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Table representation of search results timeline featuring number of search results per year.

Year Number of Results
1926 1
1947 1
1948 1
1949 2
1950 2
1951 1
1955 1
1956 2
1962 2
1964 2
1965 7
1966 1
1967 1
1968 3
1969 4
1970 4
1971 4
1972 3
1973 10
1974 11
1975 39
1976 56
1977 42
1978 44
1979 50
1980 59
1981 57
1982 94
1983 93
1984 104
1985 119
1986 123
1987 147
1988 127
1989 246
1990 323
1991 379
1992 407
1993 442
1994 432
1995 441
1996 492
1997 483
1998 646
1999 578
2000 678
2001 702
2002 683
2003 706
2004 857
2005 973
2006 1115
2007 1336
2008 1420
2009 1652
2010 1961
2011 2221
2012 2399
2013 2412
2014 2328
2015 2380
2016 2432
2017 2550
2018 2754
2019 2863
2020 3256
2021 3622
2022 3843
2023 3426
2024 1415

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49,410 results

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Page 1
Subspace-based non-blind deconvolution.
Zhuang PX, Ding XH, Duan JM. Zhuang PX, et al. Math Biosci Eng. 2019 Mar 14;16(4):2202-2218. doi: 10.3934/mbe.2019108. Math Biosci Eng. 2019. PMID: 31137207 Free article.
Meanwhile, with exploiting the difference of subspace priors, we put forward differentiation modelings on different subspace priors for improving deblurring performance. Then we utilize the least square integration method to fuse deblurred estimations and to compens …
Meanwhile, with exploiting the difference of subspace priors, we put forward differentiation modelings on different subspace priors for impr …
Data analysis techniques in phosphoproteomics.
Meyer-Baese A, Wildberger J, Meyer-Baese U, Nilsson CL. Meyer-Baese A, et al. Electrophoresis. 2014 Dec;35(24):3452-62. doi: 10.1002/elps.201400219. Epub 2014 Nov 25. Electrophoresis. 2014. PMID: 25311575 Review.
The interpretation of phosphoproteomics data sets is crucial for generating hypotheses that guide therapeutic solutions, yet not many techniques have been applied to this type of analysis. This paper intends to give an overview about the two main standard techniques that c …
The interpretation of phosphoproteomics data sets is crucial for generating hypotheses that guide therapeutic solutions, yet not many techni …
Neighbor-based adaptive sparsity orthogonal least square for fluorescence molecular tomography.
Yi H, Ma S, Yang R, Zhong S, Guo H, He X, He X, Hou Y. Yi H, et al. J Biomed Opt. 2023 Jun;28(6):066005. doi: 10.1117/1.JBO.28.6.066005. Epub 2023 Jun 29. J Biomed Opt. 2023. PMID: 37396685 Free PMC article.
AIM: We propose an algorithm, neighbor-based adaptive sparsity orthogonal least square (NASOLS), to improve the quality of FMT reconstruction. APPROACH: The proposed NASOLS does not require sparsity prior information and is designed to efficiently establish a suppor …
AIM: We propose an algorithm, neighbor-based adaptive sparsity orthogonal least square (NASOLS), to improve the quality of FMT …
Kernel weighted least square approach for imputing missing values of metabolomics data.
Kumar N, Hoque MA, Sugimoto M. Kumar N, et al. Sci Rep. 2021 May 27;11(1):11108. doi: 10.1038/s41598-021-90654-0. Sci Rep. 2021. PMID: 34045614 Free PMC article.
We evaluated the performance of the proposed method and other conventional and recently developed missing imputation techniques using both artificially generated data and experimentally measured data analysis in both the absence and presence of different rates of outliers. …
We evaluated the performance of the proposed method and other conventional and recently developed missing imputation techniques using both a …
Regularized Partial Least Square Regression for Continuous Decoding in Brain-Computer Interfaces.
Foodeh R, Ebadollahi S, Daliri MR. Foodeh R, et al. Neuroinformatics. 2020 Jun;18(3):465-477. doi: 10.1007/s12021-020-09455-x. Neuroinformatics. 2020. PMID: 32107734
A serious issue in this technique is the over-fitting phenomenon. Partial least square (PLS) is a well-known and popular method which tries to overcome this problem. ...In this paper, a regularized version of PLS (RPLS) is proposed which tries to determine a linear …
A serious issue in this technique is the over-fitting phenomenon. Partial least square (PLS) is a well-known and popular metho …
A fast divide-and-conquer sparse Cox regression.
Wang Y, Hong C, Palmer N, Di Q, Schwartz J, Kohane I, Cai T. Wang Y, et al. Biostatistics. 2021 Apr 10;22(2):381-401. doi: 10.1093/biostatistics/kxz036. Biostatistics. 2021. PMID: 31545341 Free PMC article.
The proposed algorithm achieves computational efficiency through a one-step linear approximation followed by a least square approximation to the partial likelihood (PL). These sequences of linearization enable us to maximize the PL with only a small subset and perfo …
The proposed algorithm achieves computational efficiency through a one-step linear approximation followed by a least square ap …
Partial least squares methods: partial least squares correlation and partial least square regression.
Abdi H, Williams LJ. Abdi H, et al. Methods Mol Biol. 2013;930:549-79. doi: 10.1007/978-1-62703-059-5_23. Methods Mol Biol. 2013. PMID: 23086857
Partial least square (PLS) methods (also sometimes called projection to latent structures) relate the information present in two data tables that collect measurements on the same set of observations. ...When the goal is to find the shared information between two tab …
Partial least square (PLS) methods (also sometimes called projection to latent structures) relate the information present in t …
Penalized partial least squares for pleiotropy.
Broc C, Truong T, Liquet B. Broc C, et al. BMC Bioinformatics. 2021 Feb 24;22(1):86. doi: 10.1186/s12859-021-03968-1. BMC Bioinformatics. 2021. PMID: 33627076 Free PMC article.
The method is based on a generalization of the sparse group Partial Least Squares (sgPLS) to take into account groups of variables, and a Lasso penalization that links all independent data sets. ...
The method is based on a generalization of the sparse group Partial Least Squares (sgPLS) to take into account groups of varia …
Inter-class sparsity based discriminative least square regression.
Wen J, Xu Y, Li Z, Ma Z, Xu Y. Wen J, et al. Neural Netw. 2018 Jun;102:36-47. doi: 10.1016/j.neunet.2018.02.002. Epub 2018 Feb 21. Neural Netw. 2018. PMID: 29524766
Least square regression is a very popular supervised classification method. ...To solve these problems and improve the performance, this paper presents a novel method, i.e., inter-class sparsity based discriminative least square regression (ICS_DLSR), …
Least square regression is a very popular supervised classification method. ...To solve these problems and improve the perform …
Modeling the Ranked Antenatal Care Visits Using Optimized Partial Least Square Regression.
Sadiq M, Abdulrahman AT, Alharbi R, Alnagar DKF, Anwar SM. Sadiq M, et al. Comput Math Methods Med. 2022 Mar 14;2022:2868885. doi: 10.1155/2022/2868885. eCollection 2022. Comput Math Methods Med. 2022. PMID: 35321203 Free PMC article.
The present research is conducted to determine the risk factors of reduced antenatal care visits using an optimized partial least square regression model. A data set collected during 2017-2018 by Pakistan Demographic and Health Surveys is used for modeling purposes. …
The present research is conducted to determine the risk factors of reduced antenatal care visits using an optimized partial least …
49,410 results
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