A data parallel strategy for aligning multiple biological sequences on multi-core computers

Faculty Computer Science Year: 2013
Type of Publication: ZU Hosted Pages: 350-361
Authors:
Ahmed Salah Mohamed Mostafa
Journal: Computers in Biology and Medicine Computers in Biology and Medicine Volume: 4
Keywords : , data parallel strategy , aligning multiple biological    
Abstract:
In this paper, we address the large-scale biological sequence alignment problem, which has an increasing demand in computational biology. We employ data parallelism paradigm that is suitable for handling large-scale processing on multi-core
    
     
 
       

Author Related Publications

  • Ahmed Salah Mohamed Mostafa, "Artificial Intelligence and Machine Learning-Driven Decision-Making", Hindawi, 2021 More
  • Ahmed Salah Mohamed Mostafa, "Usages of Spark Framework with Different Machine Learning Algorithms", Hindawi, 2021 More
  • Ahmed Salah Mohamed Mostafa, "Efficient index-independent approaches for the collective spatial keyword queries", elsevier, 2021 More
  • Ahmed Salah Mohamed Mostafa, "A robust UWSN handover prediction system using ensemble learning", MDPI, 2021 More
  • Ahmed Salah Mohamed Mostafa, "Price Prediction of Seasonal Items Using Machine Learning and Statistical Methods", Tech Science Press, 2021 More

Department Related Publications

  • Ahmed Raafat Abass Mohamed Saliem, "BERT-CNN: A Deep Learning Model for Detecting Emotions from Text", Tech Science Press, 2021 More
  • Ibrahiem Mahmoud Mohamed Elhenawy, "BERT-CNN: A Deep Learning Model for Detecting Emotions from Text", Tech Science Press, 2021 More
  • Ahmed Raafat Abass Mohamed Saliem, "Using General Regression with Local Tuning for Learning Mixture Models from Incomplete Data Sets", ScienceDirect, 2010 More
  • Abdallah Gamal abdallah mahmoud, "An approach of TOPSIS technique for developing supplier selection with group decision making under type-2 neutrosophic number", Elsevier B.V., 2019 More
  • Ibrahiem Mahmoud Mohamed Elhenawy, "A practical evaluation of correlation filter-based object trackers with new features", Public Library of Science, 2022 More
Tweet