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Published: 30 May 2025
Figure 2. Research methodology.
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Published: 30 May 2025
Figure 1. Artificial Intelligence Driven Predictive Framework for Analyzing Protein Sequences Across Diverse Bioinformatics Tasks.
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Published: 30 May 2025
Figure 3. Precise classification of unique protein sequence analysis tasks in 11 major biological goals.
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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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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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Published: 30 May 2025
Figure 8. Distribution of publishers involved in the publication of protein sequence analysis literature.
Journal Article
Muhammad Nabeel Asim and others
Database, Volume 2025, 2025, baaf027, https://doi.org/10.1093/database/baaf027
Published: 30 May 2025
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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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Published: 30 May 2025
Figure 7. Illustration of confusion matrix.
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Published: 22 May 2025
Figure 1. Model architecture.
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Published: 22 May 2025
Figure 2. Workflow of our approach.
Journal Article
Wilailack Meesawad and others
Database, Volume 2025, 2025, baae127, https://doi.org/10.1093/database/baae127
Published: 22 May 2025
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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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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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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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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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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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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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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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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.