摘要
一位新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-机器学习的新研究是一篇报告的主题。根据Ne wsRx编辑在德国柏林的新闻报道，研究人员称：“分子编码及其在机器学习模型中的应用已经证明了在生物医学应用方面的重大突破，特别是在肽和蛋白质的分类方面。为此，我们提出了一种新的编码方法：可解释的基于碳的Neighborh oods阵列(iCAN)。”我们的新闻记者从Robert Koch Comporte的研究中获得了一句话，“旨在解决机器学习模型对更结构化和更灵活输入的需求，它捕获计数阵列中碳原子的邻域，并改进由此产生的编码对机器学习模型的效用。iCAN方法提供可解释的分子编码和表示，使分子邻域的比较成为可能。”识别Rep eating模式，并可视化给定数据集的相关热图。当再现大型生物医学肽分类研究时，它的表现优于先前的编码。当扩展到蛋白质时，它的表现优于基于领先结构的编码，在71%的数据集上。我们的方法提供了可以应用于所有有机分子的三元编码，包括外生氨基酸、环肽和更大的蛋白质。"使它在不同的领域和数据集中具有高度的通用性."
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting out of Berlin, Germany, by Ne wsRx editors, research stated, "Molecular encodings and their usage in machine l earning models have demonstrated significant breakthroughs in biomedical applica tions, particularly in the classification of peptides and proteins. To this end, we propose a new encoding method: Interpretable Carbon-based Array of Neighborh oods (iCAN)." Our news journalists obtained a quote from the research from Robert Koch Institu te, "Designed to address machine learning models' need for more structured and l ess flexible input, it captures the neighborhoods of carbon atoms in a counting array and improves the utility of the resulting encodings for machine learning m odels. The iCAN method provides interpretable molecular encodings and representa tions, enabling the comparison of molecular neighborhoods, identification of rep eating patterns, and visualization of relevance heat maps for a given data set. When reproducing a large biomedical peptide classification study, it outperforms its predecessor encoding. When extended to proteins, it outperforms a lead stru cture-based encoding on 71% of the data sets. Our method offers in terpretable encodings that can be applied to all organic molecules, including ex otic amino acids, cyclic peptides, and larger proteins, making it highly versati le across various domains and data sets."
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