array attribute - PMC

Trina Mouchahoir, John E. Schiel, Rich Rogers, Alan Heckert, Benjamin J. Place, Aaron Ammerman, Xiaoxiao Li, Tom Robinson, Brian Schmidt, Chris M. Chumsae, Xinbi Li, Anton V. Manuilov, Bo Yan, Gregory O. Staples, Da Ren, Alexander J. Veach, Dongdong Wang, Wael Yared, Zoran Sosic, Yan Wang, Li Zang, Anthony M. Leone, Peiran Liu, Richard Ludwig, Li Tao, Wei Wu, Ahmet Cansizoglu, Andrew Hanneman, Greg W. Adams, Irina Perdivara, Hunter Walker, Margo Wilson, Arnd Brandenburg, Nick DeGraan-Weber, Stefano Gotta, Joe Shambaugh, Melissa Alvarez, X. Christopher Yu, Li Cao, Chun Shao, Andrew Mahan, Hirsh Nanda, Kristen Nields, Nancy Nightlinger, Ben Niu, Jihong Wang, Wei Xu, Gabriella Leo, Nunzio Sepe, Yan-Hui Liu, Bhumit A. Patel, Douglas Richardson, Yi Wang, Daniela Tizabi, Oleg V. Borisov, Yali Lu, Ernest L. Maynard, Albrecht Gruhler, Kim F. Haselmann, Thomas N. Krogh, Carsten P. Sönksen, Simon Letarte, Sean Shen, Kristin Boggio, Keith Johnson, Wenqin Ni, Himakshi Patel, David Ripley, Jason C. Rouse, Ying Zhang, Carly Daniels, Andrew Dawdy, Olga Friese, Thomas W. Powers, Justin B. Sperry, Josh Woods, Eric Carlson, K. Ilker Sen, St John Skilton, Michelle Busch, Anders Lund, Martha Stapels, Xu Guo, Sibylle Heidelberger, Harini Kaluarachchi, Sean McCarthy, John Kim, Jing Zhen, Ying Zhou, Sarah Rogstad, Xiaoshi Wang, Jing Fang, Weibin Chen, Ying Qing Yu, John G. Hoogerheide, Rebecca Scott, Hua Yuan

J Am Soc Mass Spectrom. 2022 Sep 7; 33(9): 1659–1677. Published online 2022 Aug 26. doi: 10.1021/jasms.2c00129

PMCID:: PMC9460773

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Interval-valued intuitionistic fuzzy multi-attribute group decision-making method considering risk preference of decision-makers and its application

Sha Fu, Ye-zhi Xiao, Hang-jun Zhou

Sci Rep. 2022; 12: 11597. Published online 2022 Jul 8. doi: 10.1038/s41598-022-15815-1

PMCID:: PMC9270440

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Large-Scale Coarse-to-Fine Object Retrieval Ontology and Deep Local Multitask Learning

Ngoc Q. Ly, Tuong K. Do, Binh X. Nguyen

Comput Intell Neurosci. 2019; 2019: 1483294. Published online 2019 Jul 18. doi: 10.1155/2019/1483294

PMCID:: PMC6668564

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An integrated group decision-making method for the evaluation of hypertension follow-up systems using interval-valued q-rung orthopair fuzzy sets

Benting Wan, Zhaopeng Hu, Harish Garg, Youyu Cheng, Mengjie Han

Complex Intell Systems. 2023 Jan 20 : 1–34. doi: 10.1007/s40747-022-00953-w [Epub ahead of print]

PMCID:: PMC9853511

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Establishing haptic texture attribute space and predicting haptic attributes from image features using 1D-CNN

Waseem Hassan, Joolekha Bibi Joolee, Seokhee Jeon

Sci Rep. 2023; 13: 11684. Published online 2023 Jul 19. doi: 10.1038/s41598-023-38929-6

PMCID:: PMC10356925

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Dissociable neural mechanisms track evidence accumulation for selection of attention versus action

Amitai Shenhav, Mark A. Straccia, Sebastian Musslick, Jonathan D. Cohen, Matthew M. Botvinick

Nat Commun. 2018; 9: 2485. Published online 2018 Jun 27. doi: 10.1038/s41467-018-04841-1

PMCID:: PMC6021379

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Attribute reduction of SE-ISI concept lattices for incomplete contexts

Zhen Wang, Ling Wei, Jianjun Qi, Ting Qian

Soft comput. 2020; 24(20): 15143–15158. Published online 2020 Sep 6. doi: 10.1007/s00500-020-05271-2

PMCID:: PMC7474801

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An Improved ELECTRE II-Based Outranking Method for MADM with Double Hierarchy Hesitant Fuzzy Linguistic Sets and Its Application to Emergency Logistics Provider Selection

Zhengmin Liu, Di Wang, Yingjie Zhao, Xiaohan Zhang, Peide Liu

Int J Fuzzy Syst. 2023; 25(4): 1495–1517. Published online 2023 Jan 18. doi: 10.1007/s40815-022-01449-y

PMCID:: PMC9847466

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Aggregation operators and VIKOR method based on complex q-rung orthopair uncertain linguistic informations and their applications in multi-attribute decision making

Tahir Mahmood, Zeeshan Ali

Comp Appl Math. 2020; 39(4): 306. Published online 2020 Oct 30. doi: 10.1007/s40314-020-01332-2

PMCID:: PMC7597754

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A Tutorial on Cognitive Diagnosis Modeling for Characterizing Mental Health Symptom Profiles Using Existing Item Responses

Zhengqi Tan, Jimmy de la Torre, Wenchao Ma, David Huh, Mary E. Larimer, Eun-Young Mun

Prev Sci. 2023; 24(3): 480–492. Published online 2022 Feb 3. doi: 10.1007/s11121-022-01346-8

PMCID:: PMC10115700

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Gaze-dependent evidence accumulation predicts multi-alternative risky choice behaviour

Felix Molter, Armin W. Thomas, Scott A. Huettel, Hauke R. Heekeren, Peter N. C. Mohr

PLoS Comput Biol. 2022 Jul; 18(7): e1010283. Published online 2022 Jul 6. doi: 10.1371/journal.pcbi.1010283

PMCID:: PMC9292127

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Core Scientific Dataset Model: A lightweight and portable model and file format for multi-dimensional scientific data

Deepansh J. Srivastava, Thomas Vosegaard, Dominique Massiot, Philip J. Grandinetti

PLoS One. 2020; 15(1): e0225953. Published online 2020 Jan 2. doi: 10.1371/journal.pone.0225953

PMCID:: PMC6940021

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Location selection of electric vehicles charging stations by using the spherical fuzzy CPT–CoCoSo and D-CRITIC method

Huiyuan Zhang, Guiwu Wei

Comp Appl Math. 2023; 42(1): 60. Published online 2023 Jan 28. doi: 10.1007/s40314-022-02183-9

PMCID:: PMC9884158

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PMC Images search for array attribute

Figure 1

Schematic overview of the ProBic model and the conditional probability distributions of the attributes. Gene, Array and Expression represent the three ProBic classes of the PRM model. For each class, a set of specific gene, array and expression objects exists (denoted by the lowercase letters g, a and e respectively). The complete set of genes, array and expression objects that belong to a certain class are indicated by uppercase letters G, A and E. For the Gene (Array) class, a Boolean attribute B_b indicates whether a gene (array) belongs to a bicluster b or not. For each gene (array) object, the gene-bicluster labels g.B_b (over all biclusters b) and the array-bicluster labels a.B_b are the hidden variables of the model. Each object e of the Expression class has one single numeric attribute e.level that contains the expression level for each specific gene and array combination. The array class has an additional attribute ID that uniquely identifies each individual array object a.
The conditional probability distribution P(e.level|e.gene.B,e.array.B,e.array.ID) is modeled as a set of Normal distributions, one for each array-bicluster combination. A number of marginal distributions P(a.B_b), P(g.B_b) and P(g.B) allow expert knowledge to be introduced in the model, as explained in the main text.

Query-based biclustering of gene expression data using Probabilistic Relational Models

BMC Bioinformatics. 2011;12(Suppl 1):S37-S37.
Figure 6

Decomposition of pooling variance for Illumina 1M-Duo arrays. Stacked barplots showing the normalized pooling variance estimates, and the breakdown into array and to pool-construction variance for pools allelotyped on the Illumina 1M-Duo array. All estimates are derived from comparison of non-identical pools, Type C. The portion of pooling variance attributed to pool-construction is indicated by hatched bars, the portion of pooling variance attribute to the array is indicated by grey bars. Pool size is indicated above each stacked bar.

Estimates of array and pool-construction variance for planning efficient DNA-pooling genome wide association studies

BMC Med Genomics. 2011;4:81-81.
Figure 5

Decomposition of pooling variance for Illumina 660-Quad arrays. Stacked barplots showing the normalized pooling variance estimates, and the breakdown into array and to pool-construction variance for pools allelotyped on the Illumina 660-Quad array. All estimates are derived from comparison of non-identical pools, Type C. The portion of pooling variance attributed to pool-construction is indicated by hatched bars, the portion of pooling variance attribute to the array is indicated by grey bars. Pool size is indicated above each stacked bar.

Estimates of array and pool-construction variance for planning efficient DNA-pooling genome wide association studies

BMC Med Genomics. 2011;4:81-81.
Figure 1Using array data type to store microarray data

The top shows the schemas of the microarray databases: A) is the schema that uses array data type; B) is the schema that stores expression value per row (EVPR) C) A diagram to illustrate how microarray data are stored in the array data type schema. The left table is the table MICROARRAY, and the table EXPERIMENT is on the right. In the table MICROARRAY, two example probes from chip Y98 and one example probe from chip Y99 are shown. The attribute MICROARRAY_DATA is an array data type, with variable array length. The table EXPERIMENT shows the eight experiments done on Y98 and three experiments done on Y99. The attributes EXP_DESCRIPTION and EXP_DATE are not shown here. The arrows show the relationship between the two tables. For the probes from chip Y98, the first entries in MICROARRAY_DATA correspond to the experiment Wt-4-1; and for Y99 the third entries of MICROARRAY_DATA are the expression data from experiment Y99-exp-3.

A Method of Microarray Data Storage Using Array Data Type

Comput Biol Chem. ;31(2):143-147.