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Biostatistics: Overview01:20

Biostatistics: Overview

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Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
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Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
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Machine learning and deep analytics for biocomputing: call for better explainability.

Dragutin Petkovic1, Lester Kobzik, Christopher Re

  • 1Computer Science Department, San Francisco State University (SFSU), 1600 Holloway Ave, San Francisco, CA 94132, USA, Petkovic@sfsu.edu.

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|December 9, 2017
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Summary
This summary is machine-generated.

Improving explainability in Machine Learning and Deep Analytics (MLDA) is crucial for biocomputing. This workshop addresses challenges and seeks solutions for transparent MLDA decision-making in healthcare and research.

Area of Science:

  • Biocomputing
  • Machine Learning and Deep Analytics (MLDA)
  • Medical Informatics

Background:

  • The increasing reliance on MLDA in biocomputing necessitates greater transparency.

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  • Complex biological data and algorithms challenge current MLDA explainability.
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