Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

The Scientific Method03:50

The Scientific Method

Chemistry is an empirical science. Scientists often pose questions to understand the chemistry in everyday life and seek answers to these questions. To achieve this, scientists follow a definitive series of steps that together make up the Scientific Method. This approach involves making observations, asking questions, building a hypothesis, conducting experiments, analyzing results, and forming a conclusion.
The Scientific Method02:40

The Scientific Method

Research is what makes the difference between facts and opinions. Facts are observable realities, and opinions are personal judgments, conclusions, or attitudes that may or may not be accurate. In the scientific community, facts can be established only using evidence collected through empirical research.
The Scientific Method01:32

The Scientific Method

The scientific method is a detailed, empirical problem-solving process used by biologists and other scientists. This iterative approach involves formulating a question based on observation, developing a testable potential explanation for the observation (called a hypothesis), making and testing predictions based on the hypothesis, and using the findings to create new hypotheses and predictions.Generally, predictions are tested using carefully-designed experiments. Based on the outcome of these...
Scientific Laws and Theories02:31

Scientific Laws and Theories

Scientific Laws
Next-generation Sequencing03:00

Next-generation Sequencing

The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features.
Emission Spectra02:39

Emission Spectra

When solids, liquids, or condensed gases are heated sufficiently, they radiate some of the excess energy as light. Photons produced in this manner have a range of energies, and thereby produce a continuous spectrum in which an unbroken series of wavelengths is present.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Open science and Big Data in South Africa.

Frontiers in research metrics and analytics·2022
Same author

The Role of Digital Technologies in Responding to the Grand Challenges of the Natural Environment: The Windermere Accord.

Patterns (New York, N.Y.)·2021
Same author

Machine learning and big scientific data.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2020
Same author

Computer science. Beyond the data deluge.

Science (New York, N.Y.)·2009
Same author

Cyberinfrastructure for e-Science.

Science (New York, N.Y.)·2005
Same author

e-Science and its implications.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2003
Same journal

The Case for Capitation.

Harvard business review·2016
Same journal

How to Pay for Health Care.

Harvard business review·2016
Same journal

How to Preempt Team Conflict.

Harvard business review·2016
Same journal

The Secrets of Great Teamwork.

Harvard business review·2016
Same journal

Leading the Team You Inherit.

Harvard business review·2016
Same journal

Wicked Problem Solvers.

Harvard business review·2016
See all related articles

Related Experiment Video

Updated: Jun 7, 2026

Microbial Communities in Nature and Laboratory - Interview
29:13

Microbial Communities in Nature and Laboratory - Interview

Published on: May 28, 2007

The next scientific revolution.

Tony Hey1

  • 1Microsoft Research, Redmond, Washington, USA.

Harvard Business Review
|November 6, 2010
PubMed
Summary
This summary is machine-generated.

Machine learning enables computers to analyze vast data for scientific discovery. This approach aids in predicting patient readmissions and offers insights across various fields.

More Related Videos

A Practical Guide to Phage- and Robotics-Assisted Near-Continuous Evolution
05:08

A Practical Guide to Phage- and Robotics-Assisted Near-Continuous Evolution

Published on: January 12, 2024

Demonstration of a Hyperlens-integrated Microscope and Super-resolution Imaging
10:01

Demonstration of a Hyperlens-integrated Microscope and Super-resolution Imaging

Published on: September 8, 2017

Related Experiment Videos

Last Updated: Jun 7, 2026

Microbial Communities in Nature and Laboratory - Interview
29:13

Microbial Communities in Nature and Laboratory - Interview

Published on: May 28, 2007

A Practical Guide to Phage- and Robotics-Assisted Near-Continuous Evolution
05:08

A Practical Guide to Phage- and Robotics-Assisted Near-Continuous Evolution

Published on: January 12, 2024

Demonstration of a Hyperlens-integrated Microscope and Super-resolution Imaging
10:01

Demonstration of a Hyperlens-integrated Microscope and Super-resolution Imaging

Published on: September 8, 2017

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Data Science

Background:

  • Traditional AI struggled to replicate human expert creativity.
  • Advancements in computing power and data availability are key.

Purpose of the Study:

  • To introduce machine learning as a new paradigm for scientific discovery.
  • To demonstrate the practical application of machine learning in healthcare.

Main Methods:

  • Utilizing machine learning algorithms to process and analyze large datasets.
  • Developing predictive models by learning patterns from historical patient data.

Main Results:

  • Successfully predicted patient readmission risk for congestive heart failure with high accuracy.
  • Identified patient profiles highly likely to be rehospitalized.

Conclusions:

  • Machine learning offers a powerful new tool for scientific breakthroughs.
  • Predictive models can optimize healthcare interventions and reduce costs.
  • Machine learning has broad applications in diverse fields like oceanography, conservation, and business.