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Related Concept Videos

Dreaming01:30

Dreaming

Sigmund Freud revolutionized our understanding of dreams by proposing that they are a window into the unconscious mind. According to Freud, dreams are not mere stories our minds create while we sleep but are profoundly meaningful narratives about our hidden desires and fears. He introduced two key concepts: manifest content and latent content. The manifest content is the actual content and imagery of the dream — what we remember when we wake up. The latent content, however, represents the...
Nightmares and Night Terrors01:18

Nightmares and Night Terrors

Nightmares and night terrors represent two distinct types of sleep disturbances that differ in timing, characteristics, and the sleeper's recall of the event. Nightmares are vivid, disturbing dreams that usually awaken the sleeper from REM sleep, a stage of sleep where brain activity is high, and dreams are most frequent. Upon awakening, individuals often have detailed recollections of their nightmares, which can include themes of threats to survival, security, or self-esteem.
Nightmares often...
Lucid Dreaming01:10

Lucid Dreaming

Lucid dreaming is a unique state of consciousness where an individual realizes they are dreaming while still in the dream. This awareness allows them to manipulate their dream environment consciously. Researchers like Stephen LaBerge have significantly contributed to the understanding of lucid dreams, highlighting that during these dreams, certain areas of the brain, such as the prefrontal cortex, that involve self-awareness and thought evaluation show increased activity.
Studies have shown...
Hindsight Biases01:12

Hindsight Biases

Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now?
Cognitive Learning01:21

Cognitive Learning

Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
Cognitive Dissonance01:38

Cognitive Dissonance

Social psychologists have documented that feeling good about ourselves and maintaining positive self-esteem is a powerful motivator of human behavior (Tavris & Aronson, 2008). In the United States, members of the predominant culture typically think very highly of themselves and view themselves as good people who are above average on many desirable traits (Ehrlinger, Gilovich, & Ross, 2005). Often, our behavior, attitudes, and beliefs are affected when we experience a threat to our...

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Lessons from the DREAM2 Challenges.

Gustavo Stolovitzky1, Robert J Prill, Andrea Califano

  • 1IBM Computational Biology Center, Yorktown Heights, New York, USA. gustavo@us.ibm.com

Annals of the New York Academy of Sciences
|April 8, 2009
PubMed
Summary
This summary is machine-generated.

Computational methods require experimental validation for true worth. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) project established challenges to systematically assess algorithm performance in systems biology network reconstruction.

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Algorithm performance is best proven by experimental validation, but most predictions remain untested.
  • Limited experimental validation of computational models provides an incomplete picture of algorithm strengths and weaknesses.
  • Experimental biologists need reliable methods to select appropriate computational tools.

Purpose of the Study:

  • To introduce the Dialogue for Reverse Engineering Assessments and Methods (DREAM) project's systematic challenges.
  • To provide a framework for evaluating computational methods in systems biology.
  • To address the gap in understanding algorithm performance through rigorous assessment.

Main Methods:

  • The DREAM project created challenges using donated biological data.
  • Data was curated for network reconstruction problems with withheld solutions.
  • Five distinct challenges were developed for the systems biology community.

Main Results:

  • A global comparison of algorithm submissions was conducted.
  • The performance of various computational strategies was evaluated.
  • Best-performing strategies for network reconstruction were identified and discussed.

Conclusions:

  • Systematic challenges like DREAM2 are crucial for validating computational methods.
  • Understanding algorithm performance requires comprehensive assessment beyond limited testing.
  • The DREAM project facilitates the advancement of systems biology through rigorous evaluation of computational tools.