Demo Data from EDBT 2026 / reviewers in the wild / expert
James M. Melott
dblp:174/6659
· DBLP profile ↗
3ranked-venue papers
1as first author
1since 2021 · last 2022
0000-0001-9052-2328ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 3 · 1 first-author · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
3 papers |
Bioinformatics and computational biology · 100% |
Topics — the 7 heaviest of 8, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology
proteomics |
0.6 | 1 | 2022 | RPPA SPACE: an R package for normalization and quantitation of Reverse-Phase Protein Array data · Bioinform. 2022 |
Bioinformatics and computational biology
bioinformatics workflow |
0.3 | 1 | 2018 | SoS Notebook: an interactive multi-language data analysis environment · Bioinform. 2018 |
Bioinformatics and computational biology › knowledge representation in biology › biomedical ontology
gene ontology |
0.2 | 1 | 2016 | PathwaysWeb: a gene pathways API with directional interactions, expanded gene ontology, and versioning · Bioinform. 2016 |
Bioinformatics and computational biology › biological database
pathway database |
0.2 | 1 | 2016 | PathwaysWeb: a gene pathways API with directional interactions, expanded gene ontology, and versioning · Bioinform. 2016 |
Bioinformatics and computational biology › systems bioinformatics › pathway analysis
pathway data integration |
0.2 | 1 | 2016 | PathwaysWeb: a gene pathways API with directional interactions, expanded gene ontology, and versioning · Bioinform. 2016 |
Bioinformatics and computational biology › drug discovery
high-throughput screening |
0.2 | 1 | 2022 | RPPA SPACE: an R package for normalization and quantitation of Reverse-Phase Protein Array data · Bioinform. 2022 |
Bioinformatics and computational biology › proteomics
protein quantification |
0.2 | 1 | 2022 | RPPA SPACE: an R package for normalization and quantitation of Reverse-Phase Protein Array data · Bioinform. 2022 |
Methods — techniques the papers use, named apart from their topics
literate programming · 0.3web API · 0.2data integration · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | RPPA SPACE: an R package for normalization and quantitation of Reverse-Phase Protein Array dataabstractSUMMARY: Reverse-Phase Protein Array (RPPA) is a robust high-throughput, cost-effective platform for quantitatively measuring proteins in biological specimens. However, converting raw RPPA data into normalized, analysis-ready data remains a challenging task. Here, we present the RPPA SPACE (RPPA Superposition Analysis and Concentration Evaluation) R package, a substantially improved successor to SuperCurve, to meet that challenge. SuperCurve has been used to normalize over 170 000 samples to date. RPPA SPACE allows exclusion of poor-quality samples from the normalization process to improve the quality of the remaining samples. It also features a novel quality-control metric, 'noise', that estimates the level of random errors present in each RPPA slide. The noise metric can help to determine the quality and reliability of the data. In addition, RPPA SPACE has simpler input requirements and is more flexible than SuperCurve, it is much faster with greatly improved error reporting. AVAILABILITY AND IMPLEMENTATION: The standalone RPPA SPACE R package, tutorials and sample data are available via https://rppa.space/, CRAN (https://cran.r-project.org/web/packages/RPPASPACE/index.html) and GitHub (https://github.com/MD-Anderson-Bioinformatics/RPPASPACE). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Huma Shehwana, Shwetha V. Kumar, James M. Melott, Mary A. Rohrdanz, Chris Wakefield, Zhenlin Ju, Doris R. Siwak, Yiling Lu, Bradley M. Broom, John N. Weinstein, Gordon B. Mills, Rehan Akbani |
Bioinform. | 3 |
| 2018 | SoS Notebook: an interactive multi-language data analysis environmentabstractMotivation: Complex bioinformatic data analysis workflows involving multiple scripts in different languages can be difficult to consolidate, share and reproduce. An environment that streamlines the entire processes of data collection, analysis, visualization and reporting of such multi-language analyses is currently lacking. Results: We developed Script of Scripts (SoS) Notebook, a web-based notebook environment that allows the use of multiple scripting language in a single notebook, with data flowing freely within and across languages. SoS Notebook enables researchers to perform sophisticated bioinformatic analysis using the most suitable tools for different parts of the workflow, without the limitations of a particular language or complications of cross-language communications. Availability and implementation: SoS Notebook is hosted at http://vatlab.github.io/SoS/ and is distributed under a BSD license. Bo Peng 0001, Man Chong Leong, Chris Wakefield, James M. Melott, Yulun Chiu, Di Du, John N. Weinstein |
Bioinform. | 6 |
| 2016 | PathwaysWeb: a gene pathways API with directional interactions, expanded gene ontology, and versioningabstractUNLABELLED: PathwaysWeb is a resource-based, well-documented web system that provides publicly available information on genes, biological pathways, Gene Ontology (GO) terms, gene-gene interaction networks (importantly, with the directionality of interactions) and links to key-related PubMed documents. The PathwaysWeb API simplifies the construction of applications that need to retrieve and interrelate information across multiple, pathway-related data types from a variety of original data sources. PathwaysBrowser is a companion website that enables users to explore the same integrated pathway data. The PathwaysWeb system facilitates reproducible analyses by providing access to all versions of the integrated datasets. Although its GO subsystem includes data for mouse, PathwaysWeb currently focuses on human data. However, pathways for mouse and many other species can be inferred with a high success rate from human pathways. AVAILABILITY AND IMPLEMENTATION: PathwaysWeb can be accessed via the Internet at http://bioinformatics.mdanderson.org/main/PathwaysWeb:Overview. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. James M. Melott, John N. Weinstein, Bradley M. Broom |
Bioinform. | 1 |