Student Factors Affecting Latent Transition of Mathematics Achievement Measuring From Latent Transition Analysis with a Mixture Item Response Theory Measurement Model

Saengtrakul, Korawan; Kanjanawasee, Sirichai; Wiratchai, Nonglak

doi:10.1016/j.sbspro.2016.02.134

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…Treatment-specific networks were inferred using an unsupervised Bayesian biclustering approach implemented using the software BicMix [14,19]. The specification of the parameters for BicMix was based on results from simulation reports [20], for example, starting number of components is set as 100. Genes that had less than 1 TPM or 6 reads in more than one treatment-region group were removed.…”

Section: Treatment-specific Gene Network Based On Bayesian Biclusteringmentioning

confidence: 99%

Co-expression networks uncover regulation of splicing and transcription markers of disease

Zhang

Southey

Rodriguez-Zas

EPiC Series in Computing

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Gene co-expression networks based on gene expression data are usually used to capture biologically significant patterns, enabling the discovery of biomarkers and interpretation of regulatory relationships. However, the coordination of numerous splicing changes within and across genes can exert a substantial impact on the function of these genes. This is particularly impactful in studies of the properties of the nervous system, which can be masked in the networks that only assess the correlation between gene expression levels. A bioinformatics approach was developed to uncover the role of alternative splicing and associated transcriptional networks using RNA-seq profiles. Data from 40 samples, including control and two treatments associated with sensitivity to stimuli across two central nervous system regions that can present differential splicing, were explored. The gene expression and relative isoform levels were integrated into a transcriptome-wide matrix, and then Graphical Lasso was applied to capture the interactions between genes and isoforms. Next, functional enrichment analysis enabled the discovery of pathways dysregulated at the isoform or gene levels and the interpretation of these interactions within a central nervous region. In addition, a Bayesian biclustering strategy was used to reconstruct treatment-specific networks from gene expression profile, allowing the identification of hub molecules and visualization of highly connected modules of isoforms and genes in specific conditions. Our bioinformatics approach can offer comparable insights into the discovery of biomarkers and therapeutic targets for a wide range of diseases and conditions.

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Section: Treatment-specific Gene Network Based On Bayesian Biclusteringmentioning

confidence: 99%

Co-expression networks uncover regulation of splicing and transcription markers of disease

Zhang

Southey

Rodriguez-Zas

EPiC Series in Computing

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Latent Transition Analysis (LTA) : A Method for Identifying Differences in Longitudinal Change Among Unobserved Groups

Abarda

Dakkon

Azhari

et al. 2020

Procedia Computer Science

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Student Factors Affecting Latent Transition of Mathematics Achievement Measuring From Latent Transition Analysis with a Mixture Item Response Theory Measurement Model

Cited by 2 publications

References 9 publications

Co-expression networks uncover regulation of splicing and transcription markers of disease

Co-expression networks uncover regulation of splicing and transcription markers of disease

Latent Transition Analysis (LTA) : A Method for Identifying Differences in Longitudinal Change Among Unobserved Groups

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