Machine-Learning Approach to Label-free Detection of new Bacterial Pathogens

无标记检测新细菌病原体的机器学习方法

• 批准号: 7896355
• 负责人: Murat Dundar
• 金额: $ 23.48万
• 依托单位: INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-05-15 至 2012-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7896355
• 关键词: Academia; Accounting; Address; Algorithms; American; Applications Grants; Bacteria; Benchmarking; Biochemical Process; Centers for Disease Control and Prevention (U.S.); Characteristics; Classification; Collaborations; Complex; Computer Vision Systems; Critiques; Data Set; Detection; Disease; Disease Outbreaks; Escherichia coli; Food; Food Supply; Frequencies; Future; Genus staphylococcus; Goals; Grant; Head; Industry; Infection; International; Knowledge; Label; Lasers; Learning; Libraries; Listeria; Machine Learning; Manuscripts; Medical; Methods; Modeling; Mutate; Mutation; Nature; Optics; Paper; Pathogenicity; Pattern; Pattern Recognition; Peer Review; Phenotype; Process; Productivity; Public Health; Published Comment; Qualifying; Reagent; Recording of previous events; Reporting; Research; Research Methodology; Safety; Salmonella; Sampling; Screening procedure; Secure; Serotyping; Solutions; Suggestion; System; Techniques; Technology; Test Result; Testing; Textbooks; Time; Training; Update; Validation; Vibrio; Work; base; cost; data mining; falls; fly; foodborne; foodborne pathogen; image processing; improved; interest; light scattering; new technology; novel strategies; optical sensor; pathogen; pathogenic bacteria; public health relevance; rapid detection; sensor; simulation; symposium; tool

DESCRIPTION (provided by applicant): We appreciate the time and effort spent by all the reviewers, and we are grateful for the useful comments and provided suggestions. We have carefully reviewed the critiques and we are happy to see that the panel was receptive to our proposal. The reviewers expressed three major concerns in the summary statement: (1) although the investigating team is well qualified our history of collaboration is short; (2) details regarding the practical constraints of the BARDOT system are lacking; (3) the machine learning techniques employed in the project are considered fairly standard. Below we briefly discuss the reviewers comments and indicate how we have changed our revised application to address the critique. (1) Dr. Dundar moved from industry to academia in the fall of 2008, at which point Dr. Rajwa (one of the original inventors of BARDOT) and Dr. Dundar began their collaboration on new approaches to the problem of non-exhaustively defined classes in phenotypic screening. This scientific partnership immediately produced interesting results, and at the time of submission of the original application, Dr. Dundar and Dr. Rajwa had their first manuscript under review. The approach presented in the original proposal was tested and the results were submitted to the ACM 15th Annual SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'09), which is the largest and one of the most respected conferences in this field. The manuscript was accepted after a full peer review as one of the 50 regular papers selected from 551 submissions [20]. Following the proposal submission, research efforts continued and produced yet another approach to the problem described in this grant application. The preliminary findings are reported in a new manuscript which is currently under review [4]. (2) We rewrote the background and research methods sections of our proposal to include information re- quested by the reviewers regarding practical aspects of the BARDOT system, such as accuracy issues (Section D.3.2), frequency of encountering new, unknown classes (Section B.3.1), and validation (Section D.3.1). (3) The problem of phenotypic screening and classification of bacteria can be defined within exhaustive (stan- dard) or non-exhaustive learning frameworks. Although we agree that the implementation of an exhaustive clas- sification approach for BARDOT does require only fairly standard tools, the problem of the non-exhaustive nature of training libraries cannot be addressed by straightforward use of any textbook-level technique. In fact, the presence of non-exhaustively defined set of classes violates basic assumptions for most supervised learning systems. The issue of non-exhaustively defined classes is the major obstacle for application of machine learning in phenotypic analysis since the number of possible phenotypes may be infinite. In our original proposal we argued that learning with a non-exhaustively defined set of classes remains a very challenging problem, and presented evidence demonstrating that simple extensions of standard techniques cannot provide an acceptable solution. Subsequently, we proposed a new approach based on Bayesian simulation of classes and showed that preliminary results outperformed benchmark techniques [4]. Although these initial results looked promising, we did not consider the described preliminary algorithms final and definitive, and we do not believe that at this point we are able to provide an exact algorithmic solution to this complex problem. If we were able to do that, it would mean that we had already accomplished all the grant goals. The very essence of the proposed research is finding the answer to the defined problem, and the answer will remain unknown until after the work has been done. However, positive reviews and an acceptance of our work by KDD'09 conference judges, tell us that we are heading in the right direction. In the amended version of this application we propose a modified Bayesian approach based on Wishart priors (Section D.2.3). The algorithm creates new classes on the fly and evaluates maximum likelihood with the updated set of classes, gradually improving detection accuracy for future samples. We believe that this offers a substantial improvement over the previous method. Consequently, the preliminary results in Section C are updated to reflect our progress. Since the modified technique allows for classification with non-exhaustive and exhaustive sets using the same algorithm, we consolidated the previous specific aims 3 and 5 into one in the revised application. PUBLIC HEALTH RELEVANCE: A Machine Learning Approach to Label-free Detection of Bacterial Pathogens using Laser Light Scattering PIs: Dr. M. Murat Dundar and Dr. Bartek Rajwa Successful implementation of this project will allow for a label-free detection and identification of food pathogens and their mutated subclasses not yet seen earlier. This will reduce the number of food related outbreaks and will help secure public food supply.

描述（由申请人提供）：我们感谢所有评审员所花费的时间和精力，我们非常感谢有用的意见和建议。我们仔细审查了这些批评，我们很高兴看到小组接受了我们的建议。评审人员在总结声明中表达了三个主要问题：（1）虽然调查团队非常合格，但我们的合作历史很短;（2）缺乏关于BARDOT系统实际限制的细节;（3）项目中使用的机器学习技术被认为是相当标准的。 下面我们简要讨论评审员的评论，并指出我们如何修改修改后的应用程序来解决批评。 (1)Dundar博士于2008年秋季从工业界转向学术界，此时Rajwa博士（BARDOT的原始发明者之一）和Dundar博士开始合作研究新方法，以解决表型筛选中非详尽定义的类别问题。这种科学合作立即产生了有趣的结果，在提交原始申请时，Dundar博士和Rajwa博士正在审查他们的第一份手稿。在最初的提案中提出的方法进行了测试，并将结果提交给ACM第15届知识发现和数据挖掘年度SIGKDD国际会议（KDD'09），这是该领域最大的和最受尊敬的会议之一。经过全面的同行评审后，该手稿被接受为从551份投稿中选出的50篇常规论文之一[20]。在提交提案后，研究工作继续进行，并提出了解决本赠款申请中所述问题的另一种方法。初步研究结果报告在一份新的手稿中，目前正在审查[4]。 (2)我们重写了我们提案的背景和研究方法部分，以包括评审员要求的关于BARDOT系统实际方面的信息，例如准确性问题（第D.3.2节）、遇到新的未知类的频率（第B.3.1节）和验证（第D.3.1节）。 (3)细菌的表型筛选和分类问题可以在穷举（标准）或非穷举学习框架内定义。尽管我们同意，BARDOT的详尽分类方法的实现确实只需要相当标准的工具，但培训库的非详尽性质的问题不能通过直接使用任何教科书级别的技术来解决。事实上，非穷举定义的类集合的存在违反了大多数监督学习系统的基本假设。非穷举定义类的问题是机器学习在表型分析中应用的主要障碍，因为可能的表型的数量可能是无限的。在我们最初的提议中，我们认为使用非穷举定义的类集进行学习仍然是一个非常具有挑战性的问题，并提出了证据证明标准技术的简单扩展无法提供可接受的解决方案。随后，我们提出了一种基于贝叶斯类模拟的新方法，并表明初步结果优于基准技术[4]。 虽然这些初步结果看起来很有希望，我们并不认为所描述的初步算法最终和确定的，我们不相信，在这一点上，我们能够提供一个精确的算法解决这个复杂的问题。如果我们能够做到这一点，这将意味着我们已经完成了所有的赠款目标。所提出的研究的本质是为所定义的问题找到答案，而答案在工作完成之前仍然是未知的。然而，KDD'09会议评委对我们工作的积极评价和认可告诉我们，我们正朝着正确的方向前进。 在本申请的修订版本中，我们提出了一种基于Wishart先验的改进贝叶斯方法（第D.2.3节）。该算法在运行中创建新的类，并使用更新的类集评估最大似然，逐渐提高未来样本的检测精度。我们认为，这比以前的方法有了很大的改进。因此，对C节中的初步结果进行了更新，以反映我们的进展。由于修改后的技术允许使用相同的算法进行非穷举和穷举集的分类，因此我们将先前的具体目标3和5合并为一个。 公共卫生相关性：使用激光散射PI的细菌病原体无标记检测的机器学习方法：M博士。穆拉特Dundar和Bartek Rajwa博士该项目的成功实施将允许无标签检测和识别食品病原体及其突变的子类，以前还没有见过。这将减少与食物有关的爆发次数，并有助于确保公共食品供应。