日期:
2020 年 — 2026 年
2020
2021
2022
2023
2024
2025
2026
影响因子:

ReverseGWAS identifies combined phenotypes associated with a genotype in GWA studies

反向全基因组关联分析(ReverseGWAS)用于识别全基因组关联研究中与基因型相关的组合表型。

Chindelevitch, Leonid; Hedman, Åsa K; Bichko, Dmitri; Ziemek, Daniel

Multivariable regression models improve accuracy and sensitive grading of antibiotic resistance mutations in Mycobacterium tuberculosis

多变量回归模型提高了结核分枝杆菌抗生素耐药突变的准确性和敏感性分级

Kulkarni, Sanjana G; Laurent, Sacha; Miotto, Paolo; Walker, Timothy M; Chindelevitch, Leonid; Nathanson, Carl-Michael; Ismail, Nazir; Rodwell, Timothy C; Farhat, Maha R

Reference-Free Variant Calling with Local Graph Construction with ska lo (SKA)

使用 ska lo (SKA) 进行基于局部图构建的无参考变异检测

Derelle, Romain; Madon, Kieran; Hellewell, Joel; Rodríguez-Bouza, Víctor; Arinaminpathy, Nimalan; Lalvani, Ajit; Croucher, Nicholas J; Harris, Simon R; Lees, John A; Chindelevitch, Leonid

Exploration of the genetic landscape of bacterial dsDNA viruses reveals an ANI gap amid extensive mosaicism

对细菌双链DNA病毒遗传图谱的探索揭示了广泛嵌合现象中存在的平均核苷酸缺失(ANI)缺口

Ndovie, Wanangwa; Havránek, Jan; Leconte, Jade; Koszucki, Janusz; Chindelevitch, Leonid; Adriaenssens, Evelien M; Mostowy, Rafal J

Quantitative prediction of disinfectant tolerance in Listeria monocytogenes using whole genome sequencing and machine learning

利用全基因组测序和机器学习对单核细胞增生李斯特菌的消毒剂耐受性进行定量预测

Gmeiner, Alexander; Ivanova, Mirena; Njage, Patrick Murigu Kamau; Hansen, Lisbeth Truelstrup; Chindelevitch, Leonid; Leekitcharoenphon, Pimlapas

All parts of the WHO Mycobacterium tuberculosis mutation catalog need to be applied when evaluating its performance

评估结核分枝杆菌的性能时,需要应用世界卫生组织结核分枝杆菌突变目录的所有部分。

Laurent, Sacha; Phelan, Jody E; Chindelevitch, Leonid; Walker, Timothy M; Cirillo, Daniela M; Suresh, Anita; Rodwell, Timothy C; Miotto, Paolo; Köser, Claudio U

BenchAMRking: a Galaxy-based platform for illustrating the major issues associated with current antimicrobial resistance (AMR) gene prediction workflows

BenchAMRking:一个基于 Galaxy 的平台,用于展示当前抗菌素耐药性 (AMR) 基因预测工作流程中的主要问题

Strepis, Nikolaos; Dollee, Dennis; Vrins, Donny; Vanneste, Kevin; Bogaerts, Bert; Carrillo, Catherine; Bharat, Amrita; Horan, Kristy; Sherry, Norelle L; Seemann, Torsten; Howden, Benjamin P; Hiltemann, Saskia; Chindelevitch, Leonid; Stubbs, Andrew P; Hays, John P

Mitigating antimicrobial resistance by innovative solutions in AI (MARISA): a modified James Lind Alliance analysis

利用人工智能创新解决方案缓解抗菌素耐药性(MARISA):一项改进的詹姆斯·林德联盟分析

Waldock, William J; Thould, Hannah; Chindelevitch, Leonid; Croucher, Nicholas J; de la Fuente, César; Collins, James J; Ashrafian, Hutan; Darzi, Ara

Seamless, rapid, and accurate analyses of outbreak genomic data using split k-mer analysis

利用分割k-mer分析法对疫情基因组数据进行无缝、快速、准确的分析

Derelle, Romain; von Wachsmann, Johanna; Mäklin, Tommi; Hellewell, Joel; Russell, Timothy; Lalvani, Ajit; Chindelevitch, Leonid; Croucher, Nicholas J; Harris, Simon R; Lees, John A

Optimising machine learning prediction of minimum inhibitory concentrations in Klebsiella pneumoniae

优化机器学习对肺炎克雷伯菌最低抑菌浓度的预测

Batisti Biffignandi, Gherard; Chindelevitch, Leonid; Corbella, Marta; Feil, Edward J; Sassera, Davide; Lees, John A