Dimensionality reduction and class prediction algorithm with application to microarray Big Data

Badaoui, Fadoua; Amar, Amine; Hassou, Laila Ait; Zoglat, Abdelhak; Okou, C. G.

doi:10.1186/s40537-017-0093-4

Cited by 15 publications

(5 citation statements)

References 46 publications

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“…(2020 examined tissue tropism of N. apis and N. ceranae in worker honey bees as well. It has been shown that the expression of the gene encoding vitellogenin (Vg), a glycolipoprotein produced and stored in the honey bees' fat body, is significantly reduced in bees infected with N. ceranae (Antunez et al 2009, Goblirsch et al 2013, Garrido et al 2016, Badaoui et al 2017. Recent studies have shown that N. ceranae C-type nosemosis has been reported to be the most common bee pathogen and has a major impact on global colony losses (Higes et al 2007, 2010, 2013, Paxton et al 2007, Cox-Foster et al 2007, Fries 2010.…”

Section: Introductionmentioning

confidence: 99%

Microscopic and Molecular Detection of Nosema Sp. In the Southwest Aegean Region

Kartal

Tunca

Özgül

et al. 2021

Uludağ Arıcılık Dergisi

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Beekeeping, performed in many parts of the world, has a very large place in the world trade market with bee products such as wax, bee venom, propolis and royal jelly, especially honey production. However, honey bee diseases are quite common and restricted the production of bee products. One of the most important of these diseases, Nosema, is caused by spores in intestinal epithelium cells of the honeybee. Nosema apis and Nosema ceranae are the factors of this disease and also common in our country. These two species can be distinguished from each other by molecular diagnostic methods. In this study, materials collected from 152 apiaries located in 13 districts of Muğla province and 62 water sources close to these apiaries. The spores were counted using Thoma lame under light microscope. DNA isolation was carried out from spore positive samples. 218MITOC FOR-REV and 321APIS FOR-REV primers were used to figure out the N. apis and N. ceranae species. After DNA sequence analysis of the obtained amplifications, it was determined that all samples formed 3 haplotypes according to studied sequences for the first time. In Muğla region, the presence of only N. ceranae as a disease agent was determined and the prevalence of the disease was detected at a rate of 71.53±6.02%. Moreover, blast analysis showed that the N. ceranae sequence detected high similarity (94-100 %) with the previously reported in Lebanon, France, Morocco and Thailand samples.

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Section: Introductionmentioning

confidence: 99%

Microscopic and Molecular Detection of Nosema Sp. In the Southwest Aegean Region

Kartal

Tunca

Özgül

et al. 2021

Uludağ Arıcılık Dergisi

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“…A dimensionality reduction was proposed with class prediction approach for gene expression data, by suggesting an innovative procedure using feature extraction and feature selection, for gaining correlation of the reduced data and eliminating redundancy respectively. Their approach was tested and compared with the state of the art [26].…”

Section: Literature Reviewmentioning

confidence: 99%

Optimized hybrid investigative based dimensionality reduction methods for malaria vector using KNN classifier

Arowolo

Adebiyi

et al. 2021

J Big Data

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RNA-Seq data are utilized for biological applications and decision making for the classification of genes. A lot of works in recent time are focused on reducing the dimension of RNA-Seq data. Dimensionality reduction approaches have been proposed in the transformation of these data. In this study, a novel optimized hybrid investigative approach is proposed. It combines an optimized genetic algorithm with Principal Component Analysis and Independent Component Analysis (GA-O-PCA and GAO-ICA), which are used to identify an optimum subset and latent correlated features, respectively. The classifier uses KNN on the reduced mosquito Anopheles gambiae dataset, to enhance the accuracy and scalability in the gene expression analysis. The proposed algorithm is used to fetch relevant features based on the high-dimensional input feature space. A fast algorithm for feature ranking is used to select relevant features. The performances of the model are evaluated and validated using the classification accuracy to compare existing approaches in the literature. The achieved experimental results prove to be promising for selecting relevant genes and classifying pertinent gene expression data analysis by indicating that the approach is capable of adding to prevailing machine learning methods.

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“…It is based on the empirical observation that lowly expressed genes are more likely to be affected by dropout than highly expressed genes. Badaoui, Amar, Hassou, Zoglat, and Okou (2017) proposed an approach based on feature extraction and selection. The feature extraction is based on correlation and rank analysis leading to a reduction in the number of variables.…”

Section: Dimensionality Reductionmentioning

confidence: 99%

Big data analytics in single‐cell transcriptomics: Five grand opportunities

Bhattacharya

Nelson

Ahuja

et al. 2021

WIREs Data Min & Knowl

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Single‐cell omics technologies provide biologists with a new dimension for systematically dissecting the underlying complexities within biological systems. These powerful technologies have triggered a wave of rapid development and deployment of new computational tools capable of teasing out critical insights by analysis of large volumes of omics data at single‐cell resolution. Some of the key advancements include identifying molecular signatures imparting cellular identities, their evolutionary relationships, identifying novel and rare cell‐types, and establishing a direct link between cellular genotypes and phenotypes. With the sharp increase in the throughput of single‐cell platforms, the demand for efficient computational algorithms has become prominent. As such, devising novel computational strategies is critical to ensure optimal use of this wealth of molecular data for gaining newer insights into cellular biology. Here we discuss some of the grand opportunities of computational breakthroughs which would accelerate single‐cell research. These are: predicting cellular identity, single‐cell guided in silico drug screening for precision medicine, transfer learning methods to handle sparsity and heterogeneity of expression data, establishing genotype–phenotype relationships at single‐cell resolution, and developing computational platforms for handling big data. This article is categorized under: Algorithmic Development > Biological Data Mining Fundamental Concepts of Data and Knowledge > Big Data Mining Technologies > Machine Learning

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Dimensionality reduction and class prediction algorithm with application to microarray Big Data

Cited by 15 publications

References 46 publications

Microscopic and Molecular Detection of Nosema Sp. In the Southwest Aegean Region

Microscopic and Molecular Detection of Nosema Sp. In the Southwest Aegean Region

Optimized hybrid investigative based dimensionality reduction methods for malaria vector using KNN classifier

Big data analytics in single‐cell transcriptomics: Five grand opportunities

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