Dynamic page sharing optimization for the R language

Kotthaus, Helena; Korb, Ingo; Engel, Michael; Marwedel, Peter

doi:10.1145/2775052.2661094

Cited by 8 publications

(6 citation statements)

References 18 publications

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“…The cuproptosis-related genes (CRGs) were collected from a previous study [17]. The Pearson correlation coe cients (PCCs) of the CRGs and FRGs expression levels were calculated using the "correlation test function (cor.test())" in R [24], the threshold was set as "|cor|>0.3 and p < 0.05", and a total of 175 CFRGs were identi ed.…”

Section: Data Preparation and The Cfrgs Identi Cationmentioning

confidence: 99%

Identification of 6 cuproptosis- and ferroptosis-related genes linking immune infiltration as diagnostic biomarkers for acute myocardial infarction

Zheng

Miao²,

Cao³

et al. 2023

Preprint

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The current diagnostic biomarkers of acute myocardial infarction (AMI), troponins, lack specificity and exist as false positives in other non-cardiac diseases. Previous studies revealed that cuproptosis, ferroptosis, and immune infiltration are all involved in the development of AMI. We hypothesize that combining the analysis of cuproptosis, ferroptosis, and immune infiltration in AMI will help identify more precise diagnostic biomarkers. The results showed that a total of 19 cuproptosis- and ferroptosis-related genes (CFRGs) were differentially expressed between the healthy and AMI groups. Functional enrichment analysis showed that the differential CFRGs were mostly enriched in biological processes related to oxidative stress and the inflammatory response. The immune infiltration status analyzed by ssGSEA found elevated levels of macrophages, neutrophils, and CCR in AMI. Then, we screened 6 immune-related CFRGs (CXCL2, DDIT3, DUSP1, CDKN1A, TLR4, STAT3) to construct a nomogram for predicting AMI and validated it in the GSE109048 dataset. Moreover, we also identified 5 pivotal miRNAs and 10 candidate drugs that target the 6 feature genes. Finally, RT-qPCR analysis verified that all 6 feature genes were upregulated in both animals and patients. In conclusion, our study reveals the significance of immune-related CFRGs in AMI and provides new insights for AMI diagnosis and treatment.

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Section: Data Preparation and The Cfrgs Identi Cationmentioning

confidence: 99%

Identification of 6 cuproptosis- and ferroptosis-related genes linking immune infiltration as diagnostic biomarkers for acute myocardial infarction

Zheng

Miao²,

Cao³

et al. 2023

Preprint

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show abstract

“…data on the methylation levels and the matched expression levels of the dMGs were extracted, and their Pearson correlation coefficients (PCCs) were calculated using the cor.test() function (https://stat. ethz.ch/R-manual/R-devel/library/stats/html/cor.test.html) in R (25). P<0.05 was set as the significance threshold.…”

Section: Analysis Of Correlation Between the Methylation Levels And Ementioning

confidence: 99%

A risk score system based on DNA methylation levels and a nomogram survival model for lung squamous cell carcinoma

Zhang

Sun

et al. 2020

Int J Mol Med

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Lung squamous cell carcinoma (LScc) is one of the primary types of non-small cell lung carcinoma, and patients with recurrent LScc usually have a poor prognosis. The present study was conducted to build a risk score (RS) system for LScc. Methylation data on LScc (training set) and on head and neck squamous cell carcinoma (validation set 2) were obtained from The cancer Genome Atlas database, and GSE39279 (validation set 1) was retrieved from the Gene Expression Omnibus database. differentially methylated protein-coding genes (dMGs)/long non-coding RNAs (dM-lncRNAs) between recurrence-associated samples and nonrecurrence samples were screened out using the limma package, and their correlation analysis was conducted using the cor.test() function. Following identification of the optimal combinations of dMGs or dM-lncRNAs using the penalized package in R, RS systems were built, and the system with optimal performance was selected. Using the rms package, a nomogram survival model was then constructed. For the differentially expressed genes (dEGs) between the high-and low-risk groups, pathway enrichment analysis was performed by Gene Set Enrichment Analysis. There were 335 dMGs and dM-lncRNAs in total. Following screening out of the top 10 genes (aldehyde dehydrogenase 7 family member A1, chromosome 8 open reading frame 48, cytokine-like 1, heat shock protein 90 alpha family class A member 1, isovaleryl-coA dehydrogenase, phosphodiesterase 3A, PNMA family member 2, SAM domain, SH3 domain and nuclear localization signals 1, thyroid hormone receptor interactor 13 and zinc finger protein 878) and 6 top lncRNAs, RS systems were constructed. According to Kaplan-Meier analysis, the dNA methylation level-based RS system exhibited the best performance. In combination with independent clinical prognostic factors, a nomogram survival model was built and successfully predicted patient survival. Furthermore, 820 dEGs between the high-and low-risk groups were identified, and 3 pathways were identified to be enriched in this gene set. The 10-DMG methylation level-based RS system and the nomogram survival model may be applied for predicting the outcomes of patients with LScc.

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“…When calling a wrapper function for the first time after evaluation, libmozart re-protects all allocated memory to re-enable capturing memory accesses. This technique has been used in other systems successfully [45,46] to inject laziness.…”

Section: C++ Client Librarymentioning

confidence: 99%

Optimizing data-intensive computations in existing libraries with split annotations

Palkar

Zaharia

2019

Proceedings of the 27th ACM Symposium on Operating Systems Principles

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Data movement between main memory and the CPU is a major bottleneck in parallel data-intensive applications. In response, researchers have proposed using compilers and intermediate representations (IRs) that apply optimizations such as loop fusion under existing high-level APIs such as NumPy and TensorFlow. Even though these techniques generally do not require changes to user applications, they require intrusive changes to the library itself: often, library developers must rewrite each function using a new IR. In this paper, we propose a new technique called split annotations (SAs) that enables key data movement optimizations over unmodified library functions. SAs only require developers to annotate functions and implement an API that specifies how to partition data in the library. The annotation and API describe how to enable cross-function data pipelining and parallelization, while respecting each function's correctness constraints. We implement a parallel runtime for SAs in a system called Mozart. We show that Mozart can accelerate workloads in libraries such as Intel MKL and Pandas by up to 15×, with no library modifications. Mozart also provides performance gains competitive with solutions that require rewriting libraries, and can sometimes outperform these systems by up to 2× by leveraging existing hand-optimized code.

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Dynamic page sharing optimization for the R language

Cited by 8 publications

References 18 publications

Identification of 6 cuproptosis- and ferroptosis-related genes linking immune infiltration as diagnostic biomarkers for acute myocardial infarction

Identification of 6 cuproptosis- and ferroptosis-related genes linking immune infiltration as diagnostic biomarkers for acute myocardial infarction

A risk score system based on DNA methylation levels and a nomogram survival model for lung squamous cell carcinoma

Optimizing data-intensive computations in existing libraries with split annotations

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