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Related Concept Videos

How Data are Classified: Numerical Data00:59

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Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
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How Data are Classified: Categorical Data01:11

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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
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Classifying Matter by Composition03:35

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Matter: Pure Substances and Mixtures
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Chemistry is the study of matter and the changes it undergoes. Matter is anything that has mass and occupies space. Matter is all around us; the air, water, soil, mountains, even our bodies are all examples of matter. Matter is divided into three states — solid, liquid, and gas — that are commonly found on earth. The fourth state of matter, plasma, occurs naturally in the interiors of stars. 
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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.
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Model Approaches for Pharmacokinetic Data: Physiological Models01:15

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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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Dissection of Drosophila melanogaster Flight Muscles for Omics Approaches
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A dropout-regularized classifier development approach optimized for precision medicine test discovery from omics

Joanna Roder1, Carlos Oliveira2, Lelia Net2

  • 1Biodesix Inc, 2970 Wilderness Pl, Ste100, Boulder, CO, 80301, USA. joanna.roder@biodesix.com.

BMC Bioinformatics
|June 15, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a novel machine learning approach for developing precision medicine tests using genomic and proteomic data. The method effectively creates clinically useful diagnostic tests and provides reliable performance estimates, even with limited patient data.

Keywords:
BoostingEnsemble averageMachine LearningMolecular diagnosticsRegularization

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Genomic and proteomic data offer potential for patient care improvements.
  • Designing precision medicine tests from limited data is challenging.
  • Reliable early-stage performance assessment of new tests is problematic.

Purpose of the Study:

  • To present a novel classifier development approach for creating clinically useful precision medicine tests.
  • To enable reliable performance estimates early in test development.
  • To address challenges of small patient cohorts and limited data.

Main Methods:

  • A novel dropout-regularized combination approach integrating traditional and modern machine learning.
  • Hierarchical classification and information abstraction.
  • Boosting, bagging, and strong dropout regularization.

Main Results:

  • The new method achieved performance similar to or better than Random Forest in two oncology classification tasks.
  • Effectively generated a classifier in a task with a known confounding variable.
  • Provided reliable test performance estimates from small development sample sets.

Conclusions:

  • The flexible dropout-regularized combination approach yields tailored molecular diagnostic tests.
  • Mitigates known confounding effects in classifier development.
  • Facilitates reliable early-stage assessment of test fitness-for-purpose.