Christoph Hiemenz

👉🏼 LitAI: Enhancing Multimodal Literature Understanding and Mining with Generative AI 🤓 Gowtham Medisetti 👇🏻 https://lnkd.in/eKB__hFi 🔍 Focus on data insights: - 📊 The integration of optical character recognition

👉🏼 Evaluating the efficacy of few-shot learning for GPT-4Vision in neurodegenerative disease histopathology: A comparative analysis with convolutional neural network model 🤓 Daisuke Ono 👇🏻 https://lnkd.in/e2RK6-xr 🔍 Focus on data insights: - GPT-4Vision accurately recognized staining techniques and tissue origin but struggled with specific lesion identification. - The interpretation of images was notably influenced by the provided textual context, sometimes leading to diagnostic inaccuracies. - Few-shot learning significantly improved GPT-4Vision's diagnostic capabilities, enhancing accuracy from 40% in zero-shot learning to 90% with 20-shot learning. 💡 Main outcomes and implications: - GPT-4Vision faces challenges in independently interpreting histopathological images. - Few-shot learning is a promising approach to enhance GPT-4Vision's performance, especially in neuropathology where acquiring extensive labeled datasets is challenging. 📚 Field significance: - Artificial intelligence advancements like GPT-4Vision have expanded the potential for medical image interpretation. - Comparative analysis with traditional convolutional neural network models provides insights into improving diagnostic accuracy in neurodegenerative disease histopathology. 🗄️: #neurodegenerative #histopathology #GPT-4Vision #fewshotlearning

👉🏼 How deep learning is transforming protein structural biology through advanced neural networks 🤓 Matthias Bochtler 👇🏻 https://lnkd.in/g9HaXxTN 🔍 Focus on data insights: - 📈 Deep neural networks like CNNs, LLMs, and GNNs are significantly enhancing protein structure prediction. - 🧬 Applications of AI are expanding in areas such as inverse folding and protein design, leading to more effective and efficient workflows. - 🤖 Machine learning models are being leveraged to translate complex biological data into understandable and actionable insights. 💡 Main outcomes and implications: - 🔄 The integration of advanced deep learning techniques is revolutionizing the methodology used in structural biology. - 🌐 This transformation highlights the necessity for structural biologists to adapt to new technologies for improved research outcomes. - 📊 Insights derived from AI applications are likely to accelerate discoveries in protein-related biological processes and therapeutics. 📚 Field significance: - 🔍 The convergence of artificial intelligence and structural biology is setting a new paradigm in the field, facilitating unprecedented levels of discovery. - 💡 This review serves as a comprehensive guide, not only for structural biologists but also for those with backgrounds in machine learning, physics, or statistics. - 📖 As new methodologies emerge, continuous education and adaptation will be essential for scientists to remain at the forefront of research. 🗄️: [#deepLearning] [#neuralNetworks] [#structuralBiology] [#proteinDesign] [#machineLearning] [#AI] [#CNN] [#LLM] [#GNN]

👉🏼 Large language model to multimodal large language model: A journey to shape the biological macromolecules to biological sciences and medicine 🤓 Manojit Bhattacharya 👇🏻 https://lnkd.in/e8BAuTz2 🔍 Focus on data insights: - 📈 The increasing adoption of large language models

👉🏼 ChatGPT-based Biological and Psychological Data Imputation 🤓 Anam Nazir https://lnkd.in/ezAXTbzp 🔍 Focus on data insights: - ChatGPT utilized for biological and psychological data imputation - Evaluation metrics include Pearson correlation coefficient, relative accuracy, and mean absolute error 💡 Main outcomes and implications: - ChatGPT shows superior efficacy compared to traditional imputation methods - Customized data-to-text prompting enhances accuracy in imputations 📚 Field significance: - Improved accuracy in imputing biological and psychological data - Potential for enhancing research outcomes in large cohort studies 🗄️: #datainsights #imputation #biologicaldata #psychologicaldata

👉🏼 Potential application of ChatGPT in Helicobacter pylori disease relevant queries 🤓 Zejun Gao 👇🏻 https://lnkd.in/emVwfvch 🔍 Focus on data insights: - 📊 The use of ChatGPT-4 showed a high level of repeatability and adequate response length across various prompts related to Helicobacter pylori. - 🧠 Expert evaluations indicated strong scores in accuracy, completeness, usefulness, comprehension, and satisfaction regarding ChatGPT's responses. - 🗣️ There is a notable difference in the perceived usefulness and comprehension of responses between experts and ordinary people, with medical students having a more favorable view. 💡 Main outcomes and implications: - 🔬 ChatGPT-4 has demonstrated the potential to be an effective tool for addressing specific medical queries, particularly concerning Helicobacter pylori. - 🏫 The platform may serve as a valuable resource for medical education, especially for medical students seeking supplemental information. - 🔄 Further research is essential to validate these findings and to explore the broader applicability of AI-driven tools in medical settings. 📚 Field significance: - 🌐 The integration of AI language models into healthcare could enhance patient education and inform clinical practice. - 💡 This study suggests that large language models might bridge knowledge gaps in healthcare communication, signaling a shift towards technology-assisted learning and support. - 📈 The findings advocate for continued exploration of AI capabilities in delivering accurate medical information, which may lead to improved patient outcomes. 🗄️: [#ChatGPT #HelicobacterPylori #ArtificialIntelligence #MedicalEducation #PatientCare #DataInsights #AIinHealthcare]

👉🏼 Pre-trained Language Models in Medicine: A Survey 🤓 Xudong Luo https://lnkd.in/eubyPuA5 🔍 Focus on data insights: - Pre-trained Language Models (PLMs) like BERT, BioBERT, and ChatGPT have shown potential in medical NLP tasks. - Survey covers achievements and methodologies of applying PLMs in medical NLP tasks. 💡 Main outcomes and implications: - Overview of basic concepts, methodologies, advantages, and steps of applying PLMs in medical NLP tasks. - Summary of recent research findings, strengths vs weaknesses, and potential limitations. 📚 Field significance: - Enhancing models' reliability, explainability, and fairness for promoting PLMs in clinical practice. 🗄️: #NaturalLanguageProcessing #PretrainedModels #MedicalNLP #DataInsights

👉🏼 GENEVIC: GENetic data exploration and visualization via intelligent interactive console 🤓 Anindita Nath 👇🏻 https://lnkd.in/ejQG-BX5 🔍 Focus on data insights: - 📊 GENEVIC automates the analysis of genetic data, significantly reducing the time researchers spend on data interpretation. - 🔍 It integrates multiple data sources, allowing for comprehensive searches across PubMed, Google Scholar, and arXiv, enhancing literature review efficiency. - 🧬 The tool prioritizes genetic variants linked to complex diseases, aiding in targeted research and potential therapeutic developments. 💡 Main outcomes and implications: - 🚀 By streamlining the process of genetic data exploration, GENEVIC empowers researchers to make informed decisions quickly, potentially accelerating biomedical discoveries. - 🛠️ The user-friendly interface democratizes access to advanced genetic analysis tools, making them available to researchers without specialized training. - 🔒 With HIPAA-compliant infrastructure, GENEVIC ensures that sensitive genetic data is handled securely, fostering trust in its use within the biomedical community. 📚 Field significance: - 🌐 GENEVIC represents a significant advancement in bioinformatics, bridging the gap between data generation and actionable biomedical insights. - 🧩 The integration of generative AI into genetic research tools marks a transformative shift in how researchers interact with complex datasets. - 📈 As a prototype, GENEVIC sets a precedent for future innovations in genetic data analysis, paving the way for more sophisticated tools in the field. 🗄️: [#bioinformatics #geneticdata #AI #biomedicalresearch #dataanalysis #geneticvariants #healthcare #machinelearning]

👉🏼 Physics-informed genetic programming for discovery of partial differential equations from scarce and noisy data 🤓 Benjamin G Cohen 👇🏻 https://lnkd.in/erTv89Nx 🔍 Focus on data insights: - 📈 The proposed framework identifies free-form partial differential equations

Today the most important article in chatgpt and "ChatGPT-based Biological and Psychological Data Imputation" - Utilizes ChatGPT, a powerful Large Language Model (LLM) developed by OpenAI, for biological and psychological data imputation. - Addresses the common problem of missing data in large cohort or longitudinal research. - Demonstrates superior efficacy of ChatGPT compared to traditional imputation techniques through comparative analyses. - Evaluates performance using metrics like Pearson correlation coefficient, relative accuracy, and mean absolute error. - Customized data-to-text prompting engineering enhances ChatGPT's ability to capture intricate patterns within biological data. - Fine-tuning ChatGPT with domain-specific biological vocabulary improves the accuracy and relevance of imputations. #chatgpt #biologicaldata #psychologicaldata #dataimputation #OpenAI