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Related Concept Videos

Surveys02:16

Surveys

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Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally. Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.
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Introduction to Surveying, Plane Surveying and Geodetic Surveys01:27

Introduction to Surveying, Plane Surveying and Geodetic Surveys

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Surveying is the art and science of mapping the earth's surface. It involves measuring distances, angles in horizontal or vertical directions, and levels to understand the shape and size of land features. Surveying techniques are essential for various tasks, such as identifying the levels of a land area with reference to a specific point, and mapping undulations and water bodies.There are two main types of surveying: plane surveys and geodetic surveys. Plane surveys assume the earth is flat,...
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Errors and Mistakes in Surveying01:19

Errors and Mistakes in Surveying

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Errors and mistakes in surveying refer to inaccuracies in measurements and data recording. The errors are deviations from the actual value caused by human sensory limitations, equipment flaws, or environmental effects. These errors are typically unintentional and can result from the inherent imperfections in the instruments used, atmospheric conditions, or the observer’s inability to perceive exact measurements. On the other hand, mistakes are caused by the surveyor's lack of...
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Types of Surveys01:27

Types of Surveys

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Surveys are essential for marking property boundaries near water bodies. Different types of surveys are defined, each with its own function. Land surveys mark the property boundaries, while route surveys determine the position of properties on nearby highways. Topographic surveys create maps by capturing the three-dimensional features of the land. Hydrographic surveys focus on the shapes of underwater areas and the movement of streams through the properties. Mine surveys determine the relative...
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Survey Safety01:28

Survey Safety

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Surveying near highways, rough terrain, or power lines involves significant risks. Working along highways is particularly dangerous and requires the use of warning signs and flagmen. It is safest to avoid working directly on roads and use offsets whenever possible. When highway work is unavoidable, it must follow all safety guidelines. Surveyors should wear bright clothing, such as orange reflective vests, to ensure visibility to motorists, coworkers, and hunters. In construction zones, wearing...
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Guidelines for Writing Outcome01:11

Guidelines for Writing Outcome

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When developing expected outcomes for a patient care plan, the nurse should adhere to the following recommendations:
Patient outcomes reflect the patient's response to the goal rather than what the nurse aims to achieve. Terminology should be observable and measurable to avoid the reader's interpretation. The desired outcome should be realistic and achievable in the designated care timeframe. Expected outcomes should align with adjunctive therapies. The outcome should enhance care...
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Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
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Deep learning in omics: a survey and guideline.

Zhiqiang Zhang1, Yi Zhao2, Xiangke Liao1

  • 1School of Computer Science, National University of Defense Technology, Changsha, China.

Briefings in Functional Genomics
|September 29, 2018
PubMed
Summary

Deep learning effectively addresses challenges posed by big data in omics research, such as genomics and proteomics. This survey guides researchers in applying deep learning (DL) to complex omics data analysis.

Keywords:
bioinformaticsdeep learninggeneneural networkomics

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Science

Background:

  • Omics data (genomics, transcriptome, proteomics) generates high-dimensional, complex datasets.
  • Conventional machine learning struggles with the scale and complexity of modern omics data.
  • Deep learning offers advanced capabilities to analyze large-scale omics information.

Purpose of the Study:

  • To provide an introductory guideline for researchers on utilizing deep learning for omics problems.
  • To bridge the gap between deep learning methodologies and omics data analysis.
  • To facilitate the adoption of deep learning in omics research.

Main Methods:

  • Introduction to various deep learning models relevant to omics data.
  • Discussion of research areas integrating omics and deep learning.
  • Systematic summary of general steps for applying deep learning in omics.
  • Comparative analysis of open-source deep learning frameworks.

Main Results:

  • Deep learning models demonstrate efficacy in handling and resolving omics data challenges.
  • Identification of key research applications combining omics and deep learning.
  • A structured approach to implementing deep learning for omics analysis is outlined.
  • Evaluation of current deep learning frameworks for omics applications.

Conclusions:

  • Deep learning is a powerful tool for advancing omics research in the big data era.
  • This survey serves as a foundational resource for omics researchers new to deep learning.
  • Understanding and applying deep learning will unlock new insights from complex biological data.