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Journal articles on the topic 'Data Subgroup'

Author: Grafiati

Published: 14 March 2023

Last updated: 31 July 2025

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1

Ma, Jianglei, Huijie Zhang, Yuanyuan Zhang, and Guangming Wang. "Construction of gene subgroups of Crohn disease based on transcriptome data." Medicine 102, no. 31 (2023): e34482. http://dx.doi.org/10.1097/md.0000000000034482.

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Background: The global prevalence of Crohn disease (CD), a chronic inflammatory disease of the intestine, has been increasing; however, the etiology and pathogenesis of this disease have not been fully elucidated. Therefore, in the present study, we aimed to better understand the molecular mechanisms underlying CD to aid the development of novel therapeutic strategies for this condition. Methods: Based on the transcriptome data from patients with CD, this study used an unsupervised learning method to construct gene co-expression molecular subgroups and the R and SPSS software to identify the b

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Kavšek, Branko, and Nada Lavrač. "Using subgroup discovery to analyze the UK traffic data." Advances in Methodology and Statistics 1, no. 1 (2004): 249–64. http://dx.doi.org/10.51936/zewh2294.

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Rule learning is typically used in solving classification and prediction tasks. However, learning of classification rules can be adapted also to subgroup discovery. Such an adaptation has already been done for the CN2 rule learning algorithm. In previous work this new algorithm, called CN2-SD, has been described in detail and applied to the well known UCI data sets. This paper summarizes the modifications needed for the adaptation of the CN2 rule learner to subgroup discovery and presents its application to a real-life data set - the UK traffic data - confirming its appropriateness for subgrou

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Huang, Xifen, Chaosong Xiong, Jinfeng Xu, Jianhua Shi, and Jinhong Huang. "Mixture Modeling of Time-to-Event Data in the Proportional Odds Model." Mathematics 10, no. 18 (2022): 3375. http://dx.doi.org/10.3390/math10183375.

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Subgroup analysis with survival data are most essential for detailed assessment of the risks of medical products in heterogeneous population subgroups. In this paper, we developed a semiparametric mixture modeling strategy in the proportional odds model for simultaneous subgroup identification and regression analysis of survival data that flexibly allows the covariate effects to differ among several subgroups. Neither the membership or the subgroup-specific covariate effects are known a priori. The nonparametric maximum likelihood method together with a pair of MM algorithms with monotone asce

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Mukherjee, Shubhabrata, Jesse Mez, Emily H. Trittschuh, et al. "Genetic data and cognitively defined late-onset Alzheimer’s disease subgroups." Molecular Psychiatry 25, no. 11 (2018): 2942–51. http://dx.doi.org/10.1038/s41380-018-0298-8.

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Abstract Categorizing people with late-onset Alzheimer’s disease into biologically coherent subgroups is important for personalized medicine. We evaluated data from five studies (total n = 4050, of whom 2431 had genome-wide single-nucleotide polymorphism (SNP) data). We assigned people to cognitively defined subgroups on the basis of relative performance in memory, executive functioning, visuospatial functioning, and language at the time of Alzheimer’s disease diagnosis. We compared genotype frequencies for each subgroup to those from cognitively normal elderly controls. We focused on APOE and

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Sidorenkov, Andrey. "Informal subgroups in work groups: size and relations in subgroups." Bulletin of the Donetsk National University. Series D: Philology and Psychology 1 (February 27, 2025): 146–58. https://doi.org/10.5281/zenodo.14934698.

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The work examines the connection between the size of informal subgroups that arise in production groups and some relationship phenomena (interpersonal and subgroup trust, interpersonal and subgroup identity, interpersonal and subgroup conflict) within these subgroups. It also compares the convexity of the corresponding relationship phenomena in informal dyads and larger subgroups. The data from two empirical studies on different samples of work groups (N=37 and N=42) were used. In these groups, 65 and 71 informal subgroups, respectively, were found. The size of informal subgroups is significan

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Lötsch, Jörn, and Alfred Ultsch. "Current Projection Methods-Induced Biases at Subgroup Detection for Machine-Learning Based Data-Analysis of Biomedical Data." International Journal of Molecular Sciences 21, no. 1 (2019): 79. http://dx.doi.org/10.3390/ijms21010079.

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Advances in flow cytometry enable the acquisition of large and high-dimensional data sets per patient. Novel computational techniques allow the visualization of structures in these data and, finally, the identification of relevant subgroups. Correct data visualizations and projections from the high-dimensional space to the visualization plane require the correct representation of the structures in the data. This work shows that frequently used techniques are unreliable in this respect. One of the most important methods for data projection in this area is the t-distributed stochastic neighbor e

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Atzmueller, Martin, and Frank Puppe. "Semi-Automatic Visual Subgroup Mining using VIKAMINE." JUCS - Journal of Universal Computer Science 11, no. (11) (2005): 1752–65. https://doi.org/10.3217/jucs-011-11-1752.

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Visual mining methods enable the direct integration of the user to overcome major problems of automatic data mining methods, e.g., the presentation of uninteresting results, lack of acceptance of the discovered findings, or limited confidence in these. We present a novel subgroup mining approach for explorative and descriptive data mining implemented in the VIKAMINE system. We propose several integrated visualization methods to support subgroup mining. Furthermore, we describe three case studies using data from fielded systems in the medical domain.

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Khadirnaikar, Seema, Sudhanshu Shukla, and S. R. M. Prasanna. "Integration of pan-cancer multi-omics data for novel mixed subgroup identification using machine learning methods." PLOS ONE 18, no. 10 (2023): e0287176. http://dx.doi.org/10.1371/journal.pone.0287176.

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Cancer is a heterogeneous disease, and patients with tumors from different organs can share similar epigenetic and genetic alterations. Therefore, it is crucial to identify the novel subgroups of patients with similar molecular characteristics. It is possible to propose a better treatment strategy when the heterogeneity of the patient is accounted for during subgroup identification, irrespective of the tissue of origin. This work proposes a machine learning (ML) based pipeline for subgroup identification in pan-cancer. Here, mRNA, miRNA, DNA methylation, and protein expression features from pa

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An, Shengli, Peter Zhang, and Hong-Bin Fang. "Subgroup Identification in Survival Outcome Data Based on Concordance Probability Measurement." Mathematics 11, no. 13 (2023): 2855. http://dx.doi.org/10.3390/math11132855.

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Identifying a subgroup of patients who may have an enhanced treatment effect in a randomized clinical trial has received increasing attention recently. For time-to-event outcomes, it is a challenge to define the effectiveness of a treatment and to choose a cutoff time point for identifying subgroup membership, especially in trials in which the two treatment arms do not differ in overall survival. In this paper, we propose a mixture cure model to identify a subgroup for a new treatment that was compared to a classical treatment (or placebo) in a randomized clinical trial with respect to surviva

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Pan, Yunzhi, Weidan Pu, Xudong Chen, et al. "Morphological Profiling of Schizophrenia: Cluster Analysis of MRI-Based Cortical Thickness Data." Schizophrenia Bulletin 46, no. 3 (2020): 623–32. http://dx.doi.org/10.1093/schbul/sbz112.

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Abstract The diagnosis of schizophrenia is thought to embrace several distinct subgroups. The manifold entities in a single clinical patient group increase the variance of biological measures, deflate the group-level estimates of causal factors, and mask the presence of treatment effects. However, reliable neurobiological boundaries to differentiate these subgroups remain elusive. Since cortical thinning is a well-established feature in schizophrenia, we investigated if individuals (patients and healthy controls) with similar patterns of regional cortical thickness form naturally occurring mor

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Bach, Jakob. "Subgroup Discovery with Small and Alternative Feature Sets." Proceedings of the ACM on Management of Data 3, no. 3 (2025): 1–27. https://doi.org/10.1145/3725358.

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Subgroup-discovery methods find interesting regions in a dataset. In this article, we analyze two constraint types to enhance the interpretability of subgroups: First, we make subgroup descriptions small by limiting the number of features used. Second, we propose the novel problem of finding alternative subgroup descriptions, which cover a similar set of data objects as a given subgroup but use different features. We describe how to integrate both constraint types into heuristic subgroup-discovery methods as well as a novel Satisfiability Modulo Theories (SMT) formulation, which enables a solv

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Emery, William. "WOCE/TOGA Historical Oceanographic Data Subgroup." Eos, Transactions American Geophysical Union 67, no. 22 (1986): 500. http://dx.doi.org/10.1029/eo067i022p00500-03.

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Shirrell, Matthew. "The Effects of Subgroup-Specific Accountability on Teacher Turnover and Attrition." Education Finance and Policy 13, no. 3 (2018): 333–68. http://dx.doi.org/10.1162/edfp_a_00227.

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The No Child Left Behind Act of 2001 required states to set cutoffs to determine which schools were subject to accountability for their racial/ethnic subgroups. Using a regression discontinuity design and data from North Carolina, this study examines the effects of this policy on teacher turnover and attrition. Subgroup-specific accountability had no overall effects on teacher turnover or attrition, but the policy caused black teachers who taught in schools that were held accountable for the black student subgroup to leave teaching at significantly lower rates, compared with black teachers who

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Pimvichai, Piyatida, Henrik Enghoff, and Thierry Backeljau. "Morphological and DNA Sequence Data of Two New Millipede Species of the Thyropygus induratus Subgroup (Diplopoda: Spirostreptida: Harpagophoridae)." Tropical Natural History, no. 7 (May 6, 2023): 107–22. https://doi.org/10.58837/tnh.23.7.258818.

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The common cylindrical millipede genus Thyropygus (family Harpagophoridae) comprises four informal species groups, one of which is the T. allevatus group. This group is characterized by two synapomorphic gonopodal characters: (1) gonopods with tibial and femoral spines and (2) the tibial spine is very long and recurved proximad towards the femoral spine. The T. allevatus group is further divided into four subgroups: (1) T. allevatus subgroup, (2) T. opinatus subgroup, (3) T. induratus subgroup, and (4) T. cuisinieri subgroup. Based on gonopodal characters and COI barcoding, two new species of

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Benson, Scott J., Brian L. Ruis, Aly M. Fadly, and Kathleen F. Conklin. "The Unique Envelope Gene of the Subgroup J Avian Leukosis Virus Derives from ev/J Proviruses, a Novel Family of Avian Endogenous Viruses." Journal of Virology 72, no. 12 (1998): 10157–64. http://dx.doi.org/10.1128/jvi.72.12.10157-10164.1998.

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ABSTRACT A new subgroup of avian leukosis virus (ALV), designated subgroup J, was identified recently. Viruses of this subgroup do not cross-interfere with viruses of the avian A, B, C, D, and E subgroups, are not neutralized by antisera raised against the other virus subgroups, and have a broader host range than the A to E subgroups. Sequence comparisons reveal that while the subgroup J envelope gene includes some regions that are related to those found inenv genes of the A to E subgroups, the majority of the subgroup J gene is composed of sequences either that are more similar to those of a

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Robinson, Giles W., Matthew Parker, Tanya Kranenburg, et al. "Use of whole genome sequencing to identify novel mutations in distinct subgroups of medulloblastoma." Journal of Clinical Oncology 30, no. 15_suppl (2012): 9518. http://dx.doi.org/10.1200/jco.2012.30.15_suppl.9518.

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9518 Background: Medulloblastoma is a malignant childhood brain tumor comprising four discrete subgroups (SHH-subgroup, WNT-subgroup, subgroup-3 and subgroup-4). The genetic alterations that drive these subgroups and that might serve as treatment targets are largely unknown. Methods: We sequenced entire genomes of 37 tumors and matched normal blood. 136 somatically mutated genes identified in this discovery cohort were sequenced in an additional 56 medulloblastomas. All tumors were classified into the 4 subgroups by expression profiling and immunohistochemistry. All mutations were validated by

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Zhang, Sheng, Fei Liang, Wenfeng Li, and Xichun Hu. "Subgroup Analyses in Reporting of Phase III Clinical Trials in Solid Tumors." Journal of Clinical Oncology 33, no. 15 (2015): 1697–702. http://dx.doi.org/10.1200/jco.2014.59.8862.

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Purpose Treatment decisions in clinical oncology are guided by results from phase III randomized clinical trials (RCTs). The results of subgroup analyses may be potentially important in individualizing patient care. We investigated the appropriateness of the use and interpretation of subgroup analyses in oncology RCTs on the basis of the CONSORT statement requirements. Methods Phase III RCTs published between January 1, 2011, and December 31, 2013, were reviewed to identify eligible studies of solid tumor treatments. Information related to the subgroup analyses included prespecification, numbe

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Koopman, Laura, Geert J. M. G. van der Heijden, Arno W. Hoes, Diederick E. Grobbee, and Maroeska M. Rovers. "Empirical comparison of subgroup effects in conventional and individual patient data meta-analyses." International Journal of Technology Assessment in Health Care 24, no. 03 (2008): 358–61. http://dx.doi.org/10.1017/s0266462308080471.

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Objectives:Individual patient data (IPD) meta-analyses have been proposed as a major improvement in meta-analytic methods to study subgroup effects. Subgroup effects of conventional and IPD meta-analyses using identical data have not been compared. Our objective is to compare such subgroup effects using the data of six trials (n= 1,643) on the effectiveness of antibiotics in children with acute otitis media (AOM).Methods:Effects (relative risks, risk differences [RD], and their confidence intervals [CI]) of antibiotics in subgroups of children with AOM resulting from (i) conventional meta-anal

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Aronson, Doron. "Subgroup analyses with special reference to the effect of antiplatelet agents in acute coronary syndromes." Thrombosis and Haemostasis 112, no. 07 (2014): 16–25. http://dx.doi.org/10.1160/th13-09-0801.

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SummaryControlled trials estimate treatment effects averaged over the reference population of subjects. However, physicians are interested in whether the treatment effect varies across subgroups (effect heterogeneity) in order to target specific subgroups to maximise the benefit of treatment and minimise harm. Therefore, large clinical trials of antiplatelet agents include subgroup analyses that examine whether treatment effects differ between subgroups of subjects identified by baseline characteristics. Reporting subgroup is pervasive and often accompanied by claims of difference of treatment

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Raskin, Scott I., Peter M. K. de Blank, Catherine A. Billups, Yimei Li, James M. Olson, and Sarah E. S. Leary. "MDB-86. ISOTRETINOIN HAS NO EFFECT ON EVENT FREE SURVIVAL ACROSS HIGH-RISK MEDULLOBLASTOMA MOLECULAR SUBGROUPS WHEN ADDED TO MAINTENANCE CHEMOTHERAPY: A SECONDARY ANALYSIS OF THE CHILDREN’S ONCOLOGY GROUP ACNS0332 DATA." Neuro-Oncology 26, Supplement_4 (2024): 0. http://dx.doi.org/10.1093/neuonc/noae064.534.

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Abstract BACKGROUND Medulloblastoma has been redefined into four molecular subgroups (WNT, SHH, Group 3, and Group 4), with distinct prognoses and responses to treatment. The ACNS0332 trial investigated the impact of carboplatin and isotretinoin in high-risk medulloblastoma finding a differential effect of carboplatin by molecular subgroup. An interim futility analysis showed no benefit from isotretinoin, but did not evaluate results by molecular subgroup in the primary analysis. We performed a secondary analysis to examine whether the effect of isotretinoin on event-free survival (EFS) differ

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Yang, Hanlu, Trung Vu, Qunfang Long, Vince Calhoun, and Tülay Adali. "Identification of Homogeneous Subgroups from Resting-State fMRI Data." Sensors 23, no. 6 (2023): 3264. http://dx.doi.org/10.3390/s23063264.

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The identification of homogeneous subgroups of patients with psychiatric disorders can play an important role in achieving personalized medicine and is essential to provide insights for understanding neuropsychological mechanisms of various mental disorders. The functional connectivity profiles obtained from functional magnetic resonance imaging (fMRI) data have been shown to be unique to each individual, similar to fingerprints; however, their use in characterizing psychiatric disorders in a clinically useful way is still being studied. In this work, we propose a framework that makes use of f

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Salomon, Björn. "Interspecific hybridizations in the Elymus semicostatus group (Poaceae)." Genome 36, no. 5 (1993): 899–905. http://dx.doi.org/10.1139/g93-118.

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Meiotic pairing in 16 interspecific hybrids in the genus Elymus is reported. The hybrids were made among seven species in the Elymus semicostatus group, viz., E. semicostatus, E. validus (subgroup I), E. abolinii (subgroup II), E. fedtschenkoi, E. nevskii, E. praeruptus (subgroup III), and E. panormitanus (subgroup IV). All species are tetraploid (2n = 4x = 28) and possess the SY genomes. Meiotic pairing was distinctly higher in hybrids made within subgroups than between subgroups, but the genomes in E. panormitanus have differentiated from those in the other species. These results generally s

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Harris, E. K., and J. C. Boyd. "On dividing reference data into subgroups to produce separate reference ranges." Clinical Chemistry 36, no. 2 (1990): 265–70. http://dx.doi.org/10.1093/clinchem/36.2.265.

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Abstract We consider statistical criteria for partitioning a reference database to obtain separate reference ranges for different subpopulations. Using general formulas relating population variances, sample sizes, and the normal deviate test for the significance of the difference between two subgroup means, we show that partitioning into separate ranges produces little reduction in between-person variability, even when the differences between means are highly significant statistically. However, when there is a clear physiological basis for distinguishing between certain subgroups, simulation s

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Nanlin Jin, Peter Flach, Tom Wilcox, Royston Sellman, Joshua Thumim, and Arno Knobbe. "Subgroup Discovery in Smart Electricity Meter Data." IEEE Transactions on Industrial Informatics 10, no. 2 (2014): 1327–36. http://dx.doi.org/10.1109/tii.2014.2311968.

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Tsai, Kao-Tai, and Karl Peace. "Analysis of Subgroup Data of Clinical Trials." Journal of Causal Inference 1, no. 2 (2013): 193–207. http://dx.doi.org/10.1515/jci-2012-0008.

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AbstractLarge randomized controlled clinical trials are the gold standard to evaluate and compare the effects of treatments. It is common practice for investigators to explore and even attempt to compare treatments, beyond the first round of primary analyses, for various subsets of the study populations based on scientific or clinical interests to take advantage of the potentially rich information contained in the clinical database. Although subjects are randomized to treatment groups in clinical trials, this does not imply the same degree of randomization among sub-populations of the original

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Groenwold, Rolf H. H. "Confounding of Subgroup Analyses in Randomized Data." Archives of Internal Medicine 169, no. 16 (2009): 1532. http://dx.doi.org/10.1001/archinternmed.2009.250.

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Foster, Jared C., Jeremy M. G. Taylor, and Stephen J. Ruberg. "Subgroup identification from randomized clinical trial data." Statistics in Medicine 30, no. 24 (2011): 2867–80. http://dx.doi.org/10.1002/sim.4322.

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Shao, Lihui, Jiaqi Wu, Weiping Zhang, and Yu Chen. "Integrated subgroup identification from multi-source data." Computational Statistics & Data Analysis 193 (May 2024): 107918. http://dx.doi.org/10.1016/j.csda.2024.107918.

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Xing, Yu, Wensheng Zhu, and Kyongson Jon. "Robust subgroup analysis for network-linked data." Statistics and Its Interface 17, no. 3 (2024): 357–70. http://dx.doi.org/10.4310/23-sii774.

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Shiozawa, Yusuke, Luca Malcovati, Anna Gallì, et al. "Combined DNA and Transcriptome Sequencing Reveals Discrete Subtypes of Myelodysplasia." Blood 128, no. 22 (2016): 1974. http://dx.doi.org/10.1182/blood.v128.22.1974.1974.

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Abstract Introduction Although gene expression profile of myelodysplastic syndromes (MDS) had been widely studied, gene expression-based disease classification was yet to be established. We performed combined DNA and transcriptome sequencing to assess the relationship between genomic lesions, transcriptomic data, hematologic phenotype, and clinical outcome were analyzed. Methods We enrolled a total of 214 patients with myeloid neoplasms with myelodysplasia, for whom complete clinical and pathological data were available. Oncogenic variants and copy number alterations were identified by targete

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Cummins, C. J. "Fundamental domains for genus-zero and genus-one congruence subgroups." LMS Journal of Computation and Mathematics 13 (July 23, 2010): 222–45. http://dx.doi.org/10.1112/s1461157008000041.

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AbstractIn this paper, we compute Ford fundamental domains for all genus-zero and genus-one congruence subgroups. This is a continuation of previous work, which found all such groups, including ones that are not subgroups ofPSL(2,ℤ). To compute these fundamental domains, an algorithm is given that takes the following as its input: a positive square-free integerf, which determines a maximal discrete subgroup Γ0(f)+ofSL(2,ℝ); a decision procedure to determine whether a given element of Γ0(f)+is in a subgroupG; and the index ofGin Γ0(f)+. The output consists of: a fundamental domain forG, a finit

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Dong, Jing, Junni L. Zhang, Shuxi Zeng, and Fan Li. "Subgroup balancing propensity score." Statistical Methods in Medical Research 29, no. 3 (2019): 659–76. http://dx.doi.org/10.1177/0962280219870836.

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This paper concerns estimation of subgroup treatment effects with observational data. Existing propensity score methods are mostly developed for estimating overall treatment effect. Although the true propensity scores balance covariates in any subpopulations, the estimated propensity scores may result in severe imbalance in subgroup samples. Indeed, subgroup analysis amplifies a bias-variance tradeoff, whereby increasing complexity of the propensity score model may help to achieve covariate balance within subgroups, but it also increases variance. We propose a new method, the subgroup balancin

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Lyons-Warren, Ariel M., Michael F. Wangler, and Ying-Wooi Wan. "Cluster Analysis of Short Sensory Profile Data Reveals Sensory-Based Subgroups in Autism Spectrum Disorder." International Journal of Molecular Sciences 23, no. 21 (2022): 13030. http://dx.doi.org/10.3390/ijms232113030.

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Autism spectrum disorder is a common, heterogeneous neurodevelopmental disorder lacking targeted treatments. Additional features include restricted, repetitive patterns of behaviors and differences in sensory processing. We hypothesized that detailed sensory features including modality specific hyper- and hypo-sensitivity could be used to identify clinically recognizable subgroups with unique underlying gene variants. Participants included 378 individuals with a clinical diagnosis of autism spectrum disorder who contributed Short Sensory Profile data assessing the frequency of sensory behavior

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Nikolaidis, Nikolas, and Zacharias G. Scouras. "The Drosophila montium subgroup species. Phylogenetic relationships based on mitochondrial DNA analysis." Genome 39, no. 5 (1996): 874–83. http://dx.doi.org/10.1139/g96-110.

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Mitochondrial DNA (mtDNA) restriction site maps for three Drosophila montium subgroup species of the melanogaster species group, inhabiting Indian and Afrotropical montium subgroup territories, were established. Taking into account previous mtDNA data concerning six oriental montium species, a phylogeny was established using distance-matrix and parsimony methods. Both genetic diversity and mtDNA size variations were found to be very narrow, suggesting close phylogenetic relationships among all montium species studied. The phylogenetic trees that were constructed revealed three main lineages fo

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Anjum, Muhammad Umair, Syed Arsalan Ahmed Naqvi, Kaneez Zahra Rubab Khakwani, et al. "Heterogeneity in subgroup reporting across clinical trials assessing systemic therapies in metastatic castration resistant prostate cancer: A report from a living systematic review." Journal of Clinical Oncology 43, no. 5_suppl (2025): 270. https://doi.org/10.1200/jco.2025.43.5_suppl.270.

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270 Background: Inconsistent reporting of subgroup data across clinical trials makes interpretation and application of the evidence from trials challenging. We conducted a living systematic review to summarize evidence for American Society of Clinical Oncology (ASCO) guidelines for management of metastatic castration resistant prostate cancer (mCRPC). Here, we provide an overview of reporting patterns of subgroup data across mCRPC trials. Methods: MEDLINE and EMBASE were systematically searched from each database's inception through September 20 th , 2024, to identify phase II/III/IV randomize

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Li, Peng-Fei, and Wei-Liang Chen. "Are the Different Diabetes Subgroups Correlated With All-Cause, Cancer-Related, and Cardiovascular-Related Mortality?" Journal of Clinical Endocrinology & Metabolism 105, no. 12 (2020): e4240-e4251. http://dx.doi.org/10.1210/clinem/dgaa628.

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Abstract Context Numerous studies have shown that cardiovascular disease (CVD) represents the most important cause of mortality among people with diabetes mellitus (DM). However, no studies have evaluated the risk of CVD-related mortality among different DM subgroups. Objective We aimed to examine all-cause, CVD-related, and cancer-related mortality for different DM subgroups. Design, Setting, Patients, and Interventions We included participants (age ≥ 20 years) from the National Health and Nutrition Examination Survey III (NHANES III) data set. We evaluated the risks of all-cause and cause-sp

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Hein, Kaung Htet, Wai Lok Woo, and Gholamreza Rafiee. "Integrative Machine Learning Framework for Enhanced Subgroup Classification in Medulloblastoma." Healthcare 13, no. 10 (2025): 1114. https://doi.org/10.3390/healthcare13101114.

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Background: Medulloblastoma is the most common malignant brain tumor in children, classified into four primary molecular subgroups: WNT, SHH, Group 3, and Group 4, each exhibiting significant molecular heterogeneity and varied survival outcomes. Accurate classification of these subgroups is crucial for optimizing treatments and improving patient outcomes. DNA methylation profiling is a promising approach for subgroup classification; however, its application is still evolving, with ongoing efforts to improve accessibility and develop more accurate classification methods. Objectives: This study

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Guo, Bing, and Carmen Moga. "OP72 Added Value Of Using Individual Patient Data Meta-analysis." International Journal of Technology Assessment in Health Care 34, S1 (2018): 26. http://dx.doi.org/10.1017/s0266462318001125.

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Introduction:Although individual patient data meta-analysis (IPD MA) is considered the gold standard of systematic reviews (SRs), a recent International Network of Agencies for Health Technology Assessment survey indicates that IPD MA is not frequently included in a health technology assessment (HTA), or conducted by HTA researchers. The objective of this presentation is to describe our first experience with including an IPD MA in a HTA report, discuss the added value for an evidence-based decision-making process, and advocate for expanding work in this field.Methods:An overview of SRs on endo

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Roossinck, Marilyn J., Lee Zhang, and Karl-Heinz Hellwald. "Rearrangements in the 5′ Nontranslated Region and Phylogenetic Analyses of Cucumber Mosaic Virus RNA 3 Indicate Radial Evolution of Three Subgroups." Journal of Virology 73, no. 8 (1999): 6752–58. http://dx.doi.org/10.1128/jvi.73.8.6752-6758.1999.

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ABSTRACT Cucumber mosaic virus (CMV) has been divided into two subgroups based on serological data, peptide mapping of the coat protein, nucleic acid hybridization, and nucleotide sequence similarity. Analyses of a number of recently isolated strains suggest a further division of the subgroup I strains. Alignment of the 5′ nontranslated regions of RNA 3 for 26 strains of CMV suggests the division of CMV into subgroups IA, IB, and II and suggests that rearrangements, deletions, and insertions in this region may have been the precursors of the subsequent radiation of each subgroup. Phylogeny ana

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Bello-Chavolla, Omar Yaxmehen, Jessica Paola Bahena-López, Arsenio Vargas-Vázquez, et al. "Clinical characterization of data-driven diabetes subgroups in Mexicans using a reproducible machine learning approach." BMJ Open Diabetes Research & Care 8, no. 1 (2020): e001550. http://dx.doi.org/10.1136/bmjdrc-2020-001550.

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IntroductionPrevious reports in European populations demonstrated the existence of five data-driven adult-onset diabetes subgroups. Here, we use self-normalizing neural networks (SNNN) to improve reproducibility of these data-driven diabetes subgroups in Mexican cohorts to extend its application to more diverse settings.Research design and methodsWe trained SNNN and compared it with k-means clustering to classify diabetes subgroups in a multiethnic and representative population-based National Health and Nutrition Examination Survey (NHANES) datasets with all available measures (training sample

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Karsidag, S., A. Akcal, S. Sahin, S. Karsidag, F. Kabukcuoglu, and K. Ugurlu. "Neurophysiological and morphological responses to treatment with acetyl-L-carnitine in a sciatic nerve injury model: preliminary data." Journal of Hand Surgery (European Volume) 37, no. 6 (2011): 529–36. http://dx.doi.org/10.1177/1753193411426969.

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We investigated the effects of acetyl-L-carnitine (ALCAR) on the recovery of sciatic nerve injuries in rats. Sprague Dawley rats were randomized to two groups: ALCAR treated (for 14 days) and control. Each group was divided into three subgroups: distal transection, proximal transection, and grafted. Distal latencies, amplitudes, and motor nerve conduction velocities were measured. In the third month, biopsies were taken and examined under light microscopy. Electrophysiological measurements demonstrated that regeneration occurred earlier and was better in the ALCAR group, particularly in the di

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Obura, Morgan, Joline W. J. Beulens, Roderick Slieker, et al. "Post-load glucose subgroups and associated metabolic traits in individuals with type 2 diabetes: An IMI-DIRECT study." PLOS ONE 15, no. 11 (2020): e0242360. http://dx.doi.org/10.1371/journal.pone.0242360.

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Aim Subclasses of different glycaemic disturbances could explain the variation in characteristics of individuals with type 2 diabetes (T2D). We aimed to examine the association between subgroups based on their glucose curves during a five-point mixed-meal tolerance test (MMT) and metabolic traits at baseline and glycaemic deterioration in individuals with T2D. Methods The study included 787 individuals with newly diagnosed T2D from the Diabetes Research on Patient Stratification (IMI-DIRECT) Study. Latent class trajectory analysis (LCTA) was used to identify distinct glucose curve subgroups du

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Wu, Mei Jiun. "School Resources and Subgroup Performance Gains: What Works for Whom?" Educational Administration Quarterly 56, no. 2 (2019): 220–54. http://dx.doi.org/10.1177/0013161x19840400.

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Using a fixed effects model, a balanced panel data set of 6,922 schools in California from 2004 to 2011 was analyzed to see whether changes in resources would affect subgroup performance at intraschool level. Seven school resources variables previously demonstrated influential to school or subgroup achievement at interschool level were tested for their effects on Academic Performance Index (API) gains of eight subgroups. Teachers’ in-district experience had the strongest positive impacts on API gains for all subgroups, ranging from 3.367 to 8.958 points, and teachers’ total experience had the

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Bancks, Michael P., Alain G. Bertoni, Mercedes Carnethon, et al. "Association of Diabetes Subgroups With Race/Ethnicity, Risk Factor Burden and Complications: The MASALA and MESA Studies." Journal of Clinical Endocrinology & Metabolism 106, no. 5 (2021): e2106-e2115. http://dx.doi.org/10.1210/clinem/dgaa962.

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Abstract Introduction There are known disparities in diabetes complications by race and ethnicity. Although diabetes subgroups may contribute to differential risk, little is known about how subgroups vary by race/ethnicity. Methods Data were pooled from 1293 (46% female) participants of the Mediators of Atherosclerosis in South Asians Living in America (MASALA) and the Multi-Ethnic Study of Atherosclerosis (MESA) who had diabetes (determined by diabetes medication use, fasting glucose, and glycated hemoglobin [HbA1c]), including 217 South Asian, 240 non-Hispanic white, 125 Chinese, 387 African

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Park, Hyun-Jeong, Dayeon Lee, and Hyeonju Kwon. "Fitting the GLMM tree to educational data: focusing on differential effects of afterschool program and private tutoring participation." Korean Society for Educational Evaluation 35, no. 4 (2022): 577–607. http://dx.doi.org/10.31158/jeev.2022.35.4.577.

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This study aims to introduce the GLMM tree and show its usefulness in educational research. The GLMM tree model is an extension of MOB to be applied to multilevel data, which detects subgroups with differential effects to estimate the fixed-effect in each subgroup and the random effect by the cluster to which each observation belongs. In this study, we identified the differential effects by detecting subgroups of afterschool programs and private tutoring participation in mathematics achievement of second grade high school students. Using the GLMM trees, students were divided into 16 and 15 sub

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Roelvink, Peter W., Alena Lizonova, Jennifer G. M. Lee, et al. "The Coxsackievirus-Adenovirus Receptor Protein Can Function as a Cellular Attachment Protein for Adenovirus Serotypes from Subgroups A, C, D, E, and F." Journal of Virology 72, no. 10 (1998): 7909–15. http://dx.doi.org/10.1128/jvi.72.10.7909-7915.1998.

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ABSTRACT Attachment of an adenovirus (Ad) to a cell is mediated by the capsid fiber protein. To date, only the cellular fiber receptor for subgroup C serotypes 2 and 5, the so-called coxsackievirus-adenovirus receptor (CAR) protein, has been identified and cloned. Previous data suggested that the fiber of the subgroup D serotype Ad9 also recognizes CAR, since Ad9 and Ad2 fiber knobs cross-blocked each other’s cellular binding. Recombinant fiber knobs and3H-labeled Ad virions from serotypes representing all six subgroups (A to F) were used to determine whether the knobs cross-blocked the bindin

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Zhu, Yuan-Chang, Tong-Hua Wu, Guan-Gui Li, et al. "Decrease in fertilization and cleavage rates, but not in clinical outcomes for infertile men with AZF microdeletion of the Y chromosome." Zygote 23, no. 5 (2014): 771–77. http://dx.doi.org/10.1017/s096719941400046x.

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SummaryThis study aimed to explore whether the presence of a Y chromosome azoospermia factor (AZF) microdeletion confers any adverse effect on embryonic development and clinical outcomes after intracytoplasmic sperm injection (ICSI) treatment. Fifty-seven patients with AZF microdeletion were included in the present study and 114 oligozoospermia and azoospermia patients without AZF microdeletion were recruited as controls. Both AZF and control groups were further divided into subgroups based upon the methods of semen collection: the AZF-testicular sperm extraction subgroup (AZF-TESE, n = 14), t

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Raghavendra, Meghana, Mohammed Al-Hamadani, and Ronald S. Go. "Long-Term (10 Years or More) Survivors Of Multiple Myeloma: A Population-Based Analysis Of The US National Cancer Data Base." Blood 122, no. 21 (2013): 760. http://dx.doi.org/10.1182/blood.v122.21.760.760.

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Abstract Introduction Long-term survivors in multiple myeloma (MM), described as those surviving >10 years since their diagnosis, are uncommon. There is paucity of data describing this subgroup of patients and how they differ clinically from the rest. Methods Patients with MM diagnosed from 1998 to 2000 were identified in the National Cancer Data Base (NCDB). We obtained data associated with socio-demographics, type and location of care facility, as well as the use high dose chemotherapy/autologous stem cell transplant (ASCT) as initial treatment option. Four cohorts were created based on o

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Askanase, A., C. Stach, C. Brittain, G. Stojan, and R. Furie. "POS0115 DAPIROLIZUMAB PEGOL EFFICACY BY SUBGROUPS IN PATIENTS WITH SYSTEMIC LUPUS ERYTHEMATOSUS: A POST HOC ANALYSIS OF PHASE 2 CLINICAL TRIAL DATA." Annals of the Rheumatic Diseases 82, Suppl 1 (2023): 272.1–273. http://dx.doi.org/10.1136/annrheumdis-2023-eular.1991.

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BackgroundSystemic lupus erythematosus (SLE) clinical trials are challenged by high placebo response rates. Subgroup analyses of historic SLE clinical trial data have identified acute flares with normal complement levels as potential predictors of high placebo response.ObjectivesTo assess the treatment effect of dapirolizumab pegol (DZP; a polyethylene glycol conjugated antigen-binding fragment lacking a functional Fc domain, which inhibits the CD40–CD40 ligand interaction) in patients from the phase 2b trial in SLE,[1]who fulfilled one or both of the characteristics identified as potential pr

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Youngblood, Mark, Amar Sheth, Amy Zhao, et al. "GENE-56. MENINGIOMA GENOMIC SUBGROUP AS A PREDICTOR OF POST-OPERATIVE PATIENT OUTCOMES: IMPLICATIONS FOR TREATMENT AND FOLLOW-UP." Neuro-Oncology 21, Supplement_6 (2019): vi109—vi110. http://dx.doi.org/10.1093/neuonc/noz175.458.

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Abstract BACKGROUND Meningiomas can be classified into six genomic subgroups based on mutations in NF2, SMARCB1, KLF4, POLR2A, or activating variants in the PI3K or Hedgehog signaling pathways. Previous work has identified specific associations of driver events with clinical and molecular features, such as tumor location. However, their utility in predicting post-operative patient outcomes is not well-explored. Similar to recently described epigenetic signatures, underlying genomic subgroup may provide prognostic value in meningioma management. METHODS Targeted sequencing data was used to clas

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