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Nonlinear multiclass discriminant analysis

Junshui Ma, J.L. Sancho-Gomez, S.C. Ahalt
2003 IEEE Signal Processing Letters  
An alternative nonlinear multiclass discriminant algorithm is presented. This algorithm is based on the use of kernel functions and is designed to optimize a general linear discriminant analysis criterion based on scatter matrices. By reformulating these matrices in a specific form, a straightforward derivation allows the kernel function to be introduced in a simple and direct way. Moreover, we propose a method to determine the value of the regularization parameter , based on this derivation.
more » ... dex Terms-Discriminant analysis, feature extraction, kernel method.
doi:10.1109/lsp.2003.813680 fatcat:4kx7go2ysfdkroti2nhylcf2au

Accurate On-line Support Vector Regression

Junshui Ma, James Theiler, Simon Perkins
2003 Neural Computation  
Batch implementations of support vector regression (SVR) are inef cient when used in an on-line setting because they must be retrained from scratch every time the training set is modi ed. Following an incremental support vector classi cation algorithm introduced by Cauwenberghs and Poggio (2001), we have developed an accurate on-line support vector regression (AOSVR) that ef ciently updates a trained SVR function whenever a sample is added to or removed from the training set. The updated SVR
more » ... ction is identical to that produced by a batch algorithm. Applications of AOSVR in both on-line and cross-validation scenarios are presented. In both scenarios, numerical experiments indicate that AOSVR is faster than batch SVR algorithms with both cold and warm start.
doi:10.1162/089976603322385117 pmid:14577858 fatcat:sasuqdprc5hbdg3xyi7ma4dnza

Two realizations of a general feature extraction framework

Junshui Ma, James Theiler, Simon Perkins
2004 Pattern Recognition  
Some of the ideas were ÿrst shaped when Junshui Ma was under the supervision of Professor Stanley C. Ahalt in the Department of Electrical Engineering, Ohio State University.  ... 
doi:10.1016/j.patcog.2003.10.010 fatcat:wuyxcst3vfezdnifmsraze3kpm

Online novelty detection on temporal sequences

Junshui Ma, Simon Perkins
2003 Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '03  
Novelty detection, or anomaly detection, on temporal sequences has increasingly attracted attention from researchers in different areas. In this paper, we present a new framework for online novelty detection on temporal sequences. This framework includes a mechanism for associating each detection result with a confidence value. Based on this framework, we develop a concrete online detection algorithm, by modeling the temporal sequence using an online support vector regression algorithm.
more » ... nts on both synthetic and real world data are performed to demonstrate the promising performance of our proposed detection algorithm. The main contributions of this paper are (a) It proposes an online novelty detection framework for temporal sequences. This online framework is capable of associating a confidence level with each detection result. (b) It proposes a concrete online novelty detection algorithm based on the framework, and describes experiments to test the new algorithm.
doi:10.1145/956750.956828 dblp:conf/kdd/MaP03 fatcat:dos7xoiwmrfkfh7sh5syzxr7iq

A Nonparametric Method for Value Function Guided Subgroup Identification via Gradient Tree Boosting for Censored Survival Data [article]

Pingye Zhang, Junshui Ma, Xinqun Chen, Yue Shentu
2020 arXiv   pre-print
In randomized clinical trials with survival outcome, there has been an increasing interest in subgroup identification based on baseline genomic, proteomic markers or clinical characteristics. Some of the existing methods identify subgroups that benefit substantially from the experimental treatment by directly modeling outcomes or treatment effect. When the goal is to find an optimal treatment for a given patient rather than finding the right patient for a given treatment, methods under the
more » ... idualized treatment regime framework estimate an individualized treatment rule that would lead to the best expected clinical outcome as measured by a value function. Connecting the concept of value function to subgroup identification, we propose a nonparametric method that searches for subgroup membership scores by maximizing a value function that directly reflects the subgroup-treatment interaction effect based on restricted mean survival time. A gradient tree boosting algorithm is proposed to search for the individual subgroup membership scores. We conduct simulation studies to evaluate the performance of the proposed method and an application to an AIDS clinical trial is performed for illustration.
arXiv:2006.08807v1 fatcat:os24uox4vbgqva3mw4tv3afpee

Selection Induced Contrast Estimate (SICE) Effect: An Attempt to Quantify the Impact of Some Patient Selection Criteria in Randomized Clinical Trials [article]

Junshui Ma, Daniel J. Holder
2020 arXiv   pre-print
Defining the Inclusion/Exclusion (I/E) criteria of a trial is one of the most important steps during a trial design. Increasingly complex I/E criteria potentially create information imbalance and transparency issues between the people who design and run the trials and those who consume the information produced by the trials. In order to better understand and quantify the impact of a category of I/E criteria on observed treatment effects, a concept, named the Selection Induced Contrast Estimate
more » ... SICE) effect, is introduced and formulated in this paper. The SICE effect can exist in controlled clinical trials when treatment affects the correlation between a marker used for selection and the response of interest. This effect is demonstrated with both simulations and real clinical trial data. Although the statistical elements behind the SICE effect have been well studied, explicitly formulating and studying this effect can benefit several areas, including better transparency in I/E criteria, meta-analysis of multiple clinical trials, treatment effect interpretation in real-world medical practice, etc.
arXiv:2001.02036v1 fatcat:ma5pubbxqjd3bmfgzis7joniye

Electroencephalographic Power Spectral Density Profile of the Orexin Receptor Antagonist Suvorexant in Patients with Primary Insomnia and Healthy Subjects

Junshui Ma, Vladimir Svetnik, Ellen Snyder, Christopher Lines, Thomas Roth, W. Joseph Herring
2014 Sleep  
1609 EEG Power Spectral Density Profile of Suvorexant-Ma et al.  ...  Ma  ... 
doi:10.5665/sleep.4068 pmid:25197807 pmcid:PMC4173918 fatcat:k2ae4wuy3zh6hmczoo3rgpoobu

A deep learning-facilitated radiomics solution for the prediction of lung lesion shrinkage in non-small cell lung cancer trials [article]

Antong Chen, Jennifer Saouaf, Bo Zhou, Randolph Crawford, Jianda Yuan, Junshui Ma, Richard Baumgartner, Shubing Wang, Gregory Goldmacher
2020 arXiv   pre-print
Herein we propose a deep learning-based approach for the prediction of lung lesion response based on radiomic features extracted from clinical CT scans of patients in non-small cell lung cancer trials. The approach starts with the classification of lung lesions from the set of primary and metastatic lesions at various anatomic locations. Focusing on the lung lesions, we perform automatic segmentation to extract their 3D volumes. Radiomic features are then extracted from the lesion on the
more » ... atment scan and the first follow-up scan to predict which lesions will shrink at least 30% in diameter during treatment (either Pembrolizumab or combinations of chemotherapy and Pembrolizumab), which is defined as a partial response by the Response Evaluation Criteria In Solid Tumors (RECIST) guidelines. A 5-fold cross validation on the training set led to an AUC of 0.84 +/- 0.03, and the prediction on the testing dataset reached AUC of 0.73 +/- 0.02 for the outcome of 30% diameter shrinkage.
arXiv:2003.02943v1 fatcat:z2gxpavqonednoctdnw7vv2tyu

Alterations in Cyclic Alternating Pattern Associated with Phase Advanced Sleep are Differentially Modulated by Gaboxadol and Zolpidem

Vladimir Svetnik, Raffaele Ferri, Shubhankar Ray, Junshui Ma, James K. Walsh, Ellen Snyder, Bjarke Ebert, Steve Deacon
2010 Sleep  
Ma, PhD 1 ; James K.  ...  Svetnik, Ray, Ma, and Snyder are employees of Merck. Dr.  ... 
doi:10.1093/sleep/33.11.1562 pmid:21102998 pmcid:PMC2954706 fatcat:iumkv4ou7jar3gdm2y44e3atly

Evaluation of Automated and Semi-Automated Scoring of Polysomnographic Recordings from a Clinical Trial Using Zolpidem in the Treatment of Insomnia

Vladimir Svetnik, Junshui Ma, Keith A. Soper, Scott Doran, John J. Renger, Steve Deacon, Ken S. Koblan
2007 Sleep  
INSOMNIA Objective: To evaluate the performance of 2 automated systems, Morpheus and Somnolyzer24X7, with various levels of human review/editing, in scoring polysomnographic (PSG) recordings from a clinical trial using zolpidem in a model of transient insomnia. Methods: 164 all-night PSG recordings from 82 subjects collected during 2 nights of sleep, one under placebo and one under zolpidem (10 mg) treatment were used. For each recording, 6 different methods were used to provide sleep stage
more » ... es based on Rechtschaffen & Kales criteria: 1) full manual scoring, 2) automated scoring by Morpheus 3) automated scoring by Somnolyzer24X7, 4) automated scoring by Morpheus with full manual review, 5) automated scoring by Morpheus with partial manual review, 6) automated scoring by Somnolyzer24X7 with partial manual review. Ten traditional clinical efficacy measures of sleep initiation, maintenance, and architecture were calculated. Results: Pair-wise epoch-by-epoch agreements between fully automated and manual scores were in the range of intersite manual scoring agreements reported in the literature (70%-72%). Pair-wise epoch-by-epoch agreements between automated scores manually reviewed were higher (73%-76%). The direction and statistical significance of treatment effect sizes using traditional efficacy endpoints were essentially the same whichever method was used. As the degree of manual review increased, the magnitude of the effect size approached those estimated with fully manual scoring. Conclusion: Automated or semi-automated sleep PSG scoring offers valuable alternatives to costly, time consuming, and intrasite and intersite variable manual scoring, especially in large multicenter clinical trials. Reduction in scoring variability may also reduce the sample size of a clinical trial.
doi:10.1093/sleep/30.11.1562 pmid:18041489 pmcid:PMC2082094 fatcat:fg6bm37rubclzlkp44nngbtrnu

EEG Power Spectra Response to a 4-h Phase Advance and Gaboxadol Treatment in 822 Men and Women

Junshui Ma, Derk-Jan Dijk, Vladimir Svetnik, Yevgen Tymofyeyev, Shubhankar Ray, James K. Walsh, Steve Deacon
2011 Journal of Clinical Sleep Medicine (JCSM)  
Citation: Ma J; Dijk DJ; Svetnik V; Tymofyeyev Y; Ray S; Walsh JK; Deacon S. EEG power spectra response to a 4-h phase advance and gaboxadol treatment in 822 men and women.  ...  ) 85.3 (11.3)/86.0 (8.9) b 85.2 (8.8)/86.5 (8.1) MSLT (min) 16.7 (4.4) 16.5 (4.0) 16.2 (3.3) Men/women MSLT (min) 16.8 (4.6)/16.7 (4.3) 16.5 (3.5)/16.5 (4.3) 16.1 (3.1)/16.3 (3.4) J Ma  ... 
doi:10.5664/jcsm.1316 pmid:22003345 pmcid:PMC3190849 fatcat:i4h6vd6m2rb3vdsqatop2bjrua

Genetic Algorithms and Support Vector Machines for Time Series Classification

Damian R. Eads, Daniel Hill, Sean Davis, Simon J. Perkins, Junshui Ma, Reid B. Porter, James P. Theiler, Bruno Bosacchi, David B. Fogel, James C. Bezdek
2002 Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation V  
We introduce an algorithm for classifying time series data. Since our initial application is for lightning data, we call the algorithm Zeus. Zeus is a hybrid algorithm that employs evolutionary computation for feature extraction, and a support vector machine for the final "backend" classification. Support vector machines have a reputation for classifying in high-dimensional spaces without overfitting, so the utility of reducing dimensionality with an intermediate feature selection step has been
more » ... questioned. We address this question by testing Zeus on a lightning classification task using data acquired from the Fast On-orbit Recording of Transient Events (FORTE) satellite.
doi:10.1117/12.453526 fatcat:lcgj4ufpz5bsnb2kzunjwk4g7m

Automated Sleep Scoring with Human Supervision Adds Value Compared with Human Scoring Alone: A reply to Zammit G. K. Insufficient evidence for the use of automated and semi-automated scoring of polysomnographic recordings. SLEEP 2008:31;449–50

Vladimir Svetnik, Junshui Ma, Keith A. Soper, Scott Doran, John J. Renger, Steve Deacon, Ken S. Koblan
2008 Sleep  
doi:10.1093/sleep/31.4.451 pmcid:PMC2279751 fatcat:2tze6uglvbfjrhgkw4wsfqpydy

Improved prediction of prostate cancer recurrence based on an automated tissue image analysis system

M. Teverovskiy, V. Kumar, Junshui Ma, A. Kotsianti, D. Verbel, A. Tabesh, Ho-Yuen Pang, Y. Vengrenyuk, S. Fogarasi, O. Saidi
2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano (IEEE Cat No. 04EX821)  
Prostate tissue characteristics play an important role in predicting the recurrence of prostate cancer. Currently, experienced pathologists manually grade these prostate tissues using the Gleason scoring system, a subjective approach which summarizes the overall progression and aggressiveness of the cancer. Using advanced image processing techniques, Aureon Biosciences Corporation has developed a proprietary image analysis system (MAGIC TM ), which here is specifically applied to prostate
more » ... analysis and designed to be capable of processing a single prostate tissue Hematoxylin-and-Eosin (H&E) stained image and automatically extracting a variety of raw measurements (spectral, shape, etc.) of histopathological objects along with spatial relationships amongst them. In the context of predicting prostate cancer recurrence, the performance of the image features is comparable to that achieved using the Gleason scoring system. Moreover, an improved prediction rate is observed by combining the Gleason scores with the image features obtained using MAGIC™, suggesting that the image data itself may possess information complementary to that of Gleason scores.
doi:10.1109/isbi.2004.1398523 fatcat:fgzlel2dr5ckbl534w4i2v7zjq

An integrated approach utilizing proteomics and bioinformatics to detect ovarian cancer

Jie-kai Yu, Shu Zheng, Yong Tang, Li Li
2005 JZUS-A - Journal of Zhejiang University. Science  
SVM classifier is based on the shareware program OSU_SVM v.3.00 Toolbox of Junshui Ma and Yi Zhao.  ... 
doi:10.1631/jzus.2005.b0227 pmid:15754417 pmcid:PMC1389728 fatcat:xbzhufac7raqnftg7rvuobnppm
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