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Related Concept Videos

Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

570
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
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Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

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Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
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Weighted Mean00:57

Weighted Mean

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While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
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Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

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Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

249
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...
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Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

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Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
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Related Experiment Video

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TOBMI: trans-omics block missing data imputation using a k-nearest neighbor weighted approach.

Xuesi Dong1,2, Lijuan Lin1, Ruyang Zhang1,3

  • 1Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China.

Bioinformatics (Oxford, England)
|September 12, 2018
PubMed
Summary

We developed TOBMIkNN, a novel imputation method to address missing RNA-seq data in trans-omics studies. This approach effectively uses DNA methylation data, outperforming existing methods for improved data integration and analysis.

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Area of Science:

  • Genomics and Bioinformatics
  • Cancer Research
  • Data Integration

Background:

  • Trans-omics data integration is crucial for understanding complex biological mechanisms in cancer.
  • A significant challenge is the 'block missing' data phenomenon, where individuals lack specific omics datasets, hindering analysis.

Purpose of the Study:

  • To address the challenge of block missing data in trans-omics datasets.
  • To propose and evaluate a novel imputation method for RNA-seq data using DNA methylation data.

Main Methods:

  • Developed TOBMIkNN, a k-nearest neighbor weighted imputation method for trans-omics block missing data.
  • Utilized external information from DNA methylation probe datasets to impute missing RNA-seq data.
  • Compared TOBMIkNN against multi-hot deck, mean imputation, and missing case deletion methods.

Main Results:

  • TOBMIkNN demonstrated superior performance in imputing RNA-seq data by leveraging DNA methylation information.
  • The method showed significant improvements in reducing imputation error (relative and absolute).
  • TOBMIkNN maintained the inter-omics correlation structure more effectively than other methods.

Conclusions:

  • TOBMIkNN is a reliable and effective method for imputing block missing data in trans-omics studies.
  • The proposed method offers advantages in imputation accuracy and preservation of data structure.
  • TOBMIkNN is recommended for handling missing data in integrated omics analyses, particularly in cancer research.