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Träfflista för sökning "WFRF:(Toor Salman) srt2:(2015-2019)"

Sökning: WFRF:(Toor Salman) > (2015-2019)

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  • Ahmed, Laeeq, et al. (författare)
  • Efficient iterative virtual screening with Apache Spark and conformal prediction
  • 2018
  • Ingår i: Journal of Cheminformatics. - : BioMed Central. - 1758-2946. ; 10
  • Tidskriftsartikel (refereegranskat)abstract
    • Background: Docking and scoring large libraries of ligands against target proteins forms the basis of structure-based virtual screening. The problem is trivially parallelizable, and calculations are generally carried out on computer clusters or on large workstations in a brute force manner, by docking and scoring all available ligands. Contribution: In this study we propose a strategy that is based on iteratively docking a set of ligands to form a training set, training a ligand-based model on this set, and predicting the remainder of the ligands to exclude those predicted as 'low-scoring' ligands. Then, another set of ligands are docked, the model is retrained and the process is repeated until a certain model efficiency level is reached. Thereafter, the remaining ligands are docked or excluded based on this model. We use SVM and conformal prediction to deliver valid prediction intervals for ranking the predicted ligands, and Apache Spark to parallelize both the docking and the modeling. Results: We show on 4 different targets that conformal prediction based virtual screening (CPVS) is able to reduce the number of docked molecules by 62.61% while retaining an accuracy for the top 30 hits of 94% on average and a speedup of 3.7. The implementation is available as open source via GitHub (https://github.com/laeeq80/spark-cpvs) and can be run on high-performance computers as well as on cloud resources.
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  • Capuccini, Marco, et al. (författare)
  • On-demand virtual research environments using microservices
  • 2019
  • Ingår i: PeerJ Computer Science. - : PeerJ. - 2376-5992. ; 5
  • Tidskriftsartikel (refereegranskat)abstract
    • The computational demands for scientific applications are continuously increasing. The emergence of cloud computing has enabled on-demand resource allocation. However, relying solely on infrastructure as a service does not achieve the degree of flexibility required by the scientific community. Here we present a microservice-oriented methodology, where scientific applications run in a distributed orchestration platform as software containers, referred to as on-demand, virtual research environments. The methodology is vendor agnostic and we provide an open source implementation that supports the major cloud providers, offering scalable management of scientific pipelines. We demonstrate applicability and scalability of our methodology in life science applications, but the methodology is general and can be applied to other scientific domains.
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