DUG’s Post

↓ Swipe to see how our tailored high-performance computing (HPC) solutions are enabling efficient use of time and resources across bioinformatics workflows, helping organisations realise research outcomes earlier while delivering more value for money. ↓

Check out this informative white paper that delves into the Build vs. Buy bioinformatics systems debate from a security perspective. https://hubs.ly/Q02zTLgw0

Check out our new white paper about security concerns you need to consider for your disgnostics bioinformatics systems. Especially in light of 21 CFR part 11!

Explore Insights: Customer Loyalty & Switching Library Prep Kits📚 Discover the latest publication by BioInformatics, delving deep into the world of Library Prep Kits. Gain invaluable insights into purchase decisions and supplier loyalty dynamics. 🔗 Ready to Learn More? Get the report here: https://hubs.la/Q02yK2Fk0

The School of Computing's own Dr. In Kee Kim, along with Drs. Robert Grossman and Haryadi Gunawi at the University of Chicago, received a new NSF grant for their project, "ALL-IN-ONE: Strengthening the System Aspects of Large-Scale Genomics Processing Platforms." This project aims to advance state-of-the-art genomic processing systems by: 1) developing cluster scheduling optimized for genomic workloads across on-prem, clouds, and accelerators 2) establishing resource and failure-aware independent task scheduling 3) and creating cloud-and-language agnostic meta-compiler for automated performance tuning.

Please take a few minutes to help us understand how you use #UniProt, and what value it adds to your work. Your input will help guide the development of the resource going forward, so please share your thoughts and needs with us via this survey.

It is well known that #lifescientists across the world depend on #bioinformatics resources such as #UniProt to do their #research, with some even admitting that they would not be able to work without access to UniProt. For the first time, a team of economists from CSIL is undertaking a full cost-benefit analysis of UniProt, treating it just as seriously as a public-funded train or road infrastructure would be At ELIXIR we could not be more excited as it is one of the ELIXIR Core Data Resources! https://lnkd.in/e99SvZWc We are particularly interested in the views from those who work in #industry and who use UniProt, so help us spread the word #CBA SIB Swiss Institute of Bioinformatics European Bioinformatics Institute | EMBL-EBI PathOS Project

Comparing the execution time and consumption energy of the LU factorization (without pivoting) and Cholesky: “The scalability in terms of the time and the energy for several matrix factorizations on a multicore machine” by Beata Bylina, Monika Piekarz. Proceedings of the 18th Conference on Computer Science and Intelligence Systems(Thematic Tracks Short Papers), appeared in: Maria Ganzha, L. Maciaszek, Marcin Paprzycki, Dominik Ślęzak (eds); ACSIS, Vol. 35, pages 895–900(2023). #LUfactorization #Cholesky #scalability #matrixfactorization #multicore Open Access: https://lnkd.in/drpr_QHd PTI - Polskie Towarzystwo Informatyczne QED SOFTWARE

Just wrapped up another fantastic webinar today: "Optimisation of Molecular Docking," hosted by NyBerMan Bioinformatics Europe. Feeling inspired and ready to dive deeper into this exciting field! #MolecularDocking #Webinar #Bioinformatics #ContinuousLearning

🔬 Excited to share our latest research breakthrough in the field of medical diagnostics! 🚀 📝 Our paper titled "A Novel Feature Extraction Technique for ECG Arrhythmia Classification Using ML" has been published in the 2023 IEEE International Conference on Dependable, Autonomic, and Secure Computing, the International Conference on Pervasive Intelligence and Computing, the International Conference on Cloud and Big Data Computing, and the International Conference on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech). 🎯 Feature extraction is crucial for accurately predicting medical conditions from raw data. In our research, we introduce a novel method called the Random Feature Explorer (RFE) for ECG signal analysis. This technique leverages existing specific features of the ECG signal to generate new features, achieving remarkable accuracy in arrhythmia classification—99.79%, to be precise! 🙏 Heartfelt thanks to our supervisor Samir B. Belhaouari for guiding us throughout this research journey and to our dedicated postdoctoral researcher Ashhadul Islam for their invaluable contributions. The source code for our proposed method is available on GitHub for open access and reproducibility.