Big Data Trends in Bioinformatics

Silva, Dennis Savio M. da; Silva, Waldeyr M. C. da; Guo, Ruizhe; Bernardi, Ana Paula; Mariano, Ari Melo; Holanda, Maristela

doi:10.1109/bibm47256.2019.8982963

Cited by 13 publications

(3 citation statements)

References 15 publications

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“…184 Agricultural and life scientists and engineers of all types are wrestling with massive omics datasets that have been generated by advances in high-throughput sequencing and other molecular biology analytic methods. [195][196][197][198][199][200] However, agricultural scientists and engineers have the additional challenge of integrating omics data with data sourced from precision agriculture, plant breeding phenomics, economics and market sectors, and governmental regulations, just to name a few. 199,[201][202][203] This adds an additional layer of complexity for harnessing these massive datasets for generating viable and sustainable solutions for Fig.…”

Section: Big Data and Digital Sciencesmentioning

confidence: 99%

Catalyzing Holistic Agriculture Innovation Through Industrial Biotechnology

Walker

Buhler

2020

Industrial Biotechnology

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Section: Big Data and Digital Sciencesmentioning

confidence: 99%

Catalyzing Holistic Agriculture Innovation Through Industrial Biotechnology

Walker

Buhler

2020

Industrial Biotechnology

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“…Large datasets and broad comparative studies have become the research paradigm across multiple disciplines of biology ( Stephens et al 2015 , da Silva et al 2019 , Muñoz and Price 2019 , Yu and Nielsen 2019 , Pal et al 2020 , Wüest et al 2020 , Xia et al 2020 , Tolani et al 2021 ). The rise of -omics technologies and high-throughput data collection permit rapid collection of data reflecting a wide variety of physiological, morphological, behavioral, and ecological traits (e.g.…”

Section: Introductionmentioning

confidence: 99%

PhyloMatcher: a tool for resolving conflicts in taxonomic nomenclature

Rader,

Pivovarnik,

Vantilburg

et al. 2023

Bioinformatics Advances

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Summary Large-scale comparative studies rely on the application of both phylogenetic trees and phenotypic data, both of which come from a variety of sources, but due to the changing nature of phylogenetic classification over time, many taxon names in comparative datasets do not match the nomenclature in phylogenetic trees. Manual curation of taxonomic synonyms in large comparative datasets can be daunting. To address this issue, we introduce PhyloMatcher, a tool which allows for programmatic querying of the National Center for Biotechnology Information Taxonomy and Global Biodiversity Information Facility databases to find associated synonyms with given target species names. Availability and implementation PhyloMatcher is easily installed as a Python package with pip, or as a standalone GUI application. PhyloMatcher source code and documentation are freely available at https://github.com/Lswhiteh/PhyloMatcher, the GUI application can be downloaded from the Releases page.

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“…LDA is used in a various applications, including the classification of genome sequence [9], the discovery of discussion concepts in social networks [10], patient data modeling [11], topic extraction from medical reports [12], the discovery of scientific data and biomedical relationships [13,14]. The LDA method finds important clinical problems and formats clinical text reports in another investigation [15]. In other work, [16] used topic modeling to express scientific reports efficiently, allowing the analysis of the collections more quickly.…”

mentioning

confidence: 99%

A novel multiple kernel fuzzy topic modeling technique for biomedical data

et al. 2022

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Background Text mining in the biomedical field has received much attention and regarded as the important research area since a lot of biomedical data is in text format. Topic modeling is one of the popular methods among text mining techniques used to discover hidden semantic structures, so called topics. However, discovering topics from biomedical data is a challenging task due to the sparsity, redundancy, and unstructured format. Methods In this paper, we proposed a novel multiple kernel fuzzy topic modeling (MKFTM) technique using fusion probabilistic inverse document frequency and multiple kernel fuzzy c-means clustering algorithm for biomedical text mining. In detail, the proposed fusion probabilistic inverse document frequency method is used to estimate the weights of global terms while MKFTM generates frequencies of local and global terms with bag-of-words. In addition, the principal component analysis is applied to eliminate higher-order negative effects for term weights. Results Extensive experiments are conducted on six biomedical datasets. MKFTM achieved the highest classification accuracy 99.04%, 99.62%, 99.69%, 99.61% in the Muchmore Springer dataset and 94.10%, 89.45%, 92.91%, 90.35% in the Ohsumed dataset. The CH index value of MKFTM is higher, which shows that its clustering performance is better than state-of-the-art topic models. Conclusion We have confirmed from results that proposed MKFTM approach is very efficient to handles to sparsity and redundancy problem in biomedical text documents. MKFTM discovers semantically relevant topics with high accuracy for biomedical documents. Its gives better results for classification and clustering in biomedical documents. MKFTM is a new approach to topic modeling, which has the flexibility to work with a variety of clustering methods.

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Big Data Trends in Bioinformatics

Cited by 13 publications

References 15 publications

Catalyzing Holistic Agriculture Innovation Through Industrial Biotechnology

Catalyzing Holistic Agriculture Innovation Through Industrial Biotechnology

PhyloMatcher: a tool for resolving conflicts in taxonomic nomenclature

A novel multiple kernel fuzzy topic modeling technique for biomedical data

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