Reiko Tanaka’s Post

I am pleased to be listed again among the top 2% of academic scientists based on Scopus citation data, ranking 50th in Biomedical Engineering (excluding self-citations single year data). While I don’t see this as a reason for overhype, such rankings highlight the impact of research on global academic peers. The "Top 2% Scientists in Biomedical Engineering - 2024" ranking reveals key leaders, institutions, and countries within the field, using metrics like the h-index, co-authorship adjusted hm-index, and composite c-score. Robert Langer (MIT), Amir A. Zadpoor (Delft University of Technology), and Dietmar W. Hutmacher (Queensland University of Technology) are the top three in Biomedical Engineering based on citation impact. The United States leads with 54 scientists, followed by Australia and China with 6 and 5, among top 100 in Biomedical Engineering, respectively. While citation metrics provide useful benchmarks, they also have limitations, including the risk of misinterpretation and the challenge of capturing qualitative contributions. Accurate Scopus profiles are essential for reliable data. This ranking encourages academics to use citation metrics wisely, emphasizing the need to consider both quantitative and qualitative aspects of scientific contributions. For ethical practices in research evaluation, the Leiden Manifesto serves as a guideline for the responsible use of these metrics. Please see the full list here https://lnkd.in/gFYqFzsf

𝐄𝐱𝐜𝐢𝐭𝐢𝐧𝐠 𝐎𝐩𝐩𝐨𝐫𝐭𝐮𝐧𝐢𝐭𝐲 𝐟𝐨𝐫 𝐂𝐨𝐦𝐩𝐮𝐭𝐞𝐫 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐒𝐭𝐮𝐝𝐞𝐧𝐭𝐬! Are you passionate about machine learning, artificial intelligence, data science, and computational biology? Do you want to contribute to groundbreaking research that bridges the gap between computational models and real-world biological applications? The Amaro Lab at the University of California, San Diego, led by Professor Rommie Amaro, is at the forefront of integrating state-of-the-art computational techniques with biological and chemical research. Our lab focuses on developing and applying advanced computational and theoretical methods to study the structure, function, and dynamics of complex biological systems. 𝐎𝐮𝐫 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐀𝐫𝐞𝐚𝐬 1. 𝐂𝐨𝐦𝐩𝐮𝐭𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐓𝐫𝐚𝐧𝐬𝐥𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 - Leveraging petascale and exascale computing to drive drug discovery efforts. 2. 𝐌𝐨𝐥𝐞𝐜𝐮𝐥𝐚𝐫 𝐑𝐞𝐜𝐨𝐠𝐧𝐢𝐭𝐢𝐨𝐧 - Enhancing drug design methodologies through receptor flexibility and disease network pharmacology. 3. 𝐂𝐨𝐦𝐩𝐮𝐭𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐕𝐢𝐫𝐨𝐥𝐨𝐠𝐲 - Exploring viral structures and their interactions with host cells using biophysical molecular dynamics simulations. 4. 𝐌𝐮𝐥𝐭𝐢𝐬𝐜𝐚𝐥𝐞 𝐌𝐨𝐝𝐞𝐥𝐢𝐧𝐠 𝐟𝐨𝐫 𝐏𝐫𝐨𝐭𝐞𝐢𝐧-𝐋𝐢𝐠𝐚𝐧𝐝 𝐈𝐧𝐭𝐞𝐫𝐚𝐜𝐭𝐢𝐨𝐧s - Employing in-house SEEKR method to investigate protein-ligand interactions. With the vast amounts of data we have generated from various simulation methods, including studies on COVID-19 spike proteins and more, we seek students in computer science to join us. Your expertise in machine learning, artificial intelligence, and methods such as geometric deep learning, graph neural networks, and dimensionality reduction techniques will be crucial in interpreting these complex datasets. By leveraging these cutting-edge approaches, you will help drive impactful discoveries in drug design, protein-ligand interactions, and disease understanding. 𝐖𝐡𝐚𝐭 𝐖𝐞 𝐎𝐟𝐟𝐞𝐫 1. Collaborative research environment with opportunities for co-authorship in high-impact publications. 2. Hands-on experience with cutting-edge computational tools and methodologies. 3. Potential to contribute to significant advancements in drug discovery and translational research. 𝐑𝐞𝐪𝐮𝐢𝐫𝐞𝐦𝐞𝐧𝐭𝐬 1. You must be working towards your full-time graduate degree in computer science. 2. This opportunity will allow you to extend your CS skills on an exciting project with us. We want to hear from you if you are eager to apply your machine-learning skills to solve real-world biological problems and work on projects with high innovation potential. You will be working with me and Ambuj Srivastava, PhD directly.  Please visit the Amaro Lab website to learn more about us. Here is the link: https://amarolab.ucsd.edu/. 𝐂𝐨𝐧𝐭𝐚𝐜𝐭 𝐔𝐬 Please contact me here or e-mail me at [email protected] or [email protected] (also cc it to [email protected]).

This course provides  a guide to the commonly used methods and tools in structural bioinformatics to analyse and interpret experimentally determined and AI-predicted macromolecular structure data: https://lnkd.in/e2xmYnfu Structural biology, determining the three-dimensional shapes of biomacromolecules and their complexes, can tell us a lot about how these molecules function and the roles they play within a cell. Data derived from structure determination experiments and Artificial Intelligence (AI)-assisted structure prediction enables life-science researchers to address a wide variety of questions.  This course explores bioinformatics data resources and tools for the investigation, analysis, and interpretation of both experimentally determined and predicted biomacromolecular structures. It will focus on how best to analyse and interpret available structural data to gain useful information given specific research contexts. The course content will also cover predicting function and exploring interactions with other macromolecules. Limited financial assistance is available in the form of a registration fee waiver, which will cover the registration fee to attend the course. The recipient will be informed about their bursary application together with the outcome of their course application. Apply by 28 July 2024. #structuralbioinformatics #structuralbiology #proteinstructures #proteinbiology #EMBLEBI #EMBL #AlphaFold #AI

Do you want to know more about how to use InterPro and Pfam in your structural bioinformatics research? Register for this online course happening this autumn!

💡User stories: Gemma Atkinson is on the hunt for bacterial immune systems We at NAISS often tend to get a bit technical. But our resources are essentially just a means to an end: to help our users do ground-breaking research. We have more than 7,600 amazing, clever and creative users who deserve more attention. What are they up to? Here is one of them: Gemma Atkinson, senior lecturer at Lunds universitet and leader of a bioinformatics team at the Atkinson Lab. In cooperation with the experimental lab of senior lecturer Vasili Hauryliuk, they are searching through bacterial genomes to discover new immune systems, which are made of proteins. For this they are using #AlphaFold, the AI-powered protein prediction programme which is recognised in this year’s Nobel Prize in Chemistry. She happily admits that she is a heavy user of NAISS resources. “We need a lot of power, so we use everything. We are doing so much! For AlphaFold, it is almost entirely AI/GPU resources. For the multiple sequence alignments of proteins that we use to predict the structure, we need CPUs.” Gemma Atkinson says researchers in Sweden are lucky to have access to large-scale computational resources for free. This is also a big selling point when she is recruiting new colleagues. Read the full story: https://lnkd.in/eFXDqFfm Learn more about the Atkinson lab: https://atkinson-lab.com/ #supercomputers #HPC #bioinformatics #proteinfolding

🔬 Unveiling the World of Structural Bioinformatics 🧬 Structural bioinformatics is an exciting and evolving field at the intersection of biology, chemistry, and computer science. It involves the study and prediction of the three-dimensional structures of biological macromolecules, like proteins and nucleic acids, to understand their function and interactions. Here are some key aspects: 🌟 Understanding Biomolecular Structures: By analyzing the 3D structures of proteins and other macromolecules, we can gain insights into their functions and mechanisms. This knowledge is crucial for drug discovery and development. 💡 Innovative Tools and Techniques: Structural bioinformatics leverages cutting-edge computational tools, such as molecular modeling, docking simulations, and dynamic simulations, to predict and visualize biomolecular structures. 🔗 Bridging Gaps in Research: This field plays a pivotal role in linking experimental data with theoretical models, enabling researchers to validate their hypotheses and explore new biological phenomena. 🚀 Future Prospects: With advancements in AI and machine learning, structural bioinformatics is set to revolutionize our understanding of molecular biology, leading to breakthroughs in personalized medicine and targeted therapies. Embracing the challenges and opportunities in structural bioinformatics can open new doors in scientific research and innovation. Let’s decode the mysteries of life at the molecular level and pave the way for a healthier future! 🌍💊🔍 #StructuralBioinformatics #Bioinformatics #MolecularBiology #DrugDiscovery #ComputationalBiology #Biotech #Innovation #Healthcare

Hi all.... Bioinformatics is an interdisciplinary field that combines biology, computer science, and information technology to analyze and interpret biological data, particularly genomic data. It involves developing algorithms, databases, and software tools to understand biological processes, predict protein structures, and study evolutionary patterns. It plays a crucial role in genomics, personalized medicine, drug discovery, and many other areas of biological research. Bioinformatics tools and databases, such as BLAST, NCBI, and Ensembl, are widely used by researchers worldwide to facilitate their analyses and discoveries. The field continues to evolve rapidly, driven by advances in genomics, data science, and computing technologies. In bioinformatics, researchers use computational methods to analyze large datasets generated from biological experiments. This could include DNA sequencing data, protein structure information, gene expression data, and more. They develop algorithms and software tools to process, analyze, and interpret these datasets, aiming to extract meaningful biological insights. #snsinstitution #snsdesignthinkers #designthinking

A science fiction story about biology and computational science. This article traces the evolution of computational models in biology, focusing on the development and impact of computing-based “models of life.” While early optimism faded due to limitations in performance, later breakthroughs led to massive shifts in fields like drug discovery, agriculture, and environmental engineering, culminating in advanced biological systems. By 2045, these models influenced nearly every aspect of life sciences. https://lnkd.in/gPz25Dwy

The 2024 Nobel Prize in Chemistry has been awarded to Demis Hassabis, John Jumper, and David Baker for their revolutionary work in AI-driven protein structure prediction. Their achievements with AlphaFold and related deep learning methods are transforming the fields of chemistry and biology. AlphaFold, developed by DeepMind, solved one of biology’s biggest challenges: predicting the 3D structure of proteins based solely on their amino acid sequences. This breakthrough, led by Hassabis and Jumper, has revolutionized the way we understand biological molecules, speeding up drug discovery, disease research, and molecular design. Meanwhile, David Baker’s work has focused on designing new proteins that do not exist in nature, using AI to create molecules that could have significant impacts on medicine and materials science. These AI-driven approaches are now shaping the future of chemistry and biomedicine, allowing scientists to simulate molecular behavior, optimize drug design, and improve our understanding of biological systems with unprecedented precision. By combining deep learning and cutting-edge molecular research, this year’s laureates have demonstrated the incredible power of AI to solve real-world scientific challenges. Their contributions will inspire a new generation of innovation at the intersection of AI, chemistry, and biology. #NobelPrize #AlphaFold #DemisHassabis #JohnJumper #DavidBaker #DeepLearning #AIInBiology #ProteinFolding #DrugDiscovery #AIRevolution #Chemistry #Biology #MolecularScience #DeepMind #ScientificBreakthrough #MachineLearning #Innovation

👉🏼 Artificial Intelligence Learns Protein Prediction 🤓 Michael Heinzinger 👇🏻 https://lnkd.in/gGju4KQg 🔍 Focus on data insights: - AI advancements in protein prediction - Impact of AlphaFold2 on protein structure prediction - Evolution of protein language models (pLMs) 💡 Main outcomes and implications: - Accelerated wealth of experimental data - Faster cycle from idea to proof in molecular biology - Enhanced protein design capabilities 📚 Field significance: - Revolutionizing computational biology - Shaping the future of molecular and medical biology 🗄️: [#ArtificialIntelligence #ProteinPrediction #AlphaFold2 #pLMs]