Scholarlink

<p>As various technological innovations are assisting medical science in a considerable way, rendering a significant leap towards ‘lab-to-land’ delivery, in a similar vein, algorithm development and concomitant framework-based approaches help the field to enrich its patient care. Although antimicrobial drugs revolutionized this particular area, antimicrobial resistance is a pressing global health concern as microbial strains are becoming resistant to conventional antibiotics, undermining the efficacy of these drugs and leading to increased illness and healthcare costs. To tackle this menace, apart from technological innovations such as diagnostic kits, an informatics-based framework approach is the call of the day. Despite the emergence of several computational approaches, they lack in generalization, scope, and scalability. Here, we have developed a novel framework StaLAENet (<Emphasis Type="Underline">sta</Emphasis>cked <Emphasis Type="Underline">L</Emphasis>STM-nested deep-<Emphasis Type="Underline">a</Emphasis>uto<Emphasis Type="Underline">e</Emphasis>ncoder <Emphasis Type="Underline">net</Emphasis>work) to predict antibiotic-resistant gene drug classes targeting ESKAPE pathogens. This framework comprises two modules: a feature representation module comprising a stacked LSTM-nested deep autoencoder and a classification module that leverages a dense network using latent features. StaLAENet demonstrated an efficient performance – accuracy: 0.938±0.043, specificity: 0.888±0.061, precision: 0.912±0.020, and recall: 0.881±0.021 – for <i>Enterococcus faecium</i> using 4-mer data, with similar results for other organisms using various <i>k</i>-mer data. Comparative analysis confirmed its superiority over existing pipelines. Further, independent evaluation with non-redundant sequences (sourced from another database) and with a metagenomic dataset highlighted its generalizability, robustness, and capability to analyze complex microbial communities. StaLAENet can offer a robust solution for combating AMR, enabling an efficient way of antimicrobial stewardship and patient care.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

StaLAENet: A stacked LSTM-nested deep-autoencoder network for identification of antimicrobial resistance of nosocomial pathogens

  • 期刊论文

  • 发表日期：

    2026年4月7日

查看全文
  • Mousumi Banerjee,
  • Abhishake Lahiri,
  • Sohom Basak,
  • Saptarshi Das,
  • Subhasis Mukhopadhyay,
  • Rachana Banerjee,
  • Kausik Basak

摘要

As various technological innovations are assisting medical science in a considerable way, rendering a significant leap towards ‘lab-to-land’ delivery, in a similar vein, algorithm development and concomitant framework-based approaches help the field to enrich its patient care. Although antimicrobial drugs revolutionized this particular area, antimicrobial resistance is a pressing global health concern as microbial strains are becoming resistant to conventional antibiotics, undermining the efficacy of these drugs and leading to increased illness and healthcare costs. To tackle this menace, apart from technological innovations such as diagnostic kits, an informatics-based framework approach is the call of the day. Despite the emergence of several computational approaches, they lack in generalization, scope, and scalability. Here, we have developed a novel framework StaLAENet (stacked LSTM-nested deep-autoencoder network) to predict antibiotic-resistant gene drug classes targeting ESKAPE pathogens. This framework comprises two modules: a feature representation module comprising a stacked LSTM-nested deep autoencoder and a classification module that leverages a dense network using latent features. StaLAENet demonstrated an efficient performance – accuracy: 0.938±0.043, specificity: 0.888±0.061, precision: 0.912±0.020, and recall: 0.881±0.021 – for Enterococcus faecium using 4-mer data, with similar results for other organisms using various k-mer data. Comparative analysis confirmed its superiority over existing pipelines. Further, independent evaluation with non-redundant sequences (sourced from another database) and with a metagenomic dataset highlighted its generalizability, robustness, and capability to analyze complex microbial communities. StaLAENet can offer a robust solution for combating AMR, enabling an efficient way of antimicrobial stewardship and patient care.