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ghazikhanihamed/TooT-PLM-ionCT_DB

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The declared licence (mit) is classified as permissive.

Declared licence: mit, from the tagEvidence: README.md at 964984d
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Citation text from the card

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

[More Information Needed]

@misc{ghazikhani_exploiting_2023,
	title = {Exploiting protein language models for the precise classification of ion channels and ion transporters},
	copyright = {© 2023, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution 4.0 International), CC BY 4.0, as described at http://creativecommons.org/licenses/by/4.0/},
	url = {https://www.biorxiv.org/content/10.1101/2023.07.11.548644v1},
	doi = {10.1101/2023.07.11.548644},
	abstract = {This study presents TooT-PLM-ionCT, a composite framework consisting of three distinct systems, each with different architectures and trained on unique datasets. Each system within TooT-PLM-ionCT is dedicated to a specific task: segregating ion channels (ICs) and ion transporters (ITs) from other membrane proteins and differentiating ICs from ITs. These systems exploit the capabilities of six diverse Protein Language Models (PLMs) - ProtBERT, ProtBERT-BFD, ESM-1b, ESM-2 (650M parameters), and ESM-2 (15B parameters). As these proteins play a pivotal role in the regulation of ion movement across cellular membranes, they are integral to numerous biological processes and overall cellular vitality. To circumvent the costly and time-consuming nature of wet lab experiments, we harness the predictive prowess of PLMs, drawin

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dataset_licence({ "id": "ghazikhanihamed/TooT-PLM-ionCT_DB" })
signal: "permissive"
stated_licence: "mit"
sources: 0