摘要翻译：
癌症的早期发现在医学上是一个具有挑战性的问题。肿瘤患者的血清中富含异质性分泌型脂质结合细胞外囊泡(EVs)，它提供了一系列复杂的信息和生物标志物，代表了他们的来源细胞，目前正在液体活检和癌症筛查领域进行研究。振动光谱为复杂生物样品的结构和生物物理特性的评估提供了非侵入性的方法。本研究对9例大肠癌、肝细胞癌、乳腺癌和胰腺癌患者和5例健康人血清中提取的EVs进行了多重拉曼光谱测量。FTIR（傅立叶变换红外）光谱测量作为拉曼分析的补充方法，对四个癌症亚型中的两个亚型进行了测量。AdaBoost随机森林分类器、决策树和支持向量机(SVM)将癌症EVs的基线校正拉曼光谱与健康对照的拉曼光谱（18个光谱）区分开来，当降低到1800-1940逆厘米的光谱频率范围并进行50：50训练：测试分裂时，分类准确率超过90%。对14个光谱的FTIR分类正确率为80%。我们的研究结果表明，基本机器学习算法是区分癌症患者和健康患者电动汽车复杂振动频谱的强大实用智能工具。这些实验方法有望成为人工智能辅助早期癌症筛查的有效和高效的液体活检。
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英文标题：
《Machine Learning Characterization of Cancer Patients-Derived
  Extracellular Vesicles using Vibrational Spectroscopies》
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作者：
Abicumaran Uthamacumaran, Samir Elouatik, Mohamed Abdouh, Michael
  Berteau-Rainville, Zu-hua Gao, and Goffredo Arena
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最新提交年份：
2021
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分类信息：

一级分类：Quantitative Biology        数量生物学
二级分类：Other Quantitative Biology        其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Physics        物理学
二级分类：Biological Physics        生物物理学
分类描述：Molecular biophysics, cellular biophysics, neurological biophysics, membrane biophysics, single-molecule biophysics, ecological biophysics, quantum phenomena in biological systems (quantum biophysics), theoretical biophysics, molecular dynamics/modeling and simulation, game theory, biomechanics, bioinformatics, microorganisms, virology, evolution, biophysical methods.
分子生物物理、细胞生物物理、神经生物物理、膜生物物理、单分子生物物理、生态生物物理、生物系统中的量子现象（量子生物物理）、理论生物物理、分子动力学/建模与模拟、博弈论、生物力学、生物信息学、微生物、病毒学、进化论、生物物理方法。
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英文摘要：
  The early detection of cancer is a challenging problem in medicine. The blood sera of cancer patients are enriched with heterogeneous secretory lipid bound extracellular vesicles (EVs), which present a complex repertoire of information and biomarkers, representing their cell of origin, that are being currently studied in the field of liquid biopsy and cancer screening. Vibrational spectroscopies provide non-invasive approaches for the assessment of structural and biophysical properties in complex biological samples. In this pilot study, multiple Raman spectroscopy measurements were performed on the EVs extracted from the blood sera of 9 patients consisting of four different cancer subtypes (colorectal cancer, hepatocellular carcinoma, breast cancer and pancreatic cancer) and five healthy patients (controls). FTIR (Fourier Transform Infrared) spectroscopy measurements were performed as a complementary approach to Raman analysis, on two of the four cancer subtypes.   The AdaBoost Random Forest Classifier, Decision Trees, and Support Vector Machines (SVM) distinguished the baseline corrected Raman spectra of cancer EVs from those of healthy controls (18 spectra) with a classification accuracy of above 90 percent when reduced to a spectral frequency range of 1800 to 1940 inverse cm and subjected to a 50:50 training: testing split. FTIR classification accuracy on 14 spectra showed an 80 percent classification accuracy. Our findings demonstrate that basic machine learning algorithms are powerful applied intelligence tools to distinguish the complex vibrational spectra of cancer patient EVs from those of healthy patients. These experimental methods hold promise as valid and efficient liquid biopsy for artificial intelligence-assisted early cancer screening.
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PDF链接：
https://arxiv.org/pdf/2107.10332