相关实验视频

Updated: Jul 1, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

713

修正:基于特征建模和集体学习的新框架,用于预测查询性能.

Mohamed Zaghloul, Mofreh Salem, Amr Ali-Eldin

    PloS one
    |March 4, 2024
    PubMed
    概括

    这项研究纠正了之前的一篇文章DOI. 修正后的DOI为10.1371/journal.pone.0258439.02571/journal.pone.0258439.02571/journal.pone.0258439.02571/journal.pone.0258439.02571/journal.pone.0258439.

    更多相关视频

    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances07:35

    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

    Published on: October 11, 2018

    7.5K
    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness03:14

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

    Published on: December 6, 2024

    555

    相关概念视频

    End Point Prediction: Gran Plot01:07

    End Point Prediction: Gran Plot

    A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
    For potentiometric titration, the Gran plot is created by plotting...
    324
    Prediction Intervals01:03

    Prediction Intervals

    The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
    However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
    2.3K
    Aggregates Classification01:29

    Aggregates Classification

    Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
    Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
    321
    Improving Translational Accuracy02:07

    Improving Translational Accuracy

    Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
    10.4K
    Per-Unit Sequence Models01:26

    Per-Unit Sequence Models

    An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
    Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
    74
    Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

    Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

    Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
    The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
    69