paper.bib

@Manual{rmanual,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2020},
}

@article{CZERNIAWSKI2020103131,
    title = {Automated digital modeling of existing buildings: A review of visual object recognition methods},
    journal = {Automation in Construction},
    volume = {113},
    pages = {103131},
    year = {2020},
    issn = {0926-5805},
    doi = {10.1016/j.autcon.2020.103131},
    url = {https://www.sciencedirect.com/science/article/pii/S0926580519309045},
    author = {Thomas Czerniawski and Fernanda Leite},
    keywords = {Review article, BIM, Building information modeling, Computer vision, Object recognition, Digitization, Laser scanning, Digital building representation, 3D reconstruction, As-built},
    abstract = {Digital building representations enable and promote new forms of simulation, automation, and information sharing. However, creating and maintaining these representations is prohibitively expensive. In an effort to make the adoption of this technology easier, researchers have been automating the digital modeling of existing buildings by applying reality capture devices and computer vision algorithms. This article is a summary of the efforts of the past ten years, with a particular focus on object recognition methods. We rectify three limitations of existing review articles by describing the general structure and variations of object recognition systems and performing an extensive and quantitative comparative performance evaluation. The coverage of building component classes (i.e. semantic coverage) and recognition performances are reported in-depth and framed using a building taxonomy. Research programs demonstrate sparse semantic coverage with a clear bias towards recognizing floor, wall, ceiling, door, and window classes. Comprehensive semantic coverage of building infrastructure will require a radical scaling and diversification of efforts.}
}

@article{sun_challenges_2021,
	title = {Challenges in benchmarking metagenomic profilers},
	volume = {18},
	issn = {1548-7105},
	url = {https://doi.org/10.1038/s41592-021-01141-3},
	doi = {10.1038/s41592-021-01141-3},
	abstract = {Accurate microbial identification and abundance estimation are crucial for metagenomics analysis. Various methods for classification of metagenomic data and estimation of taxonomic profiles, broadly referred to as metagenomic profilers, have been developed. Nevertheless, benchmarking of metagenomic profilers remains challenging because some tools are designed to report relative sequence abundance while others report relative taxonomic abundance. Here we show how misleading conclusions can be drawn by neglecting this distinction between relative abundance types when benchmarking metagenomic profilers. Moreover, we show compelling evidence that interchanging sequence abundance and taxonomic abundance will influence both per-sample summary statistics and cross-sample comparisons. We suggest that the microbiome research community pay attention to potentially misleading biological conclusions arising from this issue when benchmarking metagenomic profilers, by carefully considering the type of abundance data that were analyzed and interpreted and clearly stating the strategy used for metagenomic profiling.},
	number = {6},
	journal = {Nature Methods},
	author = {Sun, Zheng and Huang, Shi and Zhang, Meng and Zhu, Qiyun and Haiminen, Niina and Carrieri, Anna Paola and Vázquez-Baeza, Yoshiki and Parida, Laxmi and Kim, Ho-Cheol and Knight, Rob and Liu, Yang-Yu},
	month = jun,
	year = {2021},
	pages = {618--626},
}

@article{kim_how_2018,
	title = {How to {Reveal} {Magnitude} of {Gene} {Signals}: {Hierarchical} {Hypergeometric} {Complementary} {Cumulative} {Distribution} {Function}},
	volume = {14},
	issn = {1176-9343},
	url = {https://pubmed.ncbi.nlm.nih.gov/30364489},
	doi = {10.1177/1176934318797352},
	abstract = {This article introduces a new method for genome-wide association study (GWAS), hierarchical hypergeometric complementary cumulative distribution function (HH-CCDF). Existing GWAS methods, e.g. the linear model and hierarchical association coefficient algorithm, calculate the association between a marker variable and a phenotypic variable. The ideal GWAS practice is to calculate the association between a marker variable and a gene-signal variable. If the gene-signal variable and phenotypic variable are imperfectly proportional, the existing methods do not properly reveal the magnitude of the association between the gene-signal variable and marker variable. The HH-CCDF mitigates the impact of the imperfect proportionality between the phenotypic variable and gene-signal variable and thus better reveals the magnitude of gene signals. The HH-CCDF will provide new insights into GWAS approaches from the viewpoint of revealing the magnitude of gene signals.},
	language = {eng},
	journal = {Evolutionary bioinformatics online},
	author = {Kim, Bongsong},
	month = oct,
	year = {2018},
	note = {Publisher: SAGE Publications},
	keywords = {Genome-wide association study, hierarchical association coefficient algorithm, hierarchical binary categorization, hypergeometric complementary cumulative distribution function, magnitude of gene signals, quantitative trait loci},
	pages = {1176934318797352--1176934318797352},
}