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Related Concept Videos

Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Reducing Line Loss01:18

Reducing Line Loss

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Linear Approximations01:23

Linear Approximations

For a differentiable function of two variables, linear approximation estimates values near a known point by replacing the curved surface with its tangent plane. Consider the function\begin{equation*}f(x,y)=x^2+3y^2\end{equation*}near the point (2, 1). The exact value at this point is f(2, 1) = 22 + 3(1)2 = 4 + 3 = 7.The linear approximation of f(x, y)) near (a, b) is\begin{equation*}L(x,y)=f(a,b)+f_x(a,b)(x-a)+f_y(a,b)(y-b)\end{equation*}First, compute the partial derivatives: fx(x, y) = 2x and...
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
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Related Experiment Video

Updated: Jun 13, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

An approximation algorithm for the Noah's Ark problem with random feature loss.

Glenn Hickey1, Mathieu Blanchette, Paz Carmi

  • 1School of Computer Science,McGill University, 3480 University Street, McConnell Engineering Building, Room 318, Montreal, QC H3A 2A7, Canada. hickey@mcb.mcgill.ca

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|May 19, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a new conservation optimization model, the Noah's Ark Problem with Loss (NAPL), accounting for feature loss in phylogenetic diversity. A novel approximation scheme is presented for this complex biodiversity problem.

Related Experiment Videos

Last Updated: Jun 13, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

  • Ecology
  • Conservation Biology
  • Computational Biology

Background:

  • Phylogenetic diversity (PD) is a key biodiversity metric in conservation, measuring evolutionary distinctness.
  • The Noah's Ark Problem (NAP) optimizes taxon selection for maximum PD within a budget, but existing solutions are limited.
  • Current models may not adequately represent information loss along phylogenetic paths.

Purpose of the Study:

  • To generalize the Noah's Ark Problem (NAP) by incorporating a model for feature loss.
  • To address limitations of existing biodiversity optimization models in ecological conservation.
  • To introduce and solve the Noah's Ark Problem with Loss (NAPL).

Main Methods:

  • Generalizing the Noah's Ark Problem (NAP) to include feature loss modeled by an exponential distribution.
  • Developing a pseudopolynomial time approximation scheme for the generalized problem (NAPL).

Main Results:

  • The study presents a novel approximation scheme for the Noah's Ark Problem with Loss (NAPL).
  • This scheme provides a method to address complex conservation scenarios involving feature loss.
  • The work extends previous research on biodiversity optimization problems.

Conclusions:

  • The developed approximation scheme offers a viable computational approach for the NAPL.
  • This research enhances biodiversity conservation planning by incorporating realistic feature loss models.
  • The findings contribute to a more robust understanding of phylogenetic diversity in conservation contexts.