A Multi-core Tool for Searching Protein Structural Similarities

Faculty Computer Science Year: 2012
Type of Publication: ZU Hosted Pages: 34-36
Authors:
Journal: International Journal of Computer Applications International Journal of Computer Applications Volume: 20
Keywords : , Multi-core Tool , Searching Protein Structural Similarities    
Abstract:
The analysis of protein structural similarities plays an important role in different biological fields. These fields vary from the process of developing new drugs to detecting the evolutionrelationships. As the number of protein structures
   
     
 
       

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
  • Ahmed Raafat Abass Mohamed Saliem, "On determining efficient finite mixture models with compact and essential components for clustering data", ScienceDirect, 2013 More
  • Ahmed Raafat Abass Mohamed Saliem, "Unsupervised learning of mixture models based on swarm intelligence and neural networks with optimal completion using incomplete data", ScienceDirect, 2012 More
Tweet