中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
ELF: Extract Landmark Features By Optimizing Topology Maintenance, Redundancy, and Specificity

文献类型:期刊论文

作者Feng, Zhan-Ying2,3; Wang, Yong1,2,3
刊名IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
出版日期2020-03-01
卷号17期号:2页码:411-421
ISSN号1545-5963
关键词Feature extraction Topology Ground penetrating radar Geophysical measurement techniques Redundancy Maintenance engineering Manganese Feature selection topology maintenance optimization single cell
DOI10.1109/TCBB.2018.2846225
英文摘要Feature selection is the process of selecting a subset of landmark features for model construction when there are many features and a comparatively few samples. The far-reaching development technologies such as biological sequencing at single cell level make feature selection a more challenging work. The difficulty lies in four facts: those features measured are in high dimension and with noise; dropouts make the data much sparse; many features are either redundant or irrelevant; and samples are not well-labeled in the experiments. Here, we propose a new model called ELF (Extract Landmark Features) to address the above challenges. ELF aims to simultaneously maximize topology maintenance to keep the pairwise relationships among samples, minimize feature redundancy to diversify the features, and maximize feature specificity to make every selected feature more representative. This makes ELF a nonlinear combinatorial optimization. To solve this difficult problem, we propose a heuristic algorithm based on greedy strategy. We show ELF's outstanding performance on two single cell RNA-seq datasets. One is the direct reprogramming from mouse embryonic fibroblasts to induced neuron and the other is hepatoblast differentiation. ELF is able to choose only hundreds of landmark genes to maintain the cells' correlativity. Topology maintenance, redundancy removal, and specificity each plays its important role in selecting landmark features and revealing cells' biological functions. In addition, ELF can be generally applied in other scenarios. We demonstrate that ELF can reveal pivotal pixel in writing region and human face in two public image datasets. We believe that ELF is a useful tool to obtain more interpretable results by revealing key features while clustering the samples well.
资助项目Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai, China ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB13000000] ; National Natural Science Foundation of China[91730301] ; National Natural Science Foundation of China[61671444] ; National Natural Science Foundation of China[61621003] ; National Natural Science Foundation of China[11661141019]
WOS研究方向Biochemistry & Molecular Biology ; Computer Science ; Mathematics
语种英语
出版者IEEE COMPUTER SOC
WOS记录号WOS:000524236800005
源URL[http://ir.amss.ac.cn/handle/2S8OKBNM/51100]  
专题应用数学研究所
通讯作者Wang, Yong
作者单位1.Chinese Acad Sci, Ctr Excellence Anim Evolut & Genet, Kunming 650223, Yunnan, Peoples R China
2.Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Acad Math & Syst Sci, MDIS, CEMS,NCMIS, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714
Feng, Zhan-Ying,Wang, Yong. ELF: Extract Landmark Features By Optimizing Topology Maintenance, Redundancy, and Specificity[J]. IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS,2020,17(2):411-421.
APA Feng, Zhan-Ying,&Wang, Yong.(2020).ELF: Extract Landmark Features By Optimizing Topology Maintenance, Redundancy, and Specificity.IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS,17(2),411-421.
MLA Feng, Zhan-Ying,et al."ELF: Extract Landmark Features By Optimizing Topology Maintenance, Redundancy, and Specificity".IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 17.2(2020):411-421.

入库方式: OAI收割

来源:数学与系统科学研究院

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