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Related Concept Videos

Regulation of Expression at Multiple Steps01:23

Regulation of Expression at Multiple Steps

The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the addition of a...
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Regulation of Expression Occurs at Multiple Steps

Gene expression can be regulated at almost every step from gene to protein. Transcription is the step that is most commonly regulated. This involves the binding of proteins to short regulatory sequences on the DNA. This association can either promote or inhibit the transcription of a gene associated with the respective sequence.
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Regulation of Expression Occurs at Multiple Steps02:24

Regulation of Expression Occurs at Multiple Steps

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Combinatorial Gene Control

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Constitutive and Regulated Gene Expression01:27

Constitutive and Regulated Gene Expression

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Sequence Networks of Rotating Machines

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Integration of gene normalization stages and co-reference resolution using a Markov logic network.

Hong-Jie Dai1, Yen-Ching Chang, Richard Tzong-Han Tsai

  • 1Department of Computer Science, National Tsing-Hua University, Hsinchu, Taiwan, ROC. hongjie@iis.sinica.edu.tw

Bioinformatics (Oxford, England)
|June 21, 2011
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Summary

This study introduces a novel Markov logic network (MLN) approach for gene normalization (GN), improving accuracy by interactively modeling constraints. The new method outperforms existing systems in identifying unique gene database IDs from text.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Natural Language Processing

Background:

  • Gene normalization (GN) links textual gene mentions to unique database IDs.
  • Current GN systems use separate stages for constraints like filtering and disambiguation, limiting interactive improvements.
  • Existing methods struggle with interactive constraint modeling for enhanced gene normalization.

Purpose of the Study:

  • To propose a novel approach for gene normalization using Markov logic networks (MLNs).
  • To integrate various constraints interactively within a unified MLN framework for GN.
  • To introduce co-reference resolution concepts (discourse salience, transitivity) into gene normalization models.

Main Methods:

  • Formulating and combining diverse constraints within a Markov logic network (MLN).
  • Applying discourse salience (centering theory) and transitivity from co-reference resolution to GN.
  • Utilizing instance-based and article-wide precision/recall/F-measure (PRF) for performance evaluation.

Main Results:

  • The proposed MLN-based system demonstrates superior performance compared to baseline and state-of-the-art GN systems.
  • The system achieved better results under two distinct evaluation schemes.
  • Further analysis revealed previously unaddressed challenges within the gene normalization task.

Conclusions:

  • The MLN approach effectively models and integrates constraints for improved gene normalization.
  • Incorporating co-reference resolution principles enhances the capabilities of gene normalization models.
  • The developed system offers a significant advancement for information extraction applications in bioinformatics.