Seq-ing improved gene expression estimates from microarrays using machine learning

Korir, Paul K; Geeleher, Paul; Seoighe, Cathal

doi:10.1186/s12859-015-0712-z

Cited by 8 publications

(4 citation statements)

References 37 publications

(45 reference statements)

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“…Further approaches were largely influenced by the coming era of routine next-generation sequencing (NGS) of mRNA (RNA sequencing or RNAseq) that has started roughly in the second decade of this century. Nowadays, RNAseq has become the gold standard and the basic tool for transcriptomic profiling [ 44 , 45 , 46 , 47 , 48 , 49 , 50 ]. In addition to measuring gene activities, RNAseq has also the potential of detecting mutations and overall tumor mutational burden [ 51 ], gene splice isoforms [ 52 ], and oncogenic fusion transcripts [ 53 , 54 , 55 , 56 ].…”

Section: The Problem Of Transcriptomic Data Harmonizationmentioning

confidence: 99%

Transcriptomic Harmonization as the Way for Suppressing Cross-Platform Bias and Batch Effect

Borisov

Buzdin

2022

Biomedicines

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(1) Background: Emergence of methods interrogating gene expression at high throughput gave birth to quantitative transcriptomics, but also posed a question of inter-comparison of expression profiles obtained using different equipment and protocols and/or in different series of experiments. Addressing this issue is challenging, because all of the above variables can dramatically influence gene expression signals and, therefore, cause a plethora of peculiar features in the transcriptomic profiles. Millions of transcriptomic profiles were obtained and deposited in public databases of which the usefulness is however strongly limited due to the inter-comparison issues; (2) Methods: Dozens of methods and software packages that can be generally classified as either flexible or predefined format harmonizers have been proposed, but none has become to the date the gold standard for unification of this type of Big Data; (3) Results: However, recent developments evidence that platform/protocol/batch bias can be efficiently reduced not only for the comparisons of limited transcriptomic datasets. Instead, instruments were proposed for transforming gene expression profiles into the universal, uniformly shaped format that can support multiple inter-comparisons for reasonable calculation costs. This forms a basement for universal indexing of all or most of all types of RNA sequencing and microarray hybridization profiles; (4) Conclusions: In this paper, we attempted to overview the landscape of modern approaches and methods in transcriptomic harmonization and focused on the practical aspects of their application.

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Section: The Problem Of Transcriptomic Data Harmonizationmentioning

confidence: 99%

Transcriptomic Harmonization as the Way for Suppressing Cross-Platform Bias and Batch Effect

Borisov

Buzdin

2022

Biomedicines

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“…New control is fed in all trees and a majority vote is taken for each rating model, the error is estimated in cases that are not used while building the tree. OOB (Out-of-bag) is called prediction error that is received as a percentage [16].…”

Section: Random Forestmentioning

confidence: 99%

Detecting Spammers in Twitter Network

Ameen¹,

Kaya²

2017

ijamec

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The goal of Twitter is to allow friends communicate and stay connected through the exchange of short messages. However, sometimes, spammers also use Twitter as a platform to post malicious links, send unsolicited messages to legitimate users, and hijack trending topics because of two problems of Twitter. These problems are the possibilities to automatically receive following users' updates and to write on followers' profile pages. For this reason, spam is becoming an increasing problem on Twitter day after day as other online social network sites are. In this article, we present several methods to detect spam tweets on Twitter. For this purpose, we utilize Naive Bayes, Random Forest J48, and IBK algorithms. The experiments conducted on real Twitter accounts demonstrate that the Random Forest algorithm gives us the best result to detect spammers in Twitter.

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“…Gene expression data are widely used in the fields of functional genomics and molecular medicine, e.g., in cancer research (∼350,000 PubMed papers found using search terms gene expression and cancer in April 2023). Two major approaches are used nowadays for large-scale transcriptional profiling: microarray hybridization (MH) of mRNA ( Lashkari et al, 1997 ; Bednár, 2000 ; King and Sinha, 2001 ; Rew, 2001 ) and mRNA sequencing (RNAseq) ( Nagalakshmi et al, 2008 ; Maher et al, 2009 ; Wang et al, 2009 ; Chu and Corey, 2012 ; Ingolia et al, 2012 ; Korir et al, 2015 ; Taylor et al, 2016 ). Both approaches utilize different rationales and can be further subdivided in several technological platforms.…”

Section: Introductionmentioning

confidence: 99%

“…Namely, the approaches were formulated for studying the incomparability of profiles obtained using different platforms ( Shi et al, 2006 ; Chen et al, 2007 ; Liang, 2007 ), for normalization of their expression data ( Bolstad et al, 2003 ; Benito et al, 2004 ; Jiang et al, 2004 ; Warnat et al, 2005 ; Johnson et al, 2007 ; Marron et al, 2007 ; Martinez et al, 2008 ; Shabalin et al, 2008 ; Xia et al, 2009 ; Huang et al, 2012 ), and for assessing quality of their co-normalization ( Shi et al, 2006 ; Chen et al, 2007 ; Liang, 2007 ; Rudy and Valafar, 2011 ; Deshwar and Morris, 2014 ). In turn, the routine next-generation sequencing (NGS) of mRNA (RNAseq) has largely replaced MH in many applications and became the gold standard for transcriptomic profiling ( Nagalakshmi et al, 2008 ; Maher et al, 2009 ; Wang et al, 2009 ; Chu and Corey, 2012 ; Ingolia et al, 2012 ; Korir et al, 2015 ; Taylor et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Uniformly shaped harmonization combines human transcriptomic data from different platforms while retaining their biological properties and differential gene expression patterns

Borisov,

Tkachev,

Simonov

et al. 2023

Front. Mol. Biosci.

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Introduction: Co-normalization of RNA profiles obtained using different experimental platforms and protocols opens avenue for comprehensive comparison of relevant features like differentially expressed genes associated with disease. Currently, most of bioinformatic tools enable normalization in a flexible format that depends on the individual datasets under analysis. Thus, the output data of such normalizations will be poorly compatible with each other. Recently we proposed a new approach to gene expression data normalization termed Shambhala which returns harmonized data in a uniform shape, where every expression profile is transformed into a pre-defined universal format. We previously showed that following shambhalization of human RNA profiles, overall tissue-specific clustering features are strongly retained while platform-specific clustering is dramatically reduced.Methods: Here, we tested Shambhala performance in retention of fold-change gene expression features and other functional characteristics of gene clusters such as pathway activation levels and predicted cancer drug activity scores.Results: Using 6,793 cancer and 11,135 normal tissue gene expression profiles from the literature and experimental datasets, we applied twelve performance criteria for different versions of Shambhala and other methods of transcriptomic harmonization with flexible output data format. Such criteria dealt with the biological type classifiers, hierarchical clustering, correlation/regression properties, stability of drug efficiency scores, and data quality for using machine learning classifiers.Discussion: Shambhala-2 harmonizer demonstrated the best results with the close to 1 correlation and linear regression coefficients for the comparison of training vs validation datasets and more than two times lesser instability for calculation of drug efficiency scores compared to other methods.

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Seq-ing improved gene expression estimates from microarrays using machine learning

Cited by 8 publications

References 37 publications

Transcriptomic Harmonization as the Way for Suppressing Cross-Platform Bias and Batch Effect

Transcriptomic Harmonization as the Way for Suppressing Cross-Platform Bias and Batch Effect

Detecting Spammers in Twitter Network

Uniformly shaped harmonization combines human transcriptomic data from different platforms while retaining their biological properties and differential gene expression patterns

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