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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.
 Building a Survival Tree
Constructing a survival tree begins...
Survival Curves01:18

Survival Curves

Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
Censoring Survival Data01:09

Censoring Survival Data

Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different reasons...
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time until a...
Requirements for Human Life01:26

Requirements for Human Life

The Earth and its atmosphere have provided humans with air, water, and food, but these are not the only requirements for survival. Humans also require a specific range of temperature and pressure that the Earth and its atmosphere provides.
Oxygen
Atmospheric air is only about 20 percent oxygen, but that oxygen is a key component of the chemical reactions that keep the body alive, including the reactions that produce ATP. Brain cells are susceptible to a lack of oxygen because they require a...
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.

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Related Experiment Video

Updated: Jul 5, 2026

Monitoring Neuronal Survival via Longitudinal Fluorescence Microscopy
07:02

Monitoring Neuronal Survival via Longitudinal Fluorescence Microscopy

Published on: January 19, 2019

Unix survival guide.

Lincoln D Stein1

  • 1Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, USA.

Current Protocols in Bioinformatics
|April 23, 2008
PubMed
Summary
This summary is machine-generated.

This guide introduces novice users to the Unix environment, essential for running bioinformatics software on systems like Linux and Mac OSX. Learn fundamental Unix commands for effective bioinformatics data analysis.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Bioinformatics software commonly operates on Unix-based operating systems (Linux, Mac OSX, Solaris).
  • Novice users often require foundational knowledge of Unix to utilize these tools effectively.

Purpose of the Study:

  • To provide essential Unix survival information for beginners.
  • To facilitate the use of bioinformatics software in a Unix environment.

Main Methods:

  • Introduction to fundamental Unix commands.
  • Explanation of common Unix concepts relevant to bioinformatics.

Main Results:

  • Users will gain confidence navigating and operating within a Unix environment.
  • Improved accessibility to bioinformatics software for new researchers.

Conclusions:

  • A basic understanding of Unix is crucial for modern bioinformatics.
  • This appendix serves as a practical entry point for aspiring bioinformaticians.