Sadia Sabrin Prima’s Post

🚀 Excited to share an incredible course: Bioinformatics and Proteomics! 🧬💻 This interdisciplinary course dives into: 📊 Sequence analysis & microarray expression analysis 🤖 Bayesian methods, control theory & machine learning 🌐 Scale-free networks & signal processing 🧪 Biotechnology applications with real-world examples With a hands-on approach, it’s perfect for those with a computational or engineering background. Let's explore how biology meets technology! 🌱⚙️ Check link : https://lnkd.in/eUtN8TMv #Bioinformatics #Proteomics #ComputationalBiology #STEM #MachineLearning

FROM GENES TO NETWORKS: REALIZING THE DREAMS OF SYSTEMS BIOLOGY WITH BIOINFORMATICS Systems biology aims to understand biological complexity by looking at organisms as networks of genes, proteins, and interactions rather than isolated parts. Bioinformatics is the key to making this vision a reality. By integrating data from genomics, proteomics, and other "-omics," bioinformatics enables us to model entire biological systems. These models help us understand how different components interact, leading to breakthroughs in medicine, biotechnology, and agriculture. With advances in machine learning and big data, bioinformatics is pushing the boundaries of what’s possible. From identifying disease pathways to engineering synthetic organisms, the potential is immense. Bioinformatics is not just the future of biology—it’s the driving force behind systems biology’s biggest breakthroughs. #noblekinmat #Bioinformatics #SystemsBiology #Genomics #BigData #MachineLearning #Proteomics #Biotechnology #SyntheticBiology #DataScience #Healthcare #Innovation #Biology #LifeSciences

About Bioinformatics Bioinformatics is an interdisciplinary field that merges biology, computer science, and information technology to analyze and interpret vast amounts of biological data. It plays a crucial role in understanding genetic and molecular processes, enabling advancements in areas like genomics, proteomics, and personalized medicine. Key applications of bioinformatics include: 1️⃣-Genome Sequencing and Annotation: Identifying and mapping genes within DNA sequences. 2️⃣- Protein Structure Prediction: Understanding protein functions and interactions. 3️⃣-Data Management: Storing, organizing, and retrieving biological data efficiently. 4️⃣-Biological Network Analysis: Studying complex interactions within biological systems. #blood_bank #hematology #lab #Laboratories #laboratory #LinkedIn #Biochemistry #Bioinformatics

FROM GENES TO NETWORKS: REALIZING THE DREAMS OF SYSTEMS BIOLOGY WITH BIOINFORMATICS Systems biology aims to understand biological complexity by looking at organisms as networks of genes, proteins, and interactions rather than isolated parts. Bioinformatics is the key to making this vision a reality. By integrating data from genomics, proteomics, and other "-omics," bioinformatics enables us to model entire biological systems. These models help us understand how different components interact, leading to breakthroughs in medicine, biotechnology, and agriculture. With advances in machine learning and big data, bioinformatics is pushing the boundaries of what’s possible. From identifying disease pathways to engineering synthetic organisms, the potential is immense. Bioinformatics is not just the future of biology—it’s the driving force behind systems biology’s biggest breakthroughs. #noblekinmat #Bioinformatics #SystemsBiology #Genomics #BigData #MachineLearning #Proteomics #Biotechnology #SyntheticBiology #DataScience #Healthcare #Innovation #Biology #LifeSciences

Bioinformatics: Where Biology Meets Data Science Bioinformatics merges biology and computer science to extract knowledge from biological data. It's crucial for genomics, proteomics, drug discovery, and personalized medicine. Bioinformatics is transforming biological research and healthcare. It's driving innovation and accelerating our understanding of life at the molecular level. Link to full blog https://lnkd.in/gUvMzpkF

Scientists present #MultiMM, a fast and accessible #chromatin modeling tool. It generates whole-genome structures in minutes, enhancing bioinformatics research capabilities. Quick Read: https://lnkd.in/gn29sfbD #Bioinformatics #ChromatinDynamics #GenomeModeling #sciencenews

Trivia Question: Which advanced bioinformatics tool employs machine learning algorithms to predict drug-target interactions based on structural and functional data of proteins? Possible Answers: A. STRING   B. DeepDTI   C. BLAST   D. MOE (Molecular Operating Environment) #Bioinformatics #MachineLearning #DrugDiscovery #Trivia Sciqst, Precise References in Medicine. www.sciqst.com

🔬 Bioinformatics Meets Visualization: PyMOL and Beyond 🌟 In bioinformatics, data is our foundation, but visualization is our lens to discovery. Among the many tools I use, PyMOL stands out as a game-changer for bringing molecular structures to life. With PyMOL, complex 3D biomolecular data becomes: Understandable: Visualizing protein-ligand interactions, folding pathways, or structural alignments. Beautiful: High-quality, publication-ready images that communicate science effectively. Actionable: Insights that guide decisions in drug discovery, structural biology, and beyond. The ability to combine tools like PyMOL with others in the bioinformatics ecosystem makes it possible to bridge the gap between sequence data and structural insights. Whether it’s analyzing protein domains or building multi-protein complexes, PyMOL has been an essential tool in my journey. What’s your go-to tool for molecular visualization? How has it shaped your bioinformatics projects? Let’s exchange ideas and push the boundaries of what’s possible! #Bioinformatics #PyMOL #DataVisualization #StructuralBiology

Wistar researchers working in fields like genomics, proteomics, metabolomics, and molecular modeling often generate data sets so large and complex that they challenge the limits of existing software. The Center for Systems and Computational Biology supports scientists by developing new algorithms and tools to collect, analyze, and store data for highly specific research needs, often by harnessing the power of machine learning. “Our models can extract more information and identify more patterns in data than humans could on their own. Right now, people are building models that will do things like predict clinical outcomes, predict biological factors, and understand more about biology,” says Wistar scientist Noam Auslander. Want to learn more about #WistarScience? Sign up for our newsletter here. https://lnkd.in/edgDP46f #Bioinformatics #ComputationalBiology #DataScience #MachineLearning

Excited to share that I recently attended another insightful online workshop on "Multi-Ligand Docking using PyRx and AutoDock Vina". This session was a great opportunity to enhance my molecular docking skills for computer-aided drug discovery (CADD) applications. Workshops like these fuel my passion for computational chemistry and bioinformatics, and I am motivated to further explore such learning opportunities in the future. I aim to continue building my expertise and contributing to research and development in the respective fields. #ComputationalChemistry #DrugDiscovery #MolecularDocking #Bioinformatics #ResearchAndDevelopment #ContinuousLearning