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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
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Screenshot of the Molgenis catalogue’s class hierarchy in Graph2VR. The ins... Open Access
Published: 20 May 2025
Figure 1.
Screenshot of the Molgenis catalogue’s class hierarchy in Graph2VR. The instances of the “subcohorts” are expanded.
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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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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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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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(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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(a) GraphDB layout after manually ordering the nodes. An overlay was added ... Open Access
Published: 20 May 2025
Figure 7.
(a) GraphDB layout after manually ordering the nodes. An overlay was added to distinguish which nodes belong to which dataset. The different colours indicate that the nodes have different rdf:types (e.g. light blue being a gene and dark blue a “Disease, Disorder or Finding”). However, not all data w
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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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Example of a large graph demonstrating the rendering capabilities of Graph2... Open Access
Published: 20 May 2025
Figure 10.
Example of a large graph demonstrating the rendering capabilities of Graph2VR on an RTX 4090. It shows about 17 000 nodes handled by Graph2VR. A framerate drop to 14 fps was observed during the layout phase (the fps increase significantly afterwards). Dataset: DBpedia.
