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Nuclear Fusion02:45

Nuclear Fusion

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The process of converting very light nuclei into heavier nuclei is also accompanied by the conversion of mass into large amounts of energy, a process called fusion. The principal source of energy in the sun is a net fusion reaction in which four hydrogen nuclei fuse and ultimately produce one helium nucleus and two positrons.
A helium nucleus has a mass that is 0.7% less than that of four hydrogen nuclei; this lost mass is converted into energy during the fusion. This reaction produces about...
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Proteins are involved in several cellular processes and biochemical reactions. Analyzing a specific protein of interest requires it to be isolated from the other proteins in the cell. This is achieved by overexpressing the specific gene in a suitable host to produce large quantities of the target protein. A tag or label is recombined with the gene to produce a fusion protein containing the target protein and the tag. The tags on these fusion proteins can then be used for easy detection and...
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Once a transport vesicle has recognized its target organelle, the vesicular membrane needs to fuse with the target membrane to unload the cargo. Transmembrane proteins called SNAREs present on organelle membranes and their vesicles, mediate vesicle fusion.
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Proteins and neurotransmitters in secretory vesicles can be released from a cell upon vesicle docking, priming, and fusion with the plasma membrane. Vesicles are docked and primed in preparation for the quick exocytosis of their contents in response to a stimulus. The fusion process is mainly carried out by a SNAP Receptor or SNARE complex, consisting of synaptobrevin, syntaxin-1, and SNAP-25.
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Structure of a Gene01:30

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A gene is the fundamental unit of heredity. Every individual has two copies of each gene, one inherited from each parent. Although most people contain the same genes, there is a small fraction that is slightly different amongst people. A gene with a small difference in its sequence of DNA bases forms different alleles, contributing to different phenotypes.
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Prokaryotic genomes exhibit a streamlined organization of coding and non-coding regions essential for gene expression and protein synthesis. While coding regions contain the genetic instructions for proteins or functional RNAs, non-coding regions regulate the precise transcription and translation of these genes.Coding Regions: Proteins and RNAsThe primary coding regions, known as structural genes, include sequences transcribed into messenger RNA (mRNA) and ultimately translated into...
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Oncogenic Gene Fusion Detection Using Anchored Multiplex Polymerase Chain Reaction Followed by Next Generation Sequencing
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Synstable Fusion: A Network-Based Algorithm for Estimating Driver Genes in Fusion Structures.

Mingzhe Xu1,2,3, Zhongmeng Zhao4,5, Xuanping Zhang6,7

  • 1Department of Computer Science and Technology, School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China. mingzhe.xu@hnuahe.edu.cn.

Molecules (Basel, Switzerland)
|August 18, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces Synstable Fusion (SYN), a novel algorithm for identifying driver genes in cancer gene fusions. SYN balances evaluation strategies and integrates multiple networks for more comprehensive and robust results in cancer genomics.

Keywords:
evaluating driver partnergene fusion datagene networksgene susceptibility prioritization

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Area of Science:

  • Genomics
  • Computational Biology
  • Cancer Research

Background:

  • Gene fusions are common somatic mutations in cancer genomes.
  • Identifying driver genes in fusions is crucial for cancer analysis and clinical practice.
  • Existing computational methods have limitations in handling gene fusion data, including representation, network integration, and evaluation.

Purpose of the Study:

  • To propose Synstable Fusion (SYN), a novel algorithm for computationally evaluating cancer gene fusions.
  • To improve the identification of driver genes within fusion structures.
  • To address the weaknesses of existing methods in gene fusion analysis.

Main Methods:

  • Developed the Synstable Fusion (SYN) algorithm using a network-based strategy and the destructiveness hypothesis.
  • Integrated multiple gene networks using a machine learning framework to resolve conflicting results.
  • Established a synchronous stability model to reduce computational complexity.

Main Results:

  • The proposed SYN algorithm demonstrates robust performance across various configurations and parameter settings.
  • Experiments on artificial and real datasets validate the effectiveness of SYN.
  • The destructiveness hypothesis provides a balanced approach to evaluating fusion genes.

Conclusions:

  • Synstable Fusion (SYN) offers a more comprehensive and robust method for identifying driver genes in cancer gene fusions.
  • The integration of multiple networks and the synchronous stability model enhance computational efficiency and accuracy.
  • SYN has the potential to contribute significantly to downstream cancer genomics analyses and clinical applications.