Toloka’s Post

EMBL-EBI has a mission to support companies of all sizes to harness the power of bioinformatics. Asked about the impact of EMBL-EBI’s work, Demis Hassabis, Co-Founder and CEO of Google DeepMind said: “EMBL-EBI is playing a central role in the emerging field of digital biology. Its world-leading data resources and technical expertise help nurture the virtuous circle of open science on which pioneering tools like AlphaFold can be built. By helping unlock the potential of bioinformatics, EMBL-EBI is accelerating discovery and enabling the scientific community to push the boundaries of our understanding of the world.” Read more about our latest achievements in the field of AI in our 2023 Highlights report. https://lnkd.in/eMsY4Wi4  #EMBLEBI30 #AlphaFold #bioinformatics #AI #FAIRdata

🚀 𝐁𝐢𝐨𝐥𝐨𝐆𝐏𝐓 𝐋𝐚𝐮𝐧𝐜𝐡𝐞𝐬 '𝐄𝐯𝐨𝐥𝐯𝐞 𝐂𝐨𝐝𝐞' 𝐟𝐨𝐫 𝐒𝐦𝐚𝐫𝐭𝐞𝐫 𝐁𝐢𝐨𝐥𝐨𝐠𝐲 𝐀𝐈 Introducing 𝐄𝐯𝐨𝐥𝐯𝐞 𝐂𝐨𝐝𝐞, a groundbreaking AI from BioloGPT that continuously improves itself to deliver better bioinformatics code and biology answers. 𝐊𝐞𝐲 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐬 • 𝐒𝐞𝐥𝐟-𝐈𝐦𝐩𝐫𝐨𝐯𝐢𝐧𝐠 𝐋𝐨𝐨𝐩: Learns from its previous outputs to refine code and answers. • 𝐀𝐮𝐭𝐨𝐧𝐨𝐦𝐨𝐮𝐬 𝐅𝐞𝐞𝐝𝐛𝐚𝐜𝐤: Evaluates and updates its responses for maximum accuracy. • 𝐔𝐩-𝐭𝐨-𝐃𝐚𝐭𝐞 𝐃𝐚𝐭𝐚: Leverages the latest full-text research to stay current. • 𝐀𝐜𝐜𝐞𝐥𝐞𝐫𝐚𝐭𝐞 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡: Provides increasingly optimized bioinformatics solutions with every interaction. Take your biology research to the next level! 👉 Try it here: https://biologpt.com/ #AI #Bioinformatics #ResearchInnovation #BioloGPT #LLMs

🔬 AI in Bioinformatics: Transforming Data into Discoveries 🧬 Explore the revolutionary impact of AI in bioinformatics and how it's driving new scientific discoveries. Learn more in our latest blog post. 🔗 Read more: https://lnkd.in/deGGH2Nc... #Bioinformatics #AI #DataScience #Research #Innovation #Digianalix #ScientificDiscoveries

𝐓𝐡𝐞 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞 𝐨𝐟 𝐑𝐞𝐩𝐫𝐨𝐝𝐮𝐜𝐢𝐛𝐢𝐥𝐢𝐭𝐲 𝐢𝐧 𝐁𝐢𝐨𝐢𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐜𝐬 𝐀𝐈/𝐌𝐋 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 Reproducibility is the backbone of scientific research, but how often do we find that the ML models we develop on biological data fail to produce the same results when tested again? In bioinformatics, where we deal with large datasets and complex workflows, ensuring consistent results from machine learning models is no easy task. Small changes in the dataset or environment can lead to wildly different outcomes. How can we ensure that our models are as reliable as the biological hypotheses we test? 🔄 This issue becomes even more critical as institutions scale up their research. What strategies are you using to make your ML models more reproducible? #Bioinformatics #AI #ResearchChallenges

🚀 Excited to share my latest research publication! 📄 🎓 My paper titled "Hidden Markov Model - Applications, Strengths, and Weaknesses" has been accepted at the 2024 2nd International Conference on Device Intelligence, Computing, and Communication Technologies (DICCT), scheduled for May 22, 2024. This research explores the wide-ranging applications of the Hidden Markov Model (HMM) in fields like weather forecasting, bioinformatics, disease diagnosis, signal processing, and more. It also highlights the strengths of the model in providing probabilistic insights and accurate interpretations, while addressing its computational challenges and limitations. I am truly grateful for this opportunity to contribute to the research community and look forward to further exploring its role in enhancing national security and other evolving fields. https://lnkd.in/dKCzPFfT #ResearchPublication #HiddenMarkovModel #MachineLearning #DICCT2024 #DataScience #AI #ProudMoment

SPECIAL ISSUE | Our team leader Stanislav Mazurenko (RECETOX, Masaryk University Brno) collaborated with Héctor Garcia Martin, PhD (Berkeley Lab) and Zhao Huimin (University of Illinois Urbana-Champaign) to co-edit a virtual special issue published in 𝗔𝗖𝗦 𝗦𝘆𝗻𝘁𝗵𝗲𝘁𝗶𝗰 𝗕𝗶𝗼𝗹𝗼𝗴𝘆. The collection focuses on the application of artificial intelligence (AI) and machine learning (ML) to enhance predictive capabilities in synthetic biology. The issue showcases a variety of cutting-edge AI/ML architectures currently being explored in the field. 👉 "It was an exciting opportunity to participate in the special issue and observe all that progress in the field. We were surprised to see so many articles submitted to the call, covering a broad range of topics from applied synthetic biology and protein engineering tools to method development and scientific text mining. And seeing so many research groups embracing AI tools in their research is truly inspiring!" added Stas. 👉 The editorial for the special issue can be accessed at: https://lnkd.in/eGBs5UuV #editorial #journal #specialissue #publishing #publishingsuccess #AI #machinelearning #syntheticbiology #proteinengineering

🎉 Exciting News! 🎉 I’m happy to announce the acceptance of another paper! Our latest collaborative work with Dr. Muhammad Tahir, titled "An Integrated Multi-Model Framework Utilizing Convolutional Neural Networks Coupled with Feature Extraction for Identification of 4mC Sites in DNA Sequences," has been accepted for publication in the esteemed journal Computers in Biology and Medicine (IF: 7.0). In this paper, we developed a multi-model framework that effectively represents local DNA sequence patterns. By utilizing Convolutional Neural Networks along with advanced feature extraction techniques, our framework enhances the identification of 4mC sites in DNA sequences. The application of embedding allows for a more holistic encoding, considering the broader context and semantics of DNA sequence data. I believe this research will contribute to the rapid discovery and development of drugs and medicine, advancing the field of bioinformatics. A heartfelt thank you to all my collaborators. #Bioinformatics #ConvolutionalNeuralNetworks #DrugDiscovery #4mC #DNASequencing #ComputersInBiologyAndMedicine

AlphaFold3 - extending to DNA, molecules etc

I'm thrilled and humble to announce my involvement in the latest release of OpenFold, the most popular training codes for AlphaFold2. This update enabled training of AlphaFold Multimer and introduced new workflows that are not covered by AlphaFold2 and ESMFold. It specifically enhanced the speed and can now compute the ranking of decoys to protein sequences. The new version of OpenFold will tremendously help my own PhD studies and I look forward to the new applications of our programme in advancing AI-powered computational biology research in academia and the industry. #alphafold #artificialintelligence #bioinformatics #computationalbiology Link to the release note on Github: https://lnkd.in/d4EyU5bv A short summary: https://lnkd.in/dFn-CF63

Hi everyone, Hope you are doing really well at your own pace ✨ Today I am writing to express a very good news in my B.Tech journey 🙂 The journey of the last 10 months, we have worked on a research oriented project on #gan and #bioinformatics named "Realistic ScRNA-seq cell generation using Graph Attention based Generative Adversarial Network". 🤖 GAN is basically used to generate realistic samples and most use of this technology is on images. But we have chosen a really different domain "Bioinformatics" 🧬 and "Human Cell". 🤖 As extracting human cell samples from patient is too difficult and costly, so to reduce the budgetary constraints and time we have chosen the #ml. 🤖 To know the real principle of Computation Biology and Single Cell Technology was a really interesting topic, really enjoying and know many things on it. 🌟🌟 A many many thanks to Dr. Sumanta Ray sir for your idea and complete guidance. To work on this hot-cake technology was not a cup of tea 🍵. From availability of very few resources on the internet to completing the work before the proposed time, the journey was really awesome 😊. 🌟🌟 Also many thanks to Dr. Sk Md Mosaddek Hossain sir for your guidance at every step when we wanted, thank you very much sir. 👾💫 Last but not the least, it will not be completed without this two mates Nilanjana Bhattacharya and Sruti Dey . Your dedicated work and huge effort to our work was priceless, hats off to you guys 💖. 💥 In few days, we will be publishing our research paper on this work 💥 💥 Website - Also live (due to confidentiality, we remain it private, public soon 😊) Thanks. #gan #ml #generativeadversialnetwork #gat #biology #singlecell #machinelearning #computationalbiology #generativeai #ai