A modified decision tree approach to improve the prediction and mutation discovery for drug resistance in Mycobacterium tuberculosis.
A modified decision tree approach to improve the prediction and mutation discovery for drug resistance in Mycobacterium tuberculosis.
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DOI：
10.1186/s12864-022-08291-4
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发表时间：
2022-01-11
期刊：
影响因子：
4.4
通讯作者：
Clark TG
中科院分区：
文献类型：
--
作者：
Deelder W;Napier G;Campino S;Palla L;Phelan J;Clark TG
耐药结核分枝杆菌使结核病（TB）的有效治疗和控制复杂化。随着全基因组测序作为诊断工具的采用，机器学习方法被用来预测M。结核病耐药性和确定潜在的基因突变。然而，机器学习方法可能会过度拟合，如果它们被开箱即用并且不适应疾病特定的背景，则无法识别因果突变。我们引入了一种针对结核病环境定制的机器学习方法，该方法提取了个体研究中重复出现的基因组变异库，以改善基因型分析。我们开发了一种定制的决策树方法，称为Treesist-TB，通过提取和评估多项研究中的基因组变异来进行结核病耐药性预测。将Treesist-TB应用于已知耐药突变的利福平（RIF）、异烟肼（INH）和乙胺丁醇（EMB）药物，显示出与广泛使用的TB-Profiler工具相似的预测准确性水平（Treesist-TB与TB-Profiler工具：RIF 97.5%与97.6%; INH 96.8%与96.5%; EMB 96.8%与95.8%）。将Treesist-TB应用于不太了解的二线药物乙硫异烟胺（ETH）、环丝氨酸（CYS）和对氨基水杨酸（PAS），导致鉴定出新的变体（分别为52、6和11），其中大量变体在TB-Profiler文库中缺失（分别为45、4和6）。因此，Treesist-TB具有改善的预测灵敏度（Treesist-TB对比TB-Profiler工具：PAS 64.3%对比38.8%; CYS 45.3%对比30.7%; ETH 72.1%对比71.1%）。我们的工作加强了机器学习在耐药性预测中的实用性，同时强调了针对特定疾病背景定制方法的必要性。通过在一系列抗结核药物中应用改进的决策学习方法（Treesist-TB），我们确定了具有高预测能力的合理耐药编码基因组变体，同时可能克服可能影响标准机器学习应用的过拟合挑战。在线版本包含补充材料，可通过10.1186/s12864-022-08291-4获得。
Drug resistant Mycobacterium tuberculosis is complicating the effective treatment and control of tuberculosis disease (TB). With the adoption of whole genome sequencing as a diagnostic tool, machine learning approaches are being employed to predict M. tuberculosis resistance and identify underlying genetic mutations. However, machine learning approaches can overfit and fail to identify causal mutations if they are applied out of the box and not adapted to the disease-specific context. We introduce a machine learning approach that is customized to the TB setting, which extracts a library of genomic variants re-occurring across individual studies to improve genotypic profiling. We developed a customized decision tree approach, called Treesist-TB, that performs TB drug resistance prediction by extracting and evaluating genomic variants across multiple studies. The application of Treesist-TB to rifampicin (RIF), isoniazid (INH) and ethambutol (EMB) drugs, for which resistance mutations are known, demonstrated a level of predictive accuracy similar to the widely used TB-Profiler tool (Treesist-TB vs. TB-Profiler tool: RIF 97.5% vs. 97.6%; INH 96.8% vs. 96.5%; EMB 96.8% vs. 95.8%). Application of Treesist-TB to less understood second-line drugs of interest, ethionamide (ETH), cycloserine (CYS) and para-aminosalisylic acid (PAS), led to the identification of new variants (52, 6 and 11, respectively), with a high number absent from the TB-Profiler library (45, 4, and 6, respectively). Thereby, Treesist-TB had improved predictive sensitivity (Treesist-TB vs. TB-Profiler tool: PAS 64.3% vs. 38.8%; CYS 45.3% vs. 30.7%; ETH 72.1% vs. 71.1%). Our work reinforces the utility of machine learning for drug resistance prediction, while highlighting the need to customize approaches to the disease-specific context. Through applying a modified decision learning approach (Treesist-TB) across a range of anti-TB drugs, we identified plausible resistance-encoding genomic variants with high predictive ability, whilst potentially overcoming the overfitting challenges that can affect standard machine learning applications. The online version contains supplementary material available at 10.1186/s12864-022-08291-4.
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影响因子：
4.3
作者：
Libiseller-Egger J;Phelan J;Campino S;Mohareb F;Clark TG
通讯作者：
Clark TG
影响因子：
11.5
作者：
Trauner, Andrej;Borrell, Sonia;Reither, Klaus;Gagneux, Sebastien
通讯作者：
Gagneux, Sebastien
影响因子：
30.8
作者：
Safi H;Lingaraju S;Amin A;Kim S;Jones M;Holmes M;McNeil M;Peterson SN;Chatterjee D;Fleischmann R;Alland D
通讯作者：
Alland D
结核分枝杆菌中与环丝氨酸抗性相关的新突变的鉴定。
影响因子：
5.2
作者：
Chen, Jiazhen;Zhang, Shuo;Zhang, Ying
通讯作者：
Zhang, Ying
DOI：
10.1093/bioinformatics/btx801
发表时间：
2018-05-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Yang Y;Niehaus KE;Walker TM;Iqbal Z;Walker AS;Wilson DJ;Peto TEA;Crook DW;Smith EG;Zhu T;Clifton DA
通讯作者：
Clifton DA