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MicroRNA (miRNA) are short, regulatory RNA transcribed from introns (non-coding regions of a gene) or intergenic regions (stretches of DNA present between genes). Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself, forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After the pre-miRNA...
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MicroRNA (miRNA) are short, regulatory RNA transcribed from introns—non-coding regions of a gene—or intergenic regions—stretches of DNA present between genes. Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After...
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Logical Reasoning (Inferencing) on MicroRNA Data.

Jingsong Wang1

  • 1Oracle Corporation, 500 Oracle Parkway, Redwood Shores, CA, 94065, USA. jingsong.wang@oracle.com.

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|May 26, 2017
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Summary

This study introduces applying logical reasoning to microRNA data analysis for enhanced artificial intelligence. It covers propositional logic, automated reasoning, and tools for building microRNA reasoning systems.

Keywords:
Inference ruleLogical reasoningPropositional logic

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Area of Science:

  • Bioinformatics
  • Artificial Intelligence
  • Computational Biology

Background:

  • Logical reasoning is crucial for artificial intelligence.
  • Integrating logical reasoning with microRNA data enhances data analysis intelligence.

Purpose of the Study:

  • To introduce the fundamentals of logic, particularly propositional logic.
  • To explain automated reasoning using logic rules.
  • To present tools for developing automated reasoning systems with microRNA data.

Main Methods:

  • Introduction to propositional logic concepts.
  • Explanation of knowledge representation using logic rules.
  • Overview of tools for automated reasoning system development.

Main Results:

  • Provides a foundational understanding of logic for AI applications.
  • Demonstrates the potential of applying automated reasoning to microRNA data.
  • Identifies relevant tools for building such systems.

Conclusions:

  • Logical reasoning offers a powerful approach to microRNA data analysis.
  • Automated reasoning systems can be built for microRNA data using available tools.
  • This work facilitates the integration of AI and microRNA research.