cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.6633452.v1
title: C. elegans predictions

description: About this dataset: These are the most reliable 200,000 PPI predictions for C. elegans. It is a CSV file.<br>Motivation: Protein-protein interactions (PPIs) play a key role in many cellular processes. Most annotations of PPIs mix experimental and computational data. The mix optimizes coverage, but obfuscates the annotation origin. Some resources excel at focusing on reliable experimental data. Here, we focused on new pairs of interacting proteins for several model organisms based solely on sequence-based prediction methods. <br>Results: We extracted reliable experimental data about which proteins interact (binary) for eight diverse model organisms from public databases, namely from Escherichia coli, Schizosaccharomyces pombe, Plasmodium falciparum, Drosophila melanogaster, Caenorhabditis elegans, Mus musculus, Rattus norvegicus, Arabidopsis thaliana, and for the previously used Homo sapiens and Saccharomyces cerevisiae. Those data were the base to develop a PPI prediction method for each model organism. The method used evolutionary information through a profile-kernel Support Vector Machine (SVM). With the resulting eight models, we predicted all possible protein pairs in each organism and made the top predictions available through a web application. Almost all of the PPIs made available were predicted between proteins that have not been observed in any interaction, in particular for less well-studied organisms. Thus, our work complements existing resources and is particularly helpful for designing experiments because of its uniqueness. Experimental annotations and computational predictions are strongly influenced by the fact that some proteins have many partners and others few. To optimize machine learning, the newly methods explicitly ignored such a network-structure. This might be another strength of our approach. The database interface representing our results is accessible from https://rostlab.org/services/ppipair/.<br>Please cite us when you are using this data:<pre>@article{tran2018profppidb, title={ProfPPIdb: pairs of physical protein-protein interactions predicted for entire proteomes}, author={Tran, Linh and Hamp, Tobias and Rost, Burkhard}, journal={bioRxiv}, pages={332510}, year={2018}, publisher={Cold Spring Harbor Laboratory} }</pre>
DOI: 10.6084/m9.figshare.6633431
title: M. musculus predictions

description: About this dataset: These are the most reliable 200,000 PPI predictions for M. musculus. It is a CSV file.<br>Motivation: Protein-protein interactions (PPIs) play a key role in many cellular processes. Most annotations of PPIs mix experimental and computational data. The mix optimizes coverage, but obfuscates the annotation origin. Some resources excel at focusing on reliable experimental data. Here, we focused on new pairs of interacting proteins for several model organisms based solely on sequence-based prediction methods. <br>Results: We extracted reliable experimental data about which proteins interact (binary) for eight diverse model organisms from public databases, namely from Escherichia coli, Schizosaccharomyces pombe, Plasmodium falciparum, Drosophila melanogaster, Caenorhabditis elegans, Mus musculus, Rattus norvegicus, Arabidopsis thaliana, and for the previously used Homo sapiens and Saccharomyces cerevisiae. Those data were the base to develop a PPI prediction method for each model organism. The method used evolutionary information through a profile-kernel Support Vector Machine (SVM). With the resulting eight models, we predicted all possible protein pairs in each organism and made the top predictions available through a web application. Almost all of the PPIs made available were predicted between proteins that have not been observed in any interaction, in particular for less well-studied organisms. Thus, our work complements existing resources and is particularly helpful for designing experiments because of its uniqueness. Experimental annotations and computational predictions are strongly influenced by the fact that some proteins have many partners and others few. To optimize machine learning, the newly methods explicitly ignored such a network-structure. This might be another strength of our approach. The database interface representing our results is accessible from https://rostlab.org/services/ppipair/.<br>Please cite us when you are using this data:<pre>@article{tran2018profppidb, title={ProfPPIdb: pairs of physical protein-protein interactions predicted for entire proteomes}, author={Tran, Linh and Hamp, Tobias and Rost, Burkhard}, journal={bioRxiv}, pages={332510}, year={2018}, publisher={Cold Spring Harbor Laboratory} }</pre>
DOI: 10.6084/m9.figshare.6633431.v1
title: M. musculus predictions

description: About this dataset: These are the most reliable 200,000 PPI predictions for M. musculus. It is a CSV file.<br>Motivation: Protein-protein interactions (PPIs) play a key role in many cellular processes. Most annotations of PPIs mix experimental and computational data. The mix optimizes coverage, but obfuscates the annotation origin. Some resources excel at focusing on reliable experimental data. Here, we focused on new pairs of interacting proteins for several model organisms based solely on sequence-based prediction methods. <br>Results: We extracted reliable experimental data about which proteins interact (binary) for eight diverse model organisms from public databases, namely from Escherichia coli, Schizosaccharomyces pombe, Plasmodium falciparum, Drosophila melanogaster, Caenorhabditis elegans, Mus musculus, Rattus norvegicus, Arabidopsis thaliana, and for the previously used Homo sapiens and Saccharomyces cerevisiae. Those data were the base to develop a PPI prediction method for each model organism. The method used evolutionary information through a profile-kernel Support Vector Machine (SVM). With the resulting eight models, we predicted all possible protein pairs in each organism and made the top predictions available through a web application. Almost all of the PPIs made available were predicted between proteins that have not been observed in any interaction, in particular for less well-studied organisms. Thus, our work complements existing resources and is particularly helpful for designing experiments because of its uniqueness. Experimental annotations and computational predictions are strongly influenced by the fact that some proteins have many partners and others few. To optimize machine learning, the newly methods explicitly ignored such a network-structure. This might be another strength of our approach. The database interface representing our results is accessible from https://rostlab.org/services/ppipair/.<br>Please cite us when you are using this data:<pre>@article{tran2018profppidb, title={ProfPPIdb: pairs of physical protein-protein interactions predicted for entire proteomes}, author={Tran, Linh and Hamp, Tobias and Rost, Burkhard}, journal={bioRxiv}, pages={332510}, year={2018}, publisher={Cold Spring Harbor Laboratory} }</pre>
DOI: 10.6084/m9.figshare.6633413
title: P. falciparum predictions

description: About this dataset: These are the most reliable 50,000 PPI predictions for P. falciparum. It is a CSV file.<br>Motivation: Protein-protein interactions (PPIs) play a key role in many cellular processes. Most annotations of PPIs mix experimental and computational data. The mix optimizes coverage, but obfuscates the annotation origin. Some resources excel at focusing on reliable experimental data. Here, we focused on new pairs of interacting proteins for several model organisms based solely on sequence-based prediction methods. <br>Results: We extracted reliable experimental data about which proteins interact (binary) for eight diverse model organisms from public databases, namely from Escherichia coli, Schizosaccharomyces pombe, Plasmodium falciparum, Drosophila melanogaster, Caenorhabditis elegans, Mus musculus, Rattus norvegicus, Arabidopsis thaliana, and for the previously used Homo sapiens and Saccharomyces cerevisiae. Those data were the base to develop a PPI prediction method for each model organism. The method used evolutionary information through a profile-kernel Support Vector Machine (SVM). With the resulting eight models, we predicted all possible protein pairs in each organism and made the top predictions available through a web application. Almost all of the PPIs made available were predicted between proteins that have not been observed in any interaction, in particular for less well-studied organisms. Thus, our work complements existing resources and is particularly helpful for designing experiments because of its uniqueness. Experimental annotations and computational predictions are strongly influenced by the fact that some proteins have many partners and others few. To optimize machine learning, the newly methods explicitly ignored such a network-structure. This might be another strength of our approach. The database interface representing our results is accessible from https://rostlab.org/services/ppipair/.<br>Please cite us when you are using this data:<pre>@article{tran2018profppidb, title={ProfPPIdb: pairs of physical protein-protein interactions predicted for entire proteomes}, author={Tran, Linh and Hamp, Tobias and Rost, Burkhard}, journal={bioRxiv}, pages={332510}, year={2018}, publisher={Cold Spring Harbor Laboratory} }</pre>
DOI: 10.6084/m9.figshare.6633413.v1
title: P. falciparum predictions

description: About this dataset: These are the most reliable 50,000 PPI predictions for P. falciparum. It is a CSV file.<br>Motivation: Protein-protein interactions (PPIs) play a key role in many cellular processes. Most annotations of PPIs mix experimental and computational data. The mix optimizes coverage, but obfuscates the annotation origin. Some resources excel at focusing on reliable experimental data. Here, we focused on new pairs of interacting proteins for several model organisms based solely on sequence-based prediction methods. <br>Results: We extracted reliable experimental data about which proteins interact (binary) for eight diverse model organisms from public databases, namely from Escherichia coli, Schizosaccharomyces pombe, Plasmodium falciparum, Drosophila melanogaster, Caenorhabditis elegans, Mus musculus, Rattus norvegicus, Arabidopsis thaliana, and for the previously used Homo sapiens and Saccharomyces cerevisiae. Those data were the base to develop a PPI prediction method for each model organism. The method used evolutionary information through a profile-kernel Support Vector Machine (SVM). With the resulting eight models, we predicted all possible protein pairs in each organism and made the top predictions available through a web application. Almost all of the PPIs made available were predicted between proteins that have not been observed in any interaction, in particular for less well-studied organisms. Thus, our work complements existing resources and is particularly helpful for designing experiments because of its uniqueness. Experimental annotations and computational predictions are strongly influenced by the fact that some proteins have many partners and others few. To optimize machine learning, the newly methods explicitly ignored such a network-structure. This might be another strength of our approach. The database interface representing our results is accessible from https://rostlab.org/services/ppipair/.<br>Please cite us when you are using this data:<pre>@article{tran2018profppidb, title={ProfPPIdb: pairs of physical protein-protein interactions predicted for entire proteomes}, author={Tran, Linh and Hamp, Tobias and Rost, Burkhard}, journal={bioRxiv}, pages={332510}, year={2018}, publisher={Cold Spring Harbor Laboratory} }</pre>
DOI: 10.6084/m9.figshare.6633368
title: R. norvegicus predictions

description: About this dataset: These are the most reliable 200,000 PPI predictions for R. norvegicus. It is a CSV file.<br>Motivation: Protein-protein interactions (PPIs) play a key role in many cellular processes. Most annotations of PPIs mix experimental and computational data. The mix optimizes coverage, but obfuscates the annotation origin. Some resources excel at focusing on reliable experimental data. Here, we focused on new pairs of interacting proteins for several model organisms based solely on sequence-based prediction methods. <br>Results: We extracted reliable experimental data about which proteins interact (binary) for eight diverse model organisms from public databases, namely from Escherichia coli, Schizosaccharomyces pombe, Plasmodium falciparum, Drosophila melanogaster, Caenorhabditis elegans, Mus musculus, Rattus norvegicus, Arabidopsis thaliana, and for the previously used Homo sapiens and Saccharomyces cerevisiae. Those data were the base to develop a PPI prediction method for each model organism. The method used evolutionary information through a profile-kernel Support Vector Machine (SVM). With the resulting eight models, we predicted all possible protein pairs in each organism and made the top predictions available through a web application. Almost all of the PPIs made available were predicted between proteins that have not been observed in any interaction, in particular for less well-studied organisms. Thus, our work complements existing resources and is particularly helpful for designing experiments because of its uniqueness. Experimental annotations and computational predictions are strongly influenced by the fact that some proteins have many partners and others few. To optimize machine learning, the newly methods explicitly ignored such a network-structure. This might be another strength of our approach. The database interface representing our results is accessible from https://rostlab.org/services/ppipair/.<br>Please cite us when you are using this data:<pre>@article{tran2018profppidb, title={ProfPPIdb: pairs of physical protein-protein interactions predicted for entire proteomes}, author={Tran, Linh and Hamp, Tobias and Rost, Burkhard}, journal={bioRxiv}, pages={332510}, year={2018}, publisher={Cold Spring Harbor Laboratory} }</pre>
DOI: 10.6084/m9.figshare.6633368.v1
title: R. norvegicus predictions

description: About this dataset: These are the most reliable 200,000 PPI predictions for R. norvegicus. It is a CSV file.<br>Motivation: Protein-protein interactions (PPIs) play a key role in many cellular processes. Most annotations of PPIs mix experimental and computational data. The mix optimizes coverage, but obfuscates the annotation origin. Some resources excel at focusing on reliable experimental data. Here, we focused on new pairs of interacting proteins for several model organisms based solely on sequence-based prediction methods. <br>Results: We extracted reliable experimental data about which proteins interact (binary) for eight diverse model organisms from public databases, namely from Escherichia coli, Schizosaccharomyces pombe, Plasmodium falciparum, Drosophila melanogaster, Caenorhabditis elegans, Mus musculus, Rattus norvegicus, Arabidopsis thaliana, and for the previously used Homo sapiens and Saccharomyces cerevisiae. Those data were the base to develop a PPI prediction method for each model organism. The method used evolutionary information through a profile-kernel Support Vector Machine (SVM). With the resulting eight models, we predicted all possible protein pairs in each organism and made the top predictions available through a web application. Almost all of the PPIs made available were predicted between proteins that have not been observed in any interaction, in particular for less well-studied organisms. Thus, our work complements existing resources and is particularly helpful for designing experiments because of its uniqueness. Experimental annotations and computational predictions are strongly influenced by the fact that some proteins have many partners and others few. To optimize machine learning, the newly methods explicitly ignored such a network-structure. This might be another strength of our approach. The database interface representing our results is accessible from https://rostlab.org/services/ppipair/.<br>Please cite us when you are using this data:<pre>@article{tran2018profppidb, title={ProfPPIdb: pairs of physical protein-protein interactions predicted for entire proteomes}, author={Tran, Linh and Hamp, Tobias and Rost, Burkhard}, journal={bioRxiv}, pages={332510}, year={2018}, publisher={Cold Spring Harbor Laboratory} }</pre>
DOI: 10.6084/m9.figshare.6633353
title: S. Pombe predictions

description: About this dataset: These are the most reliable 50,000 PPI predictions for S. pome. It is a CSV file.<br>Motivation: Protein-protein interactions (PPIs) play a key role in many cellular processes. Most annotations of PPIs mix experimental and computational data. The mix optimizes coverage, but obfuscates the annotation origin. Some resources excel at focusing on reliable experimental data. Here, we focused on new pairs of interacting proteins for several model organisms based solely on sequence-based prediction methods. <br>Results: We extracted reliable experimental data about which proteins interact (binary) for eight diverse model organisms from public databases, namely from Escherichia coli, Schizosaccharomyces pombe, Plasmodium falciparum, Drosophila melanogaster, Caenorhabditis elegans, Mus musculus, Rattus norvegicus, Arabidopsis thaliana, and for the previously used Homo sapiens and Saccharomyces cerevisiae. Those data were the base to develop a PPI prediction method for each model organism. The method used evolutionary information through a profile-kernel Support Vector Machine (SVM). With the resulting eight models, we predicted all possible protein pairs in each organism and made the top predictions available through a web application. Almost all of the PPIs made available were predicted between proteins that have not been observed in any interaction, in particular for less well-studied organisms. Thus, our work complements existing resources and is particularly helpful for designing experiments because of its uniqueness. Experimental annotations and computational predictions are strongly influenced by the fact that some proteins have many partners and others few. To optimize machine learning, the newly methods explicitly ignored such a network-structure. This might be another strength of our approach. The database interface representing our results is accessible from https://rostlab.org/services/ppipair/.<br>Please cite us when you are using this data:<pre>@article{tran2018profppidb, title={ProfPPIdb: pairs of physical protein-protein interactions predicted for entire proteomes}, author={Tran, Linh and Hamp, Tobias and Rost, Burkhard}, journal={bioRxiv}, pages={332510}, year={2018}, publisher={Cold Spring Harbor Laboratory} }</pre>
DOI: 10.6084/m9.figshare.6633353.v1
title: S. Pombe predictions

description: About this dataset: These are the most reliable 50,000 PPI predictions for S. pome. It is a CSV file.<br>Motivation: Protein-protein interactions (PPIs) play a key role in many cellular processes. Most annotations of PPIs mix experimental and computational data. The mix optimizes coverage, but obfuscates the annotation origin. Some resources excel at focusing on reliable experimental data. Here, we focused on new pairs of interacting proteins for several model organisms based solely on sequence-based prediction methods. <br>Results: We extracted reliable experimental data about which proteins interact (binary) for eight diverse model organisms from public databases, namely from Escherichia coli, Schizosaccharomyces pombe, Plasmodium falciparum, Drosophila melanogaster, Caenorhabditis elegans, Mus musculus, Rattus norvegicus, Arabidopsis thaliana, and for the previously used Homo sapiens and Saccharomyces cerevisiae. Those data were the base to develop a PPI prediction method for each model organism. The method used evolutionary information through a profile-kernel Support Vector Machine (SVM). With the resulting eight models, we predicted all possible protein pairs in each organism and made the top predictions available through a web application. Almost all of the PPIs made available were predicted between proteins that have not been observed in any interaction, in particular for less well-studied organisms. Thus, our work complements existing resources and is particularly helpful for designing experiments because of its uniqueness. Experimental annotations and computational predictions are strongly influenced by the fact that some proteins have many partners and others few. To optimize machine learning, the newly methods explicitly ignored such a network-structure. This might be another strength of our approach. The database interface representing our results is accessible from https://rostlab.org/services/ppipair/.<br>Please cite us when you are using this data:<pre>@article{tran2018profppidb, title={ProfPPIdb: pairs of physical protein-protein interactions predicted for entire proteomes}, author={Tran, Linh and Hamp, Tobias and Rost, Burkhard}, journal={bioRxiv}, pages={332510}, year={2018}, publisher={Cold Spring Harbor Laboratory} }</pre>
DOI: 10.6084/m9.figshare.6633341
title: D. melanogaster predictions

description: About this dataset: These are the most reliable 100,000 PPI predictions for D. melanogaster. It is a CSV file.<br>Motivation: Protein-protein interactions (PPIs) play a key role in many cellular processes. Most annotations of PPIs mix experimental and computational data. The mix optimizes coverage, but obfuscates the annotation origin. Some resources excel at focusing on reliable experimental data. Here, we focused on new pairs of interacting proteins for several model organisms based solely on sequence-based prediction methods. <br>Results: We extracted reliable experimental data about which proteins interact (binary) for eight diverse model organisms from public databases, namely from Escherichia coli, Schizosaccharomyces pombe, Plasmodium falciparum, Drosophila melanogaster, Caenorhabditis elegans, Mus musculus, Rattus norvegicus, Arabidopsis thaliana, and for the previously used Homo sapiens and Saccharomyces cerevisiae. Those data were the base to develop a PPI prediction method for each model organism. The method used evolutionary information through a profile-kernel Support Vector Machine (SVM). With the resulting eight models, we predicted all possible protein pairs in each organism and made the top predictions available through a web application. Almost all of the PPIs made available were predicted between proteins that have not been observed in any interaction, in particular for less well-studied organisms. Thus, our work complements existing resources and is particularly helpful for designing experiments because of its uniqueness. Experimental annotations and computational predictions are strongly influenced by the fact that some proteins have many partners and others few. To optimize machine learning, the newly methods explicitly ignored such a network-structure. This might be another strength of our approach. The database interface representing our results is accessible from https://rostlab.org/services/ppipair/.<br>Please cite us when you are using this data:<pre>@article{tran2018profppidb, title={ProfPPIdb: pairs of physical protein-protein interactions predicted for entire proteomes}, author={Tran, Linh and Hamp, Tobias and Rost, Burkhard}, journal={bioRxiv}, pages={332510}, year={2018}, publisher={Cold Spring Harbor Laboratory} }</pre>