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Artificial Intelligence Driven Predictive Framework for Analyzing Protein S... Open Access
Published: 30 May 2025
Figure 1.
Artificial Intelligence Driven Predictive Framework for Analyzing Protein Sequences Across Diverse Bioinformatics Tasks.
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Precise classification of unique protein sequence analysis tasks in 11 majo... Open Access
Published: 30 May 2025
Figure 3.
Precise classification of unique protein sequence analysis tasks in 11 major biological goals.
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A comprehensive methodical categorization of protein sequence analysis task... Open Access
Published: 30 May 2025
Figure 4.
A comprehensive methodical categorization of protein sequence analysis tasks into regression, binary classification, multi-class classification, multi-label classification, and clustering.
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Utilization of 15 different LLMs in diverse protein sequence analysis pipel... Open Access
Published: 30 May 2025
Figure 6.
Utilization of 15 different LLMs in diverse protein sequence analysis pipelines based on a variety of machine and deep learning algorithms
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Distribution of publishers involved in the publication of protein sequence ... Open Access
Published: 30 May 2025
Figure 8.
Distribution of publishers involved in the publication of protein sequence analysis literature.
Journal Article
Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models Open Access
Database, Volume 2025, 2025, baaf027, https://doi.org/10.1093/database/baaf027
Published: 30 May 2025
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Utilization of 22 different word embedding methods in diverse protein seque... Open Access
Published: 30 May 2025
Figure 5.
Utilization of 22 different word embedding methods in diverse protein sequence analysis pipelines based on a variety of machine and deep learning predictors
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Illustration of confusion matrix. Open Access
Published: 30 May 2025
Figure 7.
Illustration of confusion matrix.
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Workflow of our approach. Open Access
Published: 22 May 2025
Figure 2.
Workflow of our approach.
Journal Article
Enhancing biomedical relation extraction through data-centric and preprocessing-robust ensemble learning approach Open Access
Database, Volume 2025, 2025, baae127, https://doi.org/10.1093/database/baae127
Published: 22 May 2025
Journal Article
An exploratory study combining Virtual Reality and Semantic Web for life science research using Graph2VR Open Access
Database, Volume 2025, 2025, baaf008, https://doi.org/10.1093/database/baaf008
Published: 20 May 2025
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This figure provides a schematic overview of the structure and interconnect... Open Access
Published: 20 May 2025
Figure 4.
This figure provides a schematic overview of the structure and interconnections within the RDF data (turtle files) generated from VIP VCF files including potential references to external sources. Each box represents a node. Each line represents a predicate. The arrow points towards the object of a t
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(a) Graph2VR using automatic layouts (first Class hierarchy layout, then 3D... Open Access
Published: 20 May 2025
Figure 5.
(a) Graph2VR using automatic layouts (first Class hierarchy layout, then 3D Force directed). Even though the graphs in the screenshot look very small, they are only spread out, and the user can easily navigate closer in VR to interact with them. (b) GraphDB manual layout where the red nodes represen
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The graph resulting from the query is based on all three graphs. It connect... Open Access
Published: 20 May 2025
Figure 8.
The graph resulting from the query is based on all three graphs. It connects the entries about the ATC codes of aspirin from Drugbank with the ATC code ontology, connects the Drugbank entry for aspirin to PubChem, and displays its CAS number(s).
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The graph from the previous query about Aspirin. It has been expanded using... Open Access
Published: 20 May 2025
Figure 9.
The graph from the previous query about Aspirin. It has been expanded using data from Drugbank and stretched manually. At left are the CAS numbers from PubChem, in the middle the expanded Drugbank entry, and on the right the ATC code from the third database, demonstrating that this can be used as a
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An example figure showing the selection of the topic “tobacco,” followed by... Open Access
Published: 20 May 2025
Figure 2.
An example figure showing the selection of the topic “tobacco,” followed by navigation to the “DNBC” cohort. The users then specified a query pattern and saw that the PSYCONN cohort was also linked to the same topic in this example set (the original catalogue holds many more records). (The labels we
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How the user made a query pattern for the variable “blood pressure” and the... Open Access
Published: 20 May 2025
Figure 3.
How the user made a query pattern for the variable “blood pressure” and then found all variable-mappings that fulfil this condition. There are three cohorts (G21, ENVIRONAGE, and GENR) in this sample dataset that match, as seen in the middle diamond. This example data was taken from the LifeCycle pr
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(a) Graph2VR layout. (b): GraphDB default layout. Red nodes represent patie... Open Access
Published: 20 May 2025
Figure 6.
(a) Graph2VR layout. (b): GraphDB default layout. Red nodes represent patients. Blue nodes represent phenotypes. Clustering of individuals with the same phenotype (minimum of two per HPO) in Graph2VR 3D force-directed layout (a) and in GraphDB automatic layout (b).
