Robotics & Machine Learning Daily News2024,Issue(Jun.20) :14-15.

Hangzhou Dianzi University Reports Findings in Klebsiella pneumoniae (Protein fu nction annotation and virulence factor identification of Klebsiella pneumoniae g enome by multiple machine learning models)

杭州电子大学报道肺炎克雷伯菌的发现(基于多机器学习模型的肺炎克雷伯菌基因组蛋白功能注释和毒力因子鉴定)

Robotics & Machine Learning Daily News2024,Issue(Jun.20) :14-15.

Hangzhou Dianzi University Reports Findings in Klebsiella pneumoniae (Protein fu nction annotation and virulence factor identification of Klebsiella pneumoniae g enome by multiple machine learning models)

杭州电子大学报道肺炎克雷伯菌的发现(基于多机器学习模型的肺炎克雷伯菌基因组蛋白功能注释和毒力因子鉴定)

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摘要

由一名新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-革兰氏阴性杆菌的新研究-肺炎克雷伯菌是一篇报道的主题。据《新闻日报》记者报道,“肺炎克雷伯菌是一种革兰阴性杆菌,可引起人体多种感染。近年来,越来越多的肺炎克雷伯菌对多种抗生素耐药,对公众健康构成严重威胁。”新闻记者引用杭州电子大学的一篇研究报道:“该菌的蛋白质功能尚不清楚,迫切需要对肺炎克雷伯菌蛋白质组进行系统的研究。本研究对该菌的蛋白质功能进行了重新诠释,并对其功能群进行了分析。”结果:肺炎克雷伯菌的16个未表征蛋白的功能首先通过序列比对得到解释,另外肺炎克雷伯菌蛋白与流感嗜血杆菌的同源性较高,与肺炎衣原体的同源性较低,与流感嗜血杆菌的同源性较低。通过对肺炎克雷伯菌的毒力因子分析,我们的模型在标准D ATASET中获得了0.901的高精度,并将我们的模型应用于肺炎克雷伯菌中,鉴定出39个毒力因子。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Gram-Negative Bacteria-Klebsiella pneumoniae is the subject of a report. According to news originati ng from Zhejiang, People's Republic of China, by NewsRx correspondents, research stated, "Klebsiella pneumoniae is a type of Gram-negative bacterium which can cause a range of infections in h uman. In recent years, an increasing number of strains of K. pneumoniae resistant to multiple antibiotics have emerged, posing a significant threat to public health." Our news journalists obtained a quote from the research from Hangzhou Dianzi Uni versity, "The protein function of this bacterium is not well known, thus a syste matic investigation of K. pneumoniae proteome is in urgent need. In this study, the protein functions of this bacter ia were re-annotated, and their function groups were analyzed. Moreover, three m achine learning models were built to identify novel virulence factors. Results s howed that the functions of 16 uncharacterized proteins were first annotated by sequence alignment. In addition, K. pneumoniae proteins share a high proportion of homology with Haemophilus influenzae and a low homology proportion with Chlamydia pneumoniae. By sequence analysis, 10 proteins were identified as potential drug targets fo r this bacterium. Our model achieved a high accuracy of 0.901 in the benchmark d ataset. By applying our models to K. pneumoniae, we identified 39 virulence factors in this pathogen."

Key words

Zhejiang/People's Republic of China/As ia/Biological Factors/Biological Toxins/Cyborgs/Drugs and Therapies/Emergin g Technologies/Enterobacteriaceae/Gammapro-teobacteria/Genetics/Gram-Negative Bacteria/Gram-Negative Facultatively Anaerobic Rods/Health and Medicine/Kleb siella/Klebsiella pneumoniae/Machine Learning/Proteobacteria/Virulence Facto rs

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
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