Ai in life

 The article "Deep learning imputes DNA methylation states in single cells and enhances the detection of epigenetic alterations in schizophrenia" introduces scMeFormer , a novel Artificial Intelligence (AI) tool based on transformers, designed for the accurate and efficient reconstruction of DNA methylation (DNAm) data at the single-cell level. Problem: Existing single-cell DNAm profiling technologies face a high rate of missing data , limiting the accuracy of epigenetic analyses. Solution: scMeFormer , with its transformer architecture, solves this problem by offering unparalleled scalability, high computational efficiency, and transfer learning capabilities. Key Highlights of scMeFormer: High scalability and speed for analyzing large single-cell datasets. Accurate reconstruction of DNAm data even with low data coverage. Transfer learning capability for easier application in new studies. Quality control tool to ensure the accuracy of reconstructed data. Applicati...

  The article "A Multi-Modal AI Copilot for Single-Cell Analysis with Instruction Following" introduces InstructCell , a novel AI tool designed to simplify the analysis of complex single-cell RNA sequencing (scRNA-seq) data . While large language models excel at understanding natural language and scRNA-seq is considered the "language of cellular biology," traditional tools for analyzing this data are often difficult and inefficient for researchers. InstructCell , a multi-modal AI assistant , addresses this issue by using natural language as a user interface. Leveraging a comprehensive dataset and a multi-modal cell language architecture, it can simultaneously interpret and process both text and scRNA-seq data . InstructCell empowers researchers to perform critical tasks such as cell type annotation, conditional pseudo-cell generation, and drug sensitivity prediction using simple natural language commands . Evaluations demonstrate that this tool exhibits competiti...

  The integration of ChatGPT into biological research has sparked a paradigm shift, enabling scientists to streamline workflows, generate novel hypotheses, and accelerate discoveries across various subfields. Since its public release in 2022, this large language model (LLM) has become an indispensable tool for computational biologists, geneticists, and drug developers. Its capabilities range from automating literature synthesis to facilitating CRISPR system design. Drawing from peer-reviewed studies, preprints, and industry reports published between 2023 and 2025, this review explores ten critical domains where ChatGPT is reshaping biological research, evaluates its current limitations, and projects future trajectories for AI-driven innovation in the life sciences. Revolutionizing Literature Review and Knowledge Synthesis Automated Extraction of Biological Insights ChatGPT’s ability to parse and summarize vast scientific corpora addresses one of biology’s most pressing challenges: ...

This report summarizes the findings of a review study examining the role of ChatGPT in the field of sports-related injuries. The study reveals that ChatGPT exhibits the capacity to generate information pertaining to various types of sports injuries, which is consistent with established scientific knowledge. However, limitations were identified in its ability to provide specialized details and up-to-date statistical data. Concerns regarding ethical considerations and data privacy associated with the application of this technology in sports were also raised. Despite these limitations, ChatGPT demonstrates significant potential as a preliminary information resource for athletes and medical professionals, although it should not be considered a replacement for human expertise. The development of more advanced and specialized AI systems for the management of sports injuries is recommended. Source: The role of ChatGPT in sports trauma: a mini review on strengths and limits of open AI appli...

  A recent study evaluated five large language models (LLMs), including GPT-4, for discovering common functions within gene sets. GPT-4 suggested functions similar to the gene set name in Gene Ontology in 73% of cases, with predictions made with high confidence showing higher accuracy. Additionally, GPT-4 was able to identify common functions in 45% of gene clusters from omics data, which is lower than functional enrichment , but with higher specificity and gene coverage. These results suggest that large language models can be valuable tools for analyzing genetic and omics data. Source: Evaluation of large language models for discovery of gene set function .

  A recent study explored the ability of ChatGPT to design resistance training programs for individuals with intermediate and advanced training levels. In this research, GPT-3.5 and GPT-4.0 were asked to create 12-week training programs. The results showed that both versions were capable of generating programs with standard intensity and repetition ranges, but GPT-4.0 performed better in creating more complex programs suited to different levels. However, the generated programs still require refinement and adjustments by experts to ensure optimal effectiveness and to prevent potential injuries. 📌 Source: Artificial intelligence in sport: Exploring the potential of using ChatGPT in resistance training prescription published in Biology of Sport

  A recent study evaluated two AI models, Gemini and GPT-4o, as tools for laboratory training in DNA subcloning. The study highlighted the strengths and weaknesses of both models in assisting laboratory procedures. Gemini demonstrated the ability to provide images and diagrams that enhanced the understanding of experimental steps, but it occasionally showed inaccuracies in material quantities and procedural steps. GPT-4o , on the other hand, excelled in providing real-time, interactive support and responding accurately to queries, though it faced issues with image functionality. Additionally, GPT-4o had superior memory retention, automatically adjusting responses based on previously provided information. Both models had limitations, such as incorrect reagent volume calculations or suggesting potentially risky methods. However, these AI tools hold significant potential to improve precision, standardization, and error reduction in laboratory training. The study suggests that AI can...

 Artificial Intelligence (AI) is transforming the sports industry, enhancing both athlete performance and injury prevention. Tools like ChatGPT and Gemini are changing how coaches, trainers, and sports scientists approach training, recovery, and competition. Introduction to AI in Sports : AI is revolutionizing the sports world by providing data-driven insights that help athletes perform at their peak. Through advanced analytics, machine learning, and AI-driven simulations, sports professionals are gaining deeper insights into athletic performance, biomechanics, and injury prevention. Improving Athletic Performance with AI : AI technologies such as ChatGPT and Gemini analyze data from wearable devices, motion sensors, and video footage to track athletes’ performance. These tools help coaches design personalized training programs, optimize techniques, and predict performance outcomes. Whether it's a sprinter refining their stride or a soccer player optimizing their kicking technique,...

  Artificial Intelligence (AI) is rapidly reshaping the landscape of biology. In particular, AI models like ChatGPT and Gemini are revolutionizing how we analyze biological data, model proteins, and predict genetic variations. But how exactly is AI making an impact in this field? Introduction to AI in Biology : AI technologies, such as ChatGPT and Gemini, have proven invaluable in accelerating biological research. By harnessing vast datasets, these AI models help biologists uncover hidden patterns, simulate complex biological processes, and even predict the efficacy of drugs or therapies. Applications in Genomics : AI plays a pivotal role in genomics, enabling researchers to analyze genetic data more efficiently. With the help of machine learning algorithms, scientists can identify gene variations associated with diseases, predict genetic predispositions, and improve personalized medicine approaches. AI in Drug Discovery : AI technologies like Gemini can help simulate molecular int...