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Relevant bibliographies by topics / Biological Pathway Data

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Journal articles

Academic literature on the topic 'Biological Pathway Data'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 February 2022

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Journal articles on the topic "Biological Pathway Data"

1

Demir, Emek, Özgün Babur, Igor Rodchenkov, et al. "Using Biological Pathway Data with Paxtools." PLoS Computational Biology 9, no. 9 (2013): e1003194. http://dx.doi.org/10.1371/journal.pcbi.1003194.

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2

Cerami, E. G., B. E. Gross, E. Demir, et al. "Pathway Commons, a web resource for biological pathway data." Nucleic Acids Research 39, Database (2010): D685—D690. http://dx.doi.org/10.1093/nar/gkq1039.

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3

Nishida, Kozo, Keiichiro Ono, Shigehiko Kanaya, and Koichi Takahashi. "KEGGscape: a Cytoscape app for pathway data integration." F1000Research 3 (July 1, 2014): 144. http://dx.doi.org/10.12688/f1000research.4524.1.

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In this paper, we present KEGGscape a pathway data integration and visualization app for Cytoscape (http://apps.cytoscape.org/apps/keggscape). KEGG is a comprehensive public biological database that contains large collection of human curated pathways. KEGGscape utilizes the database to reproduce the corresponding hand-drawn pathway diagrams with as much detail as possible in Cytoscape. Further, it allows users to import pathway data sets to visualize biologist-friendly diagrams using the Cytoscape core visualization function (Visual Style) and the ability to perform pathway analysis with a var

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4

Wu, Guanming, Eric Dawson, Adrian Duong, Robin Haw, and Lincoln Stein. "ReactomeFIViz: a Cytoscape app for pathway and network-based data analysis." F1000Research 3 (September 12, 2014): 146. http://dx.doi.org/10.12688/f1000research.4431.2.

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High-throughput experiments are routinely performed in modern biological studies. However, extracting meaningful results from massive experimental data sets is a challenging task for biologists. Projecting data onto pathway and network contexts is a powerful way to unravel patterns embedded in seemingly scattered large data sets and assist knowledge discovery related to cancer and other complex diseases. We have developed a Cytoscape app called “ReactomeFIViz”, which utilizes a highly reliable gene functional interaction network combined with human curated pathways derived from Reactome and ot

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5

Wu, Guanming, Eric Dawson, Adrian Duong, Robin Haw, and Lincoln Stein. "ReactomeFIViz: the Reactome FI Cytoscape app for pathway and network-based data analysis." F1000Research 3 (July 1, 2014): 146. http://dx.doi.org/10.12688/f1000research.4431.1.

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High-throughput experiments are routinely performed in modern biological studies. However, extracting meaningful results from massive experimental data sets is a challenging task for biologists. Projecting data onto pathway and network contexts is a powerful way to unravel patterns embedded in seemingly scattered large data sets and assist knowledge discovery related to cancer and other complex diseases. We have developed a Cytoscape app called “ReactomeFIViz”, which utilizes a highly reliable gene functional interaction network and human curated pathways from Reactome and other pathway databa

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6

Chung, Tae-Su, Ji-Hun Kim, Kee-Won Kim, and Ju-Han Kim. "Biological Pathway Extension Using Microarray Gene Expression Data." Genomics & Informatics 6, no. 4 (2008): 202–9. http://dx.doi.org/10.5808/gi.2008.6.4.202.

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7

Young, Michael R., and David L. Craft. "Pathway-Informed Classification System (PICS) for Cancer Analysis Using Gene Expression Data." Cancer Informatics 15 (January 2016): CIN.S40088. http://dx.doi.org/10.4137/cin.s40088.

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We introduce Pathway-Informed Classification System (PICS) for classifying cancers based on tumor sample gene expression levels. PICS is a computational method capable of expeditiously elucidating both known and novel biological pathway involvement specific to various cancers and uses that learned pathway information to separate patients into distinct classes. The method clearly separates a pan-cancer dataset by tissue of origin and also sub-classifies individual cancer datasets into distinct survival classes. Gene expression values are collapsed into pathway scores that reveal which biologica

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8

Cai, Binghuang, and Xia Jiang. "Revealing Biological Pathways Implicated in Lung Cancer from TCGA Gene Expression Data Using Gene Set Enrichment Analysis." Cancer Informatics 13s1 (January 2014): CIN.S13882. http://dx.doi.org/10.4137/cin.s13882.

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Analyzing biological system abnormalities in cancer patients based on measures of biological entities, such as gene expression levels, is an important and challenging problem. This paper applies existing methods, Gene Set Enrichment Analysis and Signaling Pathway Impact Analysis, to pathway abnormality analysis in lung cancer using microarray gene expression data. Gene expression data from studies of Lung Squamous Cell Carcinoma (LUSC) in The Cancer Genome Atlas project, and pathway gene set data from the Kyoto Encyclopedia of Genes and Genomes were used to analyze the relationship between pat

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9

Saraiya, Purvi, Chris North, and Karen Duca. "Visualizing Biological Pathways: Requirements Analysis, Systems Evaluation and Research Agenda." Information Visualization 4, no. 3 (2005): 191–205. http://dx.doi.org/10.1057/palgrave.ivs.9500102.

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Pathway diagrams are used by life scientists to represent complex interactions at the molecular level in living cells. The recent shift towards data-intensive bioinformatics and systems-level science has created a strong need for advanced pathway visualizations that support exploratory analysis. This paper presents a comprehensive list of requirements for pathway visualization systems, based on interviews conducted to understand life scientists' needs for pathway analysis. A variety of existing pathway visualization systems are examined, to analyze common approaches by which the contemporary s

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10

Yousef, Malik, Ege Ülgen, and Osman Uğur Sezerman. "CogNet: classification of gene expression data based on ranked active-subnetwork-oriented KEGG pathway enrichment analysis." PeerJ Computer Science 7 (February 22, 2021): e336. http://dx.doi.org/10.7717/peerj-cs.336.

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Most of the traditional gene selection approaches are borrowed from other fields such as statistics and computer science, However, they do not prioritize biologically relevant genes since the ultimate goal is to determine features that optimize model performance metrics not to build a biologically meaningful model. Therefore, there is an imminent need for new computational tools that integrate the biological knowledge about the data in the process of gene selection and machine learning. Integrative gene selection enables incorporation of biological domain knowledge from external biological res

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