Ai in life

Importance of Understanding Perturbations at the Single-Cell Level Understanding gene perturbations at the single-cell level is crucial for identifying cellular mechanisms and their roles in health and disease. Studying how individual cells respond to various changes, including genetic, pharmacological, or environmental alterations, provides valuable insights into cellular functions and disease onset. This level of detail is essential for developing effective and targeted treatments. Emergence of Simulation Methods for Perturbation Analysis The increasing availability of biological data at the single-cell level has led to the development of computational simulation methods for gene perturbations. These methods are powerful tools for examining the effects of various perturbations without the need for physical experiments. Challenges in Evaluating Simulation Methods The diversity of simulation methods and the lack of standard evaluation criteria make it difficult to assess and compare th...

  The article "Biological Robots: Perspectives on an Emerging Interdisciplinary Field" explores the evolving field of biological robots, highlighting the interdisciplinary nature of this research area. Here are the key points discussed in the article: Redefining Robotics : The field of robotics is moving beyond traditional definitions, focusing on creating useful, semi-autonomous or fully autonomous artifacts that mimic living organisms. This shift challenges the classical approach of using inert, non-living materials and emphasizes the need for new terminology to describe these advancements. Biohybrid Structures : Recent work in biohybrid constructs, which are machines made from cells, has led to a dynamic and emerging field. These structures, such as those made from cellular materials, are pushing the boundaries of what is considered a robot. The authors discuss the potential applications of these biohybrid machines, from regenerative medicine to synthetic living machines. ...

 A recently conducted study introduces a novel deep learning framework called scNET, designed to overcome the limitations present in the analysis of single-cell RNA sequencing (scRNA-seq) data. Analyzing the activation of pathways and molecular complexes under various biological conditions is crucial for understanding the changes observed in comparative systems analyses. Traditional co-expression-based methods, which were successful in bulk RNA sequencing, have shown less effectiveness in scRNA-seq data due to its zero-inflated nature and reduced correlation. The scNET framework offers an innovative approach by integrating scRNA-seq data with protein-protein interaction (PPI) networks. This framework leverages the inherent duality of the data, where cells are viewed as vectors of gene expression and genes as vectors of expression across different cells. The proposed model is an autoencoder based on a graph neural network (GNN) architecture, comprising two graphs (one for relationsh...

A report on the paper "Deep GONet: self-explainable deep neural network based on Gene Ontology for phenotype prediction from gene expression data" is provided below: This paper proposes a new deep learning model called Deep GONet for phenotype prediction based on gene e xpression data. In the discussion section, the authors emphasize that the goal of interpreting this model is to explain its 1 operational mechanism, rather than necessarily how biological processes function. They point out that there may not always be a direct relationship between the biologically interpreted functions and the predicted phenotype, but this does not necessarily mean that the predictions are unreliable. The model seeks to find correlations between input and output, not causal relationships. If a function that appears unrelated to the phenotype is returned, it is possible that this function has an indirect correlation or is linked to the phenotype through an unknown causal relationship. However,...

  The report examines the role and potential of artificial intelligence methods, particularly large language models (LLMs), in the process of developing and maintaining ontologies. It initially emphasizes that the success of these methods significantly depends on the decades-long efforts of human experts in creating and managing ontologies, as these ontologies are included in the training data of LLMs. The challenges in evaluating the performance of AI in this field include limitations arising from the training data cutoff date of the models and the subjective nature of ontology construction. Accurate evaluation requires using terms not present in the models' training data and leveraging human expertise to determine the correctness and quality of ontologies generated or suggested by AI. The "Future Directions" section proposes strategies to improve the use of AI, such as customizing the Retrieval Augmented Generation (RAG) method to prioritize higher-quality terms and uti...

The SVLearn method, a machine learning approach using a dual reference, has been introduced as a practical solution for the accurate genotyping of structural variants (SVs). By adding an alternative genome reference to the standard reference genome, this method significantly improves SV genotyping performance. Compared to traditional methods using only a single reference genome, SVLearn has increased the number of short reads mapped to SV loci by up to 45.56%. This dual-reference approach, not previously employed in similar tools, distinguishes SVLearn from other methods. One of SVLearn's strengths is its superior performance in genotyping insertion variants. While previous tools faced challenges in accurately identifying insertions, SVLearn demonstrates comparable ability in genotyping SVs in both insertion and deletion regions. SVLearn utilizes multi-source features, including genomic information, alignments, and genotyping statistics, to train its machine learning models. Fea...