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SNP Selection in Genome-Wide Association Studies via Penalized Support Vector Machine with MAX Test
Kim, Jinseog,Sohn, Insuk,Kim, Dennis (Dong Hwan),Jung, Sin-Ho Hindawi Publishing Corporation 2013 Computational and mathematical methods in medicine Vol.2013 No.-
<P>One of main objectives of a genome-wide association study (GWAS) is to develop a prediction model for a binary clinical outcome using single-nucleotide polymorphisms (SNPs) which can be used for diagnostic and prognostic purposes and for better understanding of the relationship between the disease and SNPs. Penalized support vector machine (SVM) methods have been widely used toward this end. However, since investigators often ignore the genetic models of SNPs, a final model results in a loss of efficiency in prediction of the clinical outcome. In order to overcome this problem, we propose a two-stage method such that the the genetic models of each SNP are identified using the MAX test and then a prediction model is fitted using a penalized SVM method. We apply the proposed method to various penalized SVMs and compare the performance of SVMs using various penalty functions. The results from simulations and real GWAS data analysis show that the proposed method performs better than the prediction methods ignoring the genetic models in terms of prediction power and selectivity.</P>
Robust second-order rotatable designs invariably applicable for some lifetime distributions
Kim, Jinseog,Das, Rabindra Nath,Singh, Poonam,Lee, Youngjo The Korean Statistical Society 2021 Communications for statistical applications and me Vol.28 No.6
Recently a few articles have derived robust first-order rotatable and D-optimal designs for the lifetime response having distributions gamma, lognormal, Weibull, exponential assuming errors that are correlated with different correlation structures such as autocorrelated, intra-class, inter-class, tri-diagonal, compound symmetry. Practically, a first-order model is an adequate approximation to the true surface in a small region of the explanatory variables. A second-order model is always appropriate for an unknown region, or if there is any curvature in the system. The current article aims to extend the ideas of these articles for second-order models. Invariant (free of the above four distributions) robust (free of correlation parameter values) second-order rotatable designs have been derived for the intra-class and inter-class correlated error structures. Second-order rotatability conditions have been derived herein assuming the response follows non-normal distribution (any one of the above four distributions) and errors have a general correlated error structure. These conditions are further simplified under intra-class and inter-class correlated error structures, and second-order rotatable designs are developed under these two structures for the response having anyone of the above four distributions. It is derived herein that robust second-order rotatable designs depend on the respective error variance covariance structure but they are independent of the correlation parameter values, as well as the considered four response lifetime distributions.
Towards perfect text classification with Wikipedia-based semantic Naïve Bayes learning
Kim, Han-joon,Kim, Jiyun,Kim, Jinseog,Lim, Pureum Elsevier 2018 Neurocomputing Vol.315 No.-
<P>This paper suggests a novel way of dramatically improving the Naive Bayes text classifier with our semantic tensor space model for document representation. In our work, we intend to achieve a perfect text classification with the semantic Naive Bayes learning that incorporates the semantic concept features into term feature statistics; for this, the Naive Bayes learning is semantically augmented under the tensor space model where the 'concept' space is regarded as an independent space equated with the 'term' and 'document' spaces, and it is produced with concept-level informative Wikipedia pages associated with a given document corpus. Through extensive experiments using three popular document corpora including Reuters-21578, 20Newsgroups, and OHSUMED corpora, we prove that the proposed method not only has superiority over the recent deep learning-based classification methods but also shows nearly perfect classification performance. (c) 2018 Elsevier B.V. All rights reserved.</P>
Gradient lasso for Cox proportional hazards model.
Sohn, Insuk,Kim, Jinseog,Jung, Sin-Ho,Park, Changyi Oxford University Press 2009 Bioinformatics Vol.25 No.14
<P>MOTIVATION: There has been an increasing interest in expressing a survival phenotype (e.g. time to cancer recurrence or death) or its distribution in terms of a subset of the expression data of a subset of genes. Due to high dimensionality of gene expression data, however, there is a serious problem of collinearity in fitting a prediction model, e.g. Cox's proportional hazards model. To avoid the collinearity problem, several methods based on penalized Cox proportional hazards models have been proposed. However, those methods suffer from severe computational problems, such as slow or even failed convergence, because of high-dimensional matrix inversions required for model fitting. We propose to implement the penalized Cox regression with a lasso penalty via the gradient lasso algorithm that yields faster convergence to the global optimum than do other algorithms. Moreover the gradient lasso algorithm is guaranteed to converge to the optimum under mild regularity conditions. Hence, our gradient lasso algorithm can be a useful tool in developing a prediction model based on high-dimensional covariates including gene expression data. RESULTS: Results from simulation studies showed that the prediction model by gradient lasso recovers the prognostic genes. Also results from diffuse large B-cell lymphoma datasets and Norway/Stanford breast cancer dataset indicate that our method is very competitive compared with popular existing methods by Park and Hastie and Goeman in its computational time, prediction and selectivity. AVAILABILITY: R package glcoxph is available at http://datamining.dongguk.ac.kr/R/glcoxph.</P>
( Hayne Cho Park ),( Do Hyoung Kim ),( Ajin Cho ),( Young Eun Kwon ),( Dong-ryeol Ryu ),( Jinseog Kim ),( Ki Hwa Yang ),( Ji Hyeon Shin ),( Eun Jung Son ),( Young-ki Lee ) 대한신장학회 2023 Kidney Research and Clinical Practice Vol.42 No.3
Background: It is important for the dialysis specialist to provide essential and safe care to hemodialysis (HD) patients. However, little is known about the actual effect of dialysis specialist care on the survival of HD patients. We therefore investigated the influence of dialysis specialist care on patient mortality in a nationwide Korean dialysis cohort. Methods: We used an HD quality assessment and National Health Insurance Service claims data from October to December 2015. A total of 34,408 patients were divided into two groups according to the proportion of dialysis specialists in their HD unit, as follows: 0%, no dialysis specialist care group, and ≥50%, dialysis specialist care group. We analyzed the mortality risk of these groups using the Cox proportional hazards model after matching propensity scores. Results: After propensity score matching, 18,344 patients were enrolled. The ratio of patients from the groups with and without dialysis specialist care was 86.7% to 13.3%. The dialysis specialist care group showed a shorter dialysis vintage, higher levels of hemoglobin, higher single-pool Kt/V values, lower levels of phosphorus, and lower systolic and diastolic blood pressures than the no dialysis specialist care group. After adjusting demographic and clinical parameters, the absence of dialysis specialist care was a significant independent risk factor for all-cause mortality (hazard ratio, 1.10; 95% confidence interval, 1.03-1.18; p = 0.004). Conclusion: Dialysis specialist care is an important determinant of overall patient survival among HD patients. Appropriate care given by dialysis specialists may improve clinical outcomes of patients undergoing HD.
임재걸 ( Jaegeol Yim ),도재수 ( Jaesu Do ),김진석 ( Jinseog Kim ) 한국정보처리학회 2010 한국정보처리학회 학술대회논문집 Vol.17 No.2
다양한 옥내 측위 방법이 연구 발표되었다. 그 중에서 무선근거리통신망을 이용하는 방법은 측위를 위한 별도의 특수 장비를 요구하지 않기 때문에 실용적이다. 무선근거리통신망 기반 옥내 측위에서는 지문방식과 신호세기를 거리로 환산하여 사용하는 방법이 가장 흔히 사용되는 두 가지 방법이다. 지문방식은 시간과 노력이 많이 소요되는 준비단계가 필요하지만 신호세기를 거리로 환산하여 사용하는 방법보다 정확한 반면, 신호세기를 거리로 환산하여 사용하는 방법은 구현하기가 용이하지만 오차가 심하다. 때때로 지문방식 조차도 실제 응용에 적용되기에 부적절할 만큼 오차가 커, [1]은 일종의 지문방식인 K-NN (K nearest neighbors) 방법의 오차를 개선하기 위한 파티클 필터를 소개하였다. 칼만 필터도 역시 K-NN 옥내 측위의 정확도 개선을 위하여 사용된 바 있다. 본 논문은 K-NN 방법의 정확도 개선에 있어 칼만 필터와 파티클 필터의 성능을 비교하는 실험 결과를 소개한다.