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Related Concept Videos

Protein Families02:47

Protein Families

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Protein families are groups of homologous proteins; that is, they have similarities in amino acid sequences and three-dimensional structures. Protein families usually occur because of gene duplication, where an additional copy of a gene is inserted into the genome of an organism.   Mutations that change the amino acids but still allow the protein to be properly synthesized, will lead to new protein family members.   If these new proteins contain similar amino acids in key...
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Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
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Gene Families01:57

Gene Families

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Gene families consist of groups of genes proposed to have originated from a common ancestor. Typically these arise through events in which a gene or genes are mistakenly duplicated during cell division. Unlike their parent genes (which are subject to selection pressure to maintain function), these gene copies do not need to preserve their sequences and may evolve at a relatively faster rate.
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Selecting protein families for environmental features based on manifold regularization.

Xingpeng Jiang, Weiwei Xu, E K Park

    IEEE Transactions on Nanobioscience
    |May 8, 2014
    PubMed
    Summary

    We developed a new statistical method, manifold-constrained regularization for linear regression (McRe), to understand how microbial communities adapt to different environments using protein and gene functions. This approach can identify key biological features for biosensor development.

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    Area of Science:

    • Microbial ecology
    • Bioinformatics
    • Machine learning

    Background:

    • Statistical and machine learning methods are increasingly used to identify functional or taxonomic features related to environmental conditions or physiological states.
    • Proteins and other biological entities can serve as biosensors for environmental monitoring.
    • A key challenge is understanding how the distribution of protein and gene functions reflects microbial community adaptation across diverse environments.

    Purpose of the Study:

    • To propose a novel regularization method for linear regression to address the challenge of understanding microbial community adaptation.
    • To introduce a method inspired by local linear embedding (LLE) for analyzing functional and taxonomic features.
    • To provide a new tool for identifying important biological entities for biosensor applications.

    Main Methods:

    • A novel regularization method for linear regression, termed manifold-constrained regularization for linear regression (McRe), was developed.
    • The method is inspired by the principles of local linear embedding (LLE).
    • The approach is designed to analyze the distribution of protein and gene functions in relation to environmental adaptation.

    Main Results:

    • The proposed manifold-constrained regularization (McRe) method was demonstrated to be efficient.
    • The performance of McRe was validated using both simulated and real-world biological data.
    • The study highlights the potential of McRe in analyzing complex biological systems.

    Conclusions:

    • The developed manifold-constrained regularization (McRe) method offers a powerful approach to analyze microbial community adaptation.
    • This method can help elucidate the relationship between biological functions and environmental contexts.
    • The McRe technique shows promise for applications beyond the studied problem, including solving other linear systems.