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Jung, Inkyung,Matsuyama, Akihisa,Yoshida, Minoru,Kim, Dongsup BioMed Central 2010 BMC bioinformatics Vol.11 No.suppl1
<P><B>Background</B></P><P>Post-translational modifications (PTMs) have a key role in regulating cell functions. Consequently, identification of PTM sites has a significant impact on understanding protein function and revealing cellular signal transductions. Especially, phosphorylation is a ubiquitous process with a large portion of proteins undergoing this modification. Experimental methods to identify phosphorylation sites are labor-intensive and of high-cost. With the exponentially growing protein sequence data, development of computational approaches to predict phosphorylation sites is highly desirable.</P><P><B>Results</B></P><P>Here, we present a simple and effective method to recognize phosphorylation sites by combining sequence patterns and evolutionary information and by applying a novel noise-reducing algorithm. We suggested that considering long-range region surrounding a phosphorylation site is important for recognizing phosphorylation peptides. Also, from compared results to AutoMotif in 36 different kinase families, new method outperforms AutoMotif. The mean accuracy, precision, and recall of our method are 0.93, 0.67, and 0.40, respectively, whereas those of AutoMotif with a polynomial kernel are 0.91, 0.47, and 0.17, respectively. Also our method shows better or comparable performance in four main kinase groups, CDK, CK2, PKA, and PKC compared to six existing predictors.</P><P><B>Conclusion</B></P><P>Our method is remarkable in that it is powerful and intuitive approach without need of a sophisticated training algorithm. Moreover, our method is generally applicable to other types of PTMs.</P>
Jung, Inkyung,Kim, Jung-hyun,Ma, Yuanyuan,Seo, Chanran The Korean Home Economics Association 2015 International Journal of Human Ecology Vol.16 No.2
The current study investigated how academic stress, academic burnout, and academic self-efficacy relate to each other; in addition, this study examined the mediating effects of academic self-efficacy on the relationship between academic stress and academic burnout of Chinese adolescents. A total of 412 students attending third-grade from two different middle schools (ninth-grade in the United States) located in Jiading District of Shanghai participated in the final analysis. By using structural equation modeling (SEM) and the maximum likelihood estimation procedures of AMOS 20.0, the latent variable measurement models were confirmed. The results and conclusions of this study are summarized as follows. A positive correlation between academic stress and academic burnout was soundly supported by this study. Meanwhile, both academic stress and academic burnout indicated negative correlations with academic self-efficacy. The modeling indicated that academic self-efficacy has a partial mediating process and a direct effect on the relationship between academic stress and academic burnout. Thus, academic stress and academic burnout were significantly weaker when academic self-efficacy was higher. In the field of education and curriculum, these results are applicable for restructuring or developing Chinese middle school curriculum utilizing useful methods for adolescents to develop their academic self-efficacy.
SIMPRO: simple protein homology detection method by using indirect signals.
Jung, Inkyung,Kim, Dongsup Oxford University Press 2009 Bioinformatics Vol.25 No.6
<P>Detecting homologous proteins is one of the fundamental problems in computational biology. Many tools to solve this problem have been developed, but development of a simple, effective and generally applicable method is still desirable.</P>
Jung, Inkyung,Lee, Jaehyung,Lee, Soo-Young,Kim, Dongsup BioMed Central 2008 BMC bioinformatics Vol.9 No.-
<P><B>Background</B></P><P>Nonnegative matrix factorization (NMF) is a feature extraction method that has the property of intuitive part-based representation of the original features. This unique ability makes NMF a potentially promising method for biological sequence analysis. Here, we apply NMF to fold recognition and remote homolog detection problems. Recent studies have shown that combining support vector machines (SVM) with profile-profile alignments improves performance of fold recognition and remote homolog detection remarkably. However, it is not clear which parts of sequences are essential for the performance improvement.</P><P><B>Results</B></P><P>The performance of fold recognition and remote homolog detection using NMF features is compared to that of the unmodified profile-profile alignment (PPA) features by estimating Receiver Operating Characteristic (ROC) scores. The overall performance is noticeably improved. For fold recognition at the fold level, SVM with NMF features recognize 30% of homolog proteins at > 0.99 ROC scores, while original PPA feature, HHsearch, and PSI-BLAST recognize almost none. For detecting remote homologs that are related at the superfamily level, NMF features also achieve higher performance than the original PPA features. At > 0.90 ROC<SUB>50 </SUB>scores, 25% of proteins with NMF features correctly detects remotely related proteins, whereas using original PPA features only 1% of proteins detect remote homologs. In addition, we investigate the effect of number of positive training examples and the number of basis vectors on performance improvement. We also analyze the ability of NMF to extract essential features by comparing NMF basis vectors with functionally important sites and structurally conserved regions of proteins. The results show that NMF basis vectors have significant overlap with functional sites from PROSITE and with structurally conserved regions from the multiple structural alignments generated by MUSTANG. The correlation between NMF basis vectors and biologically essential parts of proteins supports our conjecture that NMF basis vectors can explicitly represent important sites of proteins.</P><P><B>Conclusion</B></P><P>The present work demonstrates that applying NMF to profile-profile alignments can reveal essential features of proteins and that these features significantly improve the performance of fold recognition and remote homolog detection.</P>
Park, Jung Hun,Jang, Hyowon,Jung, Yun Kyung,Jung, Ye Lim,Shin, Inkyung,Cho, Dae-Yeon,Park, Hyun Gyu Elsevier 2017 Biosensors & Bioelectronics Vol.91 No.-
<P><B>Abstract</B></P> <P>We herein describe a new mass spectrometry-based method for multiplex SNP genotyping by utilizing allele-specific ligation and strand displacement amplification (SDA) reaction. In this method, allele-specific ligation is first performed to discriminate base sequence variations at the SNP site within the PCR-amplified target DNA. The primary ligation probe is extended by a universal primer annealing site while the secondary ligation probe has base sequences as an overhang with a nicking enzyme recognition site and complementary mass marker sequence. The ligation probe pairs are ligated by DNA ligase only at specific allele in the target DNA and the resulting ligated product serves as a template to promote the SDA reaction using a universal primer. This process isothermally amplifies short DNA fragments, called mass markers, to be analyzed by mass spectrometry. By varying the sizes of the mass markers, we successfully demonstrated the multiplex SNP genotyping capability of this method by reliably identifying several BRCA mutations in a multiplex manner with mass spectrometry.</P> <P><B>Highlights</B></P> <P> <UL> <LI> A new mass spectrometry-based method for multiplex SNP genotyping was developed in label-free manner. </LI> <LI> Multiple SNP sites were simultaneously genotyped based on the peak positions by designing mass markers with different sizes. </LI> <LI> The interpretation of the mass spectrum is simple to analyze the multiple polymorphic sites. </LI> <LI> Several BRCA mutations was genotyped in a multiplex manner for the real clinical patient samples. </LI> </UL> </P>
( Yun Ji Jung ),( Seok-jae Heo ),( Hayan Kwon ),( Hyun Soo Park ),( Soo-young Oh ),( Ji-hee Sung ),( Hyun-joo Seol ),( Hyun Mi Kim ),( Won Joon Seong ),( Han Sung Hwang ),( Inkyung Jung ),( Ja-young K 대한산부인과학회 2022 대한산부인과학회 학술대회 Vol.108 No.-
Objective: The aim of this study was to propose a prediction model based on machine learning algorithms for spontaneous preterm delivery (sPTD) prediction in singleton pregnancies using the cervical elastographic markers obtained at midtrimester. Methods: Multicenter prospective study was performed between June 2018 to December 2020 by the Korean Research Group of Cervical Elastography (grant ID: HI18C1696). Women with singleton pregnancies who underwent ultrasound between 18+0 and 24+0 weeks gestation were eligible for analysis. Cervical elastography were performed using E-cervixTM (WS80A and HERA, Samsung Medison). sPTD was defined as delivery <37 weeks gestation due to either spontaneous preterm labor or rupture of membranes. The final dataset included data from 1,415 patients and this dataset was split into training and validation datasets. Three machine learning classification algorithms (Lasso regression, random forest and XGBoost) were used to build sPTD prediction models. Model performance in predicting sPTD from elastographic data and clinical factors was assessed using validation dataset. Results: Among 1,415 patients, 75 (5.3%) had sPTD. The area under the receiver operating characteristic curve (AUC) results were as follows: for Lasso regression- 0.742, for the random forest- 0.769 and for the XGboost 0.776. In subgroup analysis, the XGBoost model had a better predictive performance in both nulliparity and multiparity group (AUC 0.736 and 0.741, respectively). Results of variable importance for XGBoost showed that indication for prior preterm birth, maternal weight, body mass index, cervix length at examination, hardness ratio (HR) 10 and 30, and elasticity contrast index (ECI) were significant contributors of predicting sPTD. Conclusion: The prediction model using midtrimester cervical elastographic parameters had a potential value in predicting spontaneous preterm birth in single pregnancies.