"Computational Biology Trends: AI, Genomics, and More"

Bioinformatics: The Catalyst Transforming Biology Through Big Data Biology has officially entered the data-driven era. With affordable genome sequencing and ever-evolving AI tools, bioinformatics is pushing the boundaries of what’s possible in science and healthcare. Unprecedented Data Growth Technologies like next-generation sequencing, mass spectrometry, and high-throughput imaging are producing complex datasets—spanning genomics, proteomics, and beyond—outpacing traditional analysis methods and demanding innovative computational solutions. Bridging Biology and Data Science Bioinformatics fuses biology, computer science, statistics, and data engineering—empowering researchers to extract meaningful insights and model complex biological systems at scale. Revolutionizing Fields Across the Spectrum From precision medicine and accelerated drug discovery to integrative systems biology, ecological monitoring, and pandemic surveillance—bioinformatics is reshaping multiple disciplines. Stunning Market Growth Reflects Real Impact The global bioinformatics market is booming—valued at USD 25.8 billion in 2024, with projections soaring to nearly USD 95 billion by 2032 (CAGR ~16.9%). Fortune Business Insights This blog offers a clear walkthrough of how bioinformatics is transforming research and industry, ideal for professionals, academics, and lifelong learners seeking to understand life sciences in the big data age. Read it here: https://lnkd.in/duYy4jzK #Bioinformatics #BigData #AIinBiotech #PrecisionMedicine #TechInBiology #LifeSciences

I bet you surely faced this....! Navigating the Frontiers of Bioinformatics: The Real-World Challenges of Protein-Protein Docking. As bioinformaticians, we leverage code and algorithms to decode the secrets of life. A cornerstone of this work is protein-protein docking, predicting how two proteins will bind. It's a journey filled with complex challenges that demand both technical skill and biological understanding. Here are some key hurdles we face: Structural Data: Crucial protein structures aren't always available, when studying unrevealed protein. While tools like AlphaFold are game-changers, we must still distinguish between predicted and experimental structures. Software Accessibility: Powerful simulation software often comes with prohibitive costs, limiting access for many. Accessible, high-performance open-source tools are a critical need. Scoring Variability: Docking runs generate thousands of poses. Inconsistent scoring functions make it difficult to confidently identify the correct "native" interaction without extensive validation. Computational Load: Incorporating flexibility and MD simulations requires immense processing power, often necessitating HPC clusters. This is a fundamental challenge that dictates research feasibility. Despite these hurdles, pursuing an understanding of protein-protein interactions remains a most exciting area of science. Each problem brings us closer to breakthroughs that could redefine medicine. What are some of the biggest challenges you've faced? #Bioinformatics #ComputationalBiology #ProteinDocking #StructuralBiology #DrugDiscovery #Science #Innovation #Technology #DataScience #LifeSciences

Navigating the Frontiers of Bioinformatics: The Real-World Challenges of Protein-Protein Docking Protein-protein docking is at the heart of computational drug discovery — but it’s far from easy. #Bioinformatics #ComputationalBiology #DrugDiscovery #ProteinDocking #AIinBiotech #StructuralBiology DeepMind Schrödinger CureVac Novartis Roche Pfizer Genentech Biogen

In bioinformatics, most of the real work happens in the shell: aligning reads, parsing FASTQs, fixing scripts at 2 am. But none of that survives. Papers capture figures, not the commands that created them. Lab notebooks rarely include the actual terminal history. We talk about reproducibility, but if the shell work disappears, what are we actually reproducing? Relevant reading: Wilson G. et al. (2017). Good Enough Practices in Scientific Computing. PLOS Computational Biology

In bioinformatics, most of the real work happens in the shell: aligning reads, parsing FASTQs, fixing scripts at 2 am. But none of that survives. Papers capture figures, not the commands that created them. Lab notebooks rarely include the actual terminal history. We talk about reproducibility, but if the shell work disappears, what are we actually reproducing? Relevant reading: Wilson G. et al. (2017). Good Enough Practices in Scientific Computing. PLOS Computational Biology

Quick Questions for my Computational Community 🎯 Most of my connections here are in Computational Biology, Bioinformatics, PK/PD, QSP, and AI/ML—so I’d love to hear your thoughts! (And Math Modelers and Biophysics Experts) 1️⃣ When was the first time you heard the term Digital Twin? 2️⃣ What was your first impression of the concept? Has that impression of DT changed over time? 3️⃣ Why should a Digital Twin be considered an alive mechanistic model that can answer perturbations or what-if scenarios? 4️⃣ How should AI/ML models interact with or be integrated into Digital Twins? 5️⃣ In 50 years (when I’ll be in my 80s 😅), do you think Digital Twins will be routinely used to guide medical treatments? Happy Friday, everyone! 🥳 Looking forward to your answers!

How do we maintain the mind-boggling pace of genomic research? In the year 2000, the first end-to-end sequence of a human genome was published after 13 years and billions of dollars. This “reference genome”, based on the DNA of around 20 different people, has supported real advances in medicine and given us a deeper functional understanding of the human body. Fast forward a quarter of a century. Your genome can now easily be decoded in around 10 minutes at a cost of a few hundred dollars. To maintain this staggering pace of development, one bottleneck to be overcome is the computing power needed to encode and process genomic data. The blog post below reveals that many believe quantum computers could hold the key to advancing genomics in the next quarter of a century. One goal is to decode something even more exciting than the original reference genome: the “reference pangenome”. This would be a map of all the possible genetic variation across an entire species. Quantum pangenomics will be the work of generations. It holds out the promise not only of important breakthroughs like personalised medicine, but will help us understand, at an functional level, the processes of life itself.

Fun fact: The average human genome has ~3 billion base pairs. But ask any bioinformatician, and they’ll tell you, the real challenge isn’t the billions of bases, it’s the one missing comma in your script that crashes the entire pipeline. Bioinformatics is basically the art of: Teaching computers to understand biology Convincing biologists that computers don’t hate them And making peace with error logs that are longer than your thesis. 😅 At the end of the day, it’s not just about sequences, it’s about finding patterns, meaning, and insights that drive real-world discoveries in healthcare, drug design, and personalized medicine. And yes, we still secretly celebrate when the code runs on the first try.

📢 The CIBB 2025 Conference opens tomorrow in Milan TeraLab will be contributing to the conference with a Special Session titled: "High-Performance Computing for AI-driven Genomics" The session will explore how HPC infrastructures, GPU-accelerated computing, and deep learning techniques are reshaping large-scale genomic data analysis. We look forward to engaging with researchers working at the intersection of AI, bioinformatics, and computational genomics. See you at CIBB 2025 - Computational Intelligence Methods for Bioinformatics and Biostatistics, September 10–12, Politecnico di Milano. 🔗 https://lnkd.in/e9CfBnPd #CIBB2025 #TeraLab #TeraStat #HPC #Genomics #AI #Bioinformatics #GPUcomputing #Sapienza #Supercomputing

✨ Excited to share that I successfully participated in Two days workshop on“Tools and Techniques in Bioinformatics”. 🧬 Tools and Techniques in Bioinformatics Bioinformatics combines biology, computer science, and statistics to analyze and interpret biological data. It plays a vital role in genomics, proteomics, drug discovery, and personalized medicine. 🔧 Tools in Bioinformatics Databases: GenBank, EMBL, PDB, UniProt (for storing genetic/protein data) Sequence Alignment Tools: BLAST, ClustalW, MUSCLE (for DNA/protein sequence comparison) Structural Tools: PyMOL, Chimera (for 3D molecular visualization) Phylogenetic Analysis Tools: MEGA, PhyML (for evolutionary studies) Data Analysis Tools: R, Python (for statistical and computational analysis) 🧪 Techniques in Bioinformatics Sequence Analysis: Identifying genes, motifs, and functional regions Molecular Modeling: Predicting 3D structure of proteins and nucleic acids Genomic and Proteomic Analysis: Studying large-scale DNA/RNA/protein data Phylogenetics: Understanding evolutionary relationships Data Mining & Machine Learning: Extracting patterns from biological big data The session provided deep insights into: ♦️Fundamental and advanced bioinformatics tools ♦️Techniques for sequence analysis, molecular modeling, and data interpretation ♦️Hands-on exposure to databases and computational methods 📌As a microbiologist, this workshop enhanced my understanding of how computational biology bridges the gap between experimental and digital approaches, paving the way for modern biological discoveries. Looking forward to applying these skills in research and future projects! #Bioinformatics #MolecularBiology #ComputationalBiology #Research #LifelongLearning #Microbiology