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Journal Article
An Phan and others
Database, Volume 2025, 2025, baaf036, https://doi.org/10.1093/database/baaf036
Published: 07 May 2025
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Published: 07 May 2025
Figure 2. (a) Barplot displaying average Gini coefficient (0–1) from 2013 to 2022. Each bar indicates one of three knowledge metrics of human proteins: term count (dark green), unique term count (light green), and information content (black) in one of three GO aspects. Error bars extend one standard deviation
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Published: 07 May 2025
Figure 3. Gini coefficient (0–1) of the number of FPEs of human proteins.
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Published: 07 May 2025
Figure 4. Information content and number of FPEs (in log scale) of proteins in 2022. Each black dot represents the number of FPEs and the information content per protein within an aspect (MFO, BPO, CCO) in 2022. Highlighted genes (coloured dots) have one data point for each year from 2013 to 2022. Highlighted
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Published: 07 May 2025
Figure 5. Boxplots showing Spearman’s coefficient between the number of FPEs and the gain in information content (in MFO and BPO) of disease-associated proteins. Triangles (with disease names labelled) indicate diseases with a significant Spearman correlation, and circles indicate diseases without significant
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Published: 07 May 2025
Figure 2. The figure shows the diversity and distribution of anticancer peptides.
Journal Article
Milind Chauhan and others
Database, Volume 2025, 2025, baaf030, https://doi.org/10.1093/database/baaf030
Published: 07 May 2025
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Published: 07 May 2025
Figure 1. Information content of proteins studied experimentally from 2013 to 2022 in (a) articles describing high-throughput (>100 proteins per article; blue boxes) vs. low-throughput experiments (≤100 proteins per article; orange boxes) and (b) all articles. Each box shows the distribution of information
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Published: 07 May 2025
Figure 6. (a) Barplot of the number of years between annotation and publication for experimental GO annotations of human proteins. The vertical line shows the median of this distribution at 4 years. (b) Barplot of the distribution of publication year and annotation year for GO experimental annotations of huma
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Published: 07 May 2025
Figure 1. The figure shows the complete architecture of the database.
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Published: 02 May 2025
Figure 2. Schematic diagram of the data processing and database structure of STCDB4ND in this study.
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Published: 02 May 2025
Figure 4. The whole map of the the network of neurological diseases related cell signaling transduction pathways, and figures of heatmap generated from disease data collected from DiSignAtlas website and the PPI network of TUBA4A from NDAtlas website.
Journal Article
Boyan Gong and others
Database, Volume 2025, 2025, baaf032, https://doi.org/10.1093/database/baaf032
Published: 02 May 2025
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Published: 02 May 2025
Figure 1. The flowchart of the key factor recognition algorithm, in which M is the transition matrix, R refers to the vector of node-ranks, d is damping factor, and ε is the iteration accuracy.
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Published: 02 May 2025
Figure 3. Usage pages in STCDB.
Journal Article
Annarita Marrano and others
Database, Volume 2025, 2025, baaf034, https://doi.org/10.1093/database/baaf034
Published: 25 April 2025
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Published: 25 April 2025
Figure 2. Database (DB) stakeholders, their roles in data management, and how the educational curriculum will positively impact them.
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Published: 25 April 2025
Figure 1. How biological databases (DBs) implement the FAIR principles and promote FAIR data.
Journal Article
Gabrielle Rigutto and others
Database, Volume 2025, 2025, baaf026, https://doi.org/10.1093/database/baaf026
Published: 22 April 2025
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Published: 22 April 2025
Figure 5. Physiological disorder image database system. (a) Main screen interface of crop physiological disorder image management system (b) Crop stress and crop information included in the informational list category (c) Project creation list. (d) The database can be aligned in a list form of images taken by