New method for genetic minimal cut sets uses linear programming

StackGlyEmbed introduces a stacking ensemble framework that leverages embeddings from ProteinBERT, ESM-2, and ProtT5-XL-U50 to predict N-linked glycosylation sites. By integrating multiple protein language models with machine learning classifiers, the tool achieves higher sensitivity and accuracy than existing state-of-the-art methods. This approach reduces reliance on costly structural data while improving predictive power.  Read the full study in Bioinformatics Advances: https://lnkd.in/gCDm2wzz

Accelerating Discovery: How Novaflow (YC S25) is Redefining Bioinformatics for the Next Generation of Science Read it on Amazon: https://lnkd.in/g57aNyek

{"linkedin_post":"Software engineering meets life sciences in our latest blog post, revealing how computational biology and bioinformatics are reshaping the field. Dive into the dynamic world of genomic data processing, algorithmic challenges, and career opportunities that go beyond the traditional tech industry. Read more about how software is decoding the language of life.","hashtags":["#ComputationalBiology","#Bioinformatics","#Genomics"," #TechForGood"],"url":"https://lnkd.in/gk-8aHr6","character_count":280,"tone":"professional"}

This online course introduces the principles of bioinformatics and explains how they can be applied to answer biological questions. Learn more at https://lnkd.in/dqnEGkpM

🔍 Learning to Automate, Scale, and Reproduce in Bioinformatics Today’s research workflows are no longer about just running analyses — they’re about making them scalable, reproducible, and efficient. That’s why I took part in the webinar on Mastering Nextflow : The Future of Workflow Automation in Bioinformatics, organized by DrOmics Research Lab . This session reminded me that in science, it’s not only the answers that matter, but also the processes we build to get there. 🚀 Grateful for the opportunity to learn, connect, and explore how workflow automation is shaping the next phase of bioinformatics. #Nextflow #Bioinformatics #Automation #FutureOfScience

{"linkedin_post":"Software engineers are transforming life sciences through computational biology and bioinformatics. Discover how traditional coding skills are pivotal in processing genomic data, predicting protein structures, and more. Dive into our latest blog to explore the potential of this interdisciplinary field. #Bioinformatics #TechInBiology #ComputationalScience","hashtags":["#Bioinformatics","#TechInBiology","#ComputationalScience"],"url":"//https://lnkd.in/gk-8aHr6","character_count":279,"tone":"professional"}

Want to dive deeper into Bioinformatics and Computational Biology? Here is the ocean of resources: 1. https://lnkd.in/gm9gBKDS 2. http://sandbox.bio 3. https://lnkd.in/gedAw6Ej 4. https://lnkd.in/gAguBvjY 5. https://lnkd.in/g4fr9Wsd 6. https://lnkd.in/g_F4ESPk 7. https://lnkd.in/gV62mJ-R 8. https://lnkd.in/gD2VTGD3 9. https://lnkd.in/gDfYC67M 10. https://lnkd.in/gNxrB-mX 11. https://lnkd.in/gKnTZUrH

Ever wondered how we find a cancer-causing mutation in 3 billion letters of DNA? Or how AI is used to discover new drugs? 🧬🤖 The field solving these puzzles is Bioinformatics & Computational Biology. It's where biology, computer science, and statistics collide to answer the biggest questions in modern medicine. I'm exploring a world-class course syllabus from Harvard that covers everything from: ▶️ Analyzing genome sequences (RNA-seq, ChIP-seq) ▶️ Single-cell & cancer genomics ▶️ AI/ML for clustering and classification ▶️ CRISPR screen analysis. Curious? What topic in bioinformatics amazes you the most? Drop it in the comments! 👇 #Bioinformatics #ComputationalBiology #Genomics #DataScience #AI #MachineLearning #HealthTech #Pharma #Biotech #Science

Traditional searches in MEDLINE and PubMed Central often overlook supplementary files, despite their rich scientific content. By systematically extracting text from millions of files, researchers revealed that the majority of accession numbers and a significant share of database mentions reside only in supplementary materials. This resource, now available via BiodiversityPMC, enhances findability and supports FAIR principles.  Read the full study in Bioinformatics Advances: https://lnkd.in/g_3cxX4z

This review offers an overview of the #HiddenMarkovModel, outlining its fundamental concepts & corresponding algorithms, and examines their applications in modern #Bioinformatics, thereby fostering a deeper understanding of HMM among bioinformatics researchers. Harbin Medical University #OpenAccess: https://lnkd.in/ejn39AZ3