selection can be directional, stabilizing, or disruptive. Directional selection favors one extreme trait and shifts the population towards that phenotype while selecting against individuals displaying alternate traits. Stabilizing selection favors an intermediate trait with a narrow range of variation. Deviation from the optimal phenotype towards an...
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Quantifying and Rejecting Outliers: The Grubbs Test01:02

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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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Statistical Software for Data Analysis and Clinical Trials01:12

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Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
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Law of Independent Assortment02:03

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While Mendel’s Law of Segregation states that the two alleles for one gene are separated into different gametes, a different question of how different genes are inherited remains. For example, is the gene for tall plants inherited with the gene for green peas? Mendel asked this question by experimenting with a dihybrid cross; a cross in which both parents are homozygous for two distinct traits resulting in an F1 generation that are heterozygous for both traits.
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Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

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Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
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Random Sampling Method01:09

Random Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Selector: A General Python Library for Diverse Subset Selection.

Fanwang Meng1,2, Marco Martínez González2, Valerii Chuiko2

  • 1Department of Chemistry, Queen's University, 90 Bader Lane, Kingston, Ontario, Canada, K7L 3N6.

Biorxiv : the Preprint Server for Biology
|December 3, 2025
PubMed
Summary
This summary is machine-generated.

Selector is a free, open-source Python library for diverse subset selection. It offers various sampling algorithms and integrates with Scikit-Learn for broad applications in data analysis and scientific discovery.

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Area of Science:

  • Data Science
  • Computational Chemistry
  • Drug Discovery

Background:

  • Selecting diverse subsets from large datasets is crucial for efficient analysis and discovery.
  • Existing tools may lack flexibility or comprehensive algorithms for diverse subset selection.

Purpose of the Study:

  • To introduce Selector, a free, open-source Python library for selecting diverse subsets.
  • To provide a versatile tool with multiple sampling algorithms and diversity metrics.
  • To facilitate integration with existing data analysis workflows.

Main Methods:

  • Implementation of subset sampling algorithms based on distance, similarity, and spatial partitioning.
  • Quantification of subset diversity using implemented metrics.
  • Integration with Scikit-Learn and development of an accessible web interface.

Main Results:

  • Selector offers a flexible and extensible package for diverse subset selection.
  • The library supports various applications, including computational chemistry and drug discovery.
  • User-friendly tutorials and a no-code web interface enhance accessibility.

Conclusions:

  • Selector provides a robust and accessible solution for diverse subset selection.
  • Its design promotes interoperability and maintainability through modern software practices.
  • The library empowers users across different skill levels to leverage advanced subset selection techniques.