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The repository for DeepTMHMM contains the scripts and links to the underlying datasets used in the Nature Communications paper.
These files typically contain curated sequences of proteins that cross cell membranes, used to distinguish between transmembrane helices, signal peptides, and globular domains.
If you are looking for the contents of this specific archive for replication or research, they are usually hosted on: TmPri2-005.7z
This dataset is primarily used in bioinformatics for training and evaluating machine learning models related to . Associated Research Paper The core research paper associated with this dataset is:
Read on Nature Communications | Source Code & Data on GitHub Context of the File The repository for DeepTMHMM contains the scripts and
Authors: Jeppe Hallgren, Konstantinos D. Tsirigos, et al. Journal: Nature Communications (2022).
The "TmPri" (Transmembrane Primary) naming convention is standard for the benchmark sets used to develop , a leading deep learning tool for protein structure prediction. Associated Research Paper The core research paper associated
The primary research group's resource page .
The repository for DeepTMHMM contains the scripts and links to the underlying datasets used in the Nature Communications paper.
These files typically contain curated sequences of proteins that cross cell membranes, used to distinguish between transmembrane helices, signal peptides, and globular domains.
If you are looking for the contents of this specific archive for replication or research, they are usually hosted on:
This dataset is primarily used in bioinformatics for training and evaluating machine learning models related to . Associated Research Paper The core research paper associated with this dataset is:
Read on Nature Communications | Source Code & Data on GitHub Context of the File
Authors: Jeppe Hallgren, Konstantinos D. Tsirigos, et al. Journal: Nature Communications (2022).
The "TmPri" (Transmembrane Primary) naming convention is standard for the benchmark sets used to develop , a leading deep learning tool for protein structure prediction.
The primary research group's resource page .