利用单细胞数据预测临床预后。

IF 3.8 2区医学 Q2 ENGINEERING, BIOMEDICAL

IEEE Transactions on Biomedical Circuits and Systems Pub Date : 1900-01-01 DOI:10.1109/TBCAS.2016.2577641

M. Pouyan, V. Jindal, M. Nourani

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引用次数: 0

摘要

单细胞技术如流式细胞术(FCM)为复杂细胞系统如组织和器官的知识发现提供了有价值的生物学数据。FCM数据包含有关复杂细胞系统的细胞异质性的多维信息。将单细胞标记与这些系统的表型特性联系起来是可能的。在单细胞分析领域，细胞群体鉴定和临床结果预测是具有挑战性的问题。在本文中，我们提出了一种混合学习方法，利用样本的单细胞FCM数据来预测临床结果。所提出的方法在两个方面都是有效的:1)识别每个样本FCM数据中的细胞簇;2)预测每个受试者的临床结果(健康与不健康)。实验结果表明，该方法具有较好的鲁棒性。

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Clinical Outcome Prediction Using Single-Cell Data.

Single-cell technologies like flow cytometry (FCM) provide valuable biological data for knowledge discovery in complex cellular systems like tissues and organs. FCM data contains multi-dimensional information about the cellular heterogeneity of intricate cellular systems. It is possible to correlate single-cell markers with phenotypic properties of those systems. Cell population identification and clinical outcome prediction from single-cell measurements are challenging problems in the field of single cell analysis. In this paper, we propose a hybrid learning approach to predict clinical outcome using samples' single-cell FCM data. The proposed method is efficient in both i) identification of cellular clusters in each sample's FCM data and ii) predict clinical outcome (healthy versus unhealthy) for each subject. Our method is robust and the experimental results indicate promising performance.

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来源期刊

IEEE Transactions on Biomedical Circuits and Systems 工程技术-工程：电子与电气

CiteScore

10.00

自引率

13.70%

发文量

174

审稿时长

3 months

期刊介绍： The IEEE Transactions on Biomedical Circuits and Systems addresses areas at the crossroads of Circuits and Systems and Life Sciences. The main emphasis is on microelectronic issues in a wide range of applications found in life sciences, physical sciences and engineering. The primary goal of the journal is to bridge the unique scientific and technical activities of the Circuits and Systems Society to a wide variety of related areas such as: • Bioelectronics • Implantable and wearable electronics like cochlear and retinal prosthesis, motor control, etc. • Biotechnology sensor circuits, integrated systems, and networks • Micropower imaging technology • BioMEMS • Lab-on-chip Bio-nanotechnology • Organic Semiconductors • Biomedical Engineering • Genomics and Proteomics • Neuromorphic Engineering • Smart sensors • Low power micro- and nanoelectronics • Mixed-mode system-on-chip • Wireless technology • Gene circuits and molecular circuits • System biology • Brain science and engineering: such as neuro-informatics, neural prosthesis, cognitive engineering, brain computer interface • Healthcare: information technology for biomedical, epidemiology, and other related life science applications. General, theoretical, and application-oriented papers in the abovementioned technical areas with a Circuits and Systems perspective are encouraged to publish in TBioCAS. Of special interest are biomedical-oriented papers with a Circuits and Systems angle.