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README.md
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<!-- Provide a quick summary of the dataset. -->
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## Dataset Details
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### Dataset Description
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- **Curated by:** [More Information Needed]
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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### Dataset Sources [optional]
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<!-- Provide the basic links for the dataset. -->
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- **Repository:**
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- **Paper [optional]:**
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- **Demo [optional]:**
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## Uses
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<!-- Address questions around how the dataset is intended to be used. -->
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### Direct Use
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<!-- This section describes suitable use cases for the dataset. -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
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[More Information Needed]
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## Dataset Structure
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<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
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[More Information Needed]
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## Dataset Creation
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### Curation Rationale
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<!-- Motivation for the creation of this dataset. -->
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[More Information Needed]
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### Source Data
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<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
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#### Data Collection and Processing
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<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
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[More Information Needed]
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#### Who are the source data producers?
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<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
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[More Information Needed]
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### Annotations [optional]
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<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
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#### Annotation process
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<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
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[More Information Needed]
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#### Who are the annotators?
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<!-- This section describes the people or systems who created the annotations. -->
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[More Information Needed]
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#### Personal and Sensitive Information
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<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
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## Citation [optional]
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[More Information Needed]
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[More Information Needed]
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## Dataset Card Contact
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[More Information Needed]
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<!-- Provide a quick summary of the dataset. -->
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These datasets has been used in TooT-PLM-ionCT tool which is 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.
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- **Curated by:** Hamed Ghazikhani
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### Dataset Sources [optional]
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- **Repository:** UniProt/SwissProt
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- **Paper [optional]:** https://www.biorxiv.org/content/10.1101/2023.07.11.548644v1.abstract
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- **Demo [optional]:** https://tootsuite.encs.concordia.ca/toot_plm_ionct
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## Citation [optional]
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[More Information Needed]
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@misc{ghazikhani_exploiting_2023,
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title = {Exploiting protein language models for the precise classification of ion channels and ion transporters},
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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/},
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url = {https://www.biorxiv.org/content/10.1101/2023.07.11.548644v1},
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doi = {10.1101/2023.07.11.548644},
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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, drawing parallels with techniques in natural language processing. Our strategy engages six classifiers, embracing both conventional methodologies and a deep learning model, for each of our defined tasks. Furthermore, we delve into critical factors influencing our tasks, including the implications of dataset balancing, the effect of frozen versus fine-tuned PLM representations, and the potential variance between half and full precision floating-point computations. Our empirical results showcase superior performance in distinguishing ITs from other membrane proteins and differentiating ICs from ITs, while the task of discriminating ICs from other membrane proteins exhibits results commensurate with the current state-of-the-art.},
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language = {en},
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urldate = {2023-07-31},
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publisher = {bioRxiv},
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author = {Ghazikhani, Hamed and Butler, Gregory},
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month = jul,
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year = {2023},
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note = {Pages: 2023.07.11.548644
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Section: New Results},
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file = {Full Text PDF:/Users/hamedghazikhani/Zotero/storage/NVPQKEMJ/Ghazikhani and Butler - 2023 - Exploiting protein language models for the precise.pdf:application/pdf},
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}
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