Machine learning approaches for clinical diagnosis of autoimmune diseases
用于自身免疫性疾病临床诊断的机器学习方法
基本信息
- 批准号:2876514
- 负责人:
- 金额:--
- 依托单位:
- 依托单位国家:英国
- 项目类别:Studentship
- 财政年份:2020
- 资助国家:英国
- 起止时间:2020 至 无数据
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
Genetic risk factors for some autoimmune conditions implicate T cells in disease mechanisms that are incompletely understood. The T-cell receptor (TCR) is encoded by genes that are recombined from an assortment of gene segments in the nuclei of T cells. The vastly diverse TCR repertoire arising from an individual's T cells evolved to bind to a wide variety of threats. T cell activation is initiated by TCR binding, which leads to clonal expansion. A lineage of T cells expressing the same TCR includes cells that participate in an active immune response, and some that persist to enable immunological memory. In autoimmune disease, T cells may be involved in an immune response directed against the host's own tissues or microbiome. Next generation sequencing has enabled vast libraries of TCRs to be sequenced, which presents a unique opportunity to better understand autoimmune disease. From a set of TCR repertoire samples, patterns associated with a condition might be identifiable through interpretation of a machine learning classification model. However, the limited sharing of identical TCRs between individuals with the same condition, as well as the vast outnumbering of samples by unique TCR sequences, leads to difficulty identifying signatures of TCR repertoires that are predictive of autoimmune disease status. Promising TCR repertoire classification approaches consider relationships between non-identical TCR sequences. Methods that split TCR sequences into kmers demonstrate efficient performance that is comparable to deep learning. This work is dedicated to investigating the utility of methods that augment kmer-based representations of the TCR repertoire. Throughout, methodology is evaluated using real TCR repertoire datasets including samples from patients with coeliac disease and inflammatory bowel disease, as well as participants with cytomegalovirus infection. TCR repertoires are also simulated to guide methodological development. To assess the hypothesis that capturing similarity of kmers in a TCR repertoire representation will improve generalisability, a novel approach employing a reduced amino acid alphabet is benchmarked against alternatives to reveal the limited utility of property-informed kmers alone. However, one exception when classifying TCR repertoires from a rarer subset of T cells in the small intestine by coeliac disease status suggests that appropriate use cases may exist for the approach. Next, the notion that some kmers may be more informative than others leads to exploration of a deviation-based kmer filter, which indicates that adequate regularisation precludes the need for filtering. Further, a likelihood-based normalisation of kmer counts is found to be sensitive to inaccuracies that one might expect in real TCR repertoire data. Methodology presented in this thesis may improve generalisability of certain TCR repertoire classification models, though this cannot be concluded universally. While results demonstrate the potential to identify TCR repertoire patterns that might be associated with autoimmune disease, further development of TCR repertoire classification approaches is warranted in coordination with more advanced TCR repertoire sequencing techniques. The ability to gain insights into the underlying mechanisms of autoimmune disease will also rely on experimental validation.
一些自身免疫性疾病的遗传风险因素涉及T细胞的疾病机制尚不完全清楚。T细胞受体(TCR)由从T细胞核中的基因片段的分类重组的基因编码。从个体的T细胞中产生的极其多样化的TCR库进化为与各种各样的威胁结合。T细胞活化由TCR结合引发,这导致克隆扩增。表达相同TCR的T细胞谱系包括参与主动免疫应答的细胞,以及一些持续存在以实现免疫记忆的细胞。在自身免疫性疾病中,T细胞可能参与针对宿主自身组织或微生物组的免疫应答。下一代测序使大量TCR文库能够被测序,这为更好地了解自身免疫性疾病提供了独特的机会。从一组TCR库样本中,可以通过机器学习分类模型的解释来识别与病症相关联的模式。然而,在具有相同病症的个体之间有限地共享相同的TCR,以及独特的TCR序列在数量上大大超过样品,导致难以鉴定预测自身免疫性疾病状态的TCR库的特征。有前途的TCR库分类方法考虑不同TCR序列之间的关系。将TCR序列拆分为kmer的方法表现出与深度学习相当的高效性能。这项工作致力于调查的效用的方法,增加kmer为基础的代表性的TCR剧目。在整个过程中,使用包括来自患有乳糜泻和炎性肠病的患者以及患有巨细胞病毒感染的参与者的样品的真实的TCR库数据集来评价方法。TCR剧目也模拟,以指导方法的发展。为了评估在TCR库表示中捕获kmers的相似性将提高概括性的假设,采用减少的氨基酸字母表的新方法与替代品进行基准测试,以揭示单独的性质知情的kmers的有限效用。然而,当通过腹腔疾病状态对来自小肠中的T细胞的较罕见子集的TCR库进行分类时的一个例外表明,该方法可能存在适当的用例。接下来,一些kmer可能比其他kmer信息量更大的概念导致了基于偏差的kmer过滤器的探索,这表明适当的正则化排除了过滤的需要。此外,发现kmer计数的基于可能性的标准化对在真实的TCR库数据中可能预期的不准确性敏感。本文提出的方法可能会提高某些TCR库分类模型的通用性,但这并不能普遍得出结论。虽然结果证明了鉴定可能与自身免疫性疾病相关的TCR库模式的潜力,但需要与更先进的TCR库测序技术协调,进一步开发TCR库分类方法。深入了解自身免疫性疾病的潜在机制的能力也将依赖于实验验证。
项目成果
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其他文献
吉治仁志 他: "トランスジェニックマウスによるTIMP-1の線維化促進機序"最新医学. 55. 1781-1787 (2000)
Hitoshi Yoshiji 等:“转基因小鼠中 TIMP-1 的促纤维化机制”现代医学 55. 1781-1787 (2000)。
- DOI:
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LiDAR Implementations for Autonomous Vehicle Applications
- DOI:
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2021 - 期刊:
- 影响因子:0
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吉治仁志 他: "イラスト医学&サイエンスシリーズ血管の分子医学"羊土社(渋谷正史編). 125 (2000)
Hitoshi Yoshiji 等人:“血管医学与科学系列分子医学图解”Yodosha(涉谷正志编辑)125(2000)。
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Effect of manidipine hydrochloride,a calcium antagonist,on isoproterenol-induced left ventricular hypertrophy: "Yoshiyama,M.,Takeuchi,K.,Kim,S.,Hanatani,A.,Omura,T.,Toda,I.,Akioka,K.,Teragaki,M.,Iwao,H.and Yoshikawa,J." Jpn Circ J. 62(1). 47-52 (1998)
钙拮抗剂盐酸马尼地平对异丙肾上腺素引起的左心室肥厚的影响:“Yoshiyama,M.,Takeuchi,K.,Kim,S.,Hanatani,A.,Omura,T.,Toda,I.,Akioka,
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