An extensive dataset of binary physical protein-protein interaction extracted from STRING 12.0 (>12,000 organisms) with artificially generated negatives. The dataset includes 72M positive pairs with STRING confidence scores> 0.9 and 720M negative pairs. The corresponding protein sequences are located in the .fasta files. The generation of the negatives was derived from https://doi.org/10.1016/j.isci.2024.110371

Enriched GO terms for proteins with higher than expected γ not identified when analyzing positively selected protein sequences.

POINT is a protein-protein interaction database. It includes annotation of interologs and protein phsophorylation. This work analyzes the applicability of orthologs-based PPI prediction and provide the theoretical upper-bound of this approach.

PPI network information.

United States Producer Price Index data was reported at 259.726 1982=100 in Feb 2025. This records an increase from the previous number of 257.202 1982=100 for Jan 2025. United States Producer Price Index data is updated monthly, averaging 34.900 1982=100 from Jan 1913 (Median) to Feb 2025, with 1346 observations. The data reached an all-time high of 280.251 1982=100 in Jun 2022 and a record low of 10.300 1982=100 in Feb 1933. United States Producer Price Index data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.I060: Producer Price Index: by Commodities. [COVID-19-IMPACT]

The prediction of protein complexes from protein-protein interactions (PPIs) is a well-studied problem in bioinformatics. However, the currently available PPI data is not enough to describe all known protein complexes. In this paper, we express the problem of determining the minimum number of (additional) required protein-protein interactions as a graph theoretic problem under the constraint that each complex constitutes a connected component in a PPI network. For this problem, we develop two computational methods: one is based on integer linear programming (ILPMinPPI) and the other one is based on an existing greedy-type approximation algorithm (GreedyMinPPI) originally developed in the context of communication and social networks. Since the former method is only applicable to datasets of small size, we apply the latter method to a combination of the CYC2008 protein complex dataset and each of eight PPI datasets (STRING, MINT, BioGRID, IntAct, DIP, BIND, WI-PHI, iRefIndex). The results show that the minimum number of additional required PPIs ranges from 51 (STRING) to 964 (BIND), and that even the four best PPI databases, STRING (51), BioGRID (67), WI-PHI (93) and iRefIndex (85), do not include enough PPIs to form all CYC2008 protein complexes. We also demonstrate that the proposed problem framework and our solutions can enhance the prediction accuracy of existing PPI prediction methods. ILPMinPPI can be freely downloaded from http://sunflower.kuicr.kyoto-u.ac.jp/~nakajima/.

Producer Prices in Slovenia increased 0.50 percent in February of 2025 over the same month in the previous year. This dataset provides the latest reported value for - Slovenia Producer Prices Change - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

All-to-all prediction scores between Human and HIV1 for three independent sequence-based PPI predictors: PIPE4, SPRINT, SPPS. Each algorithm was trained on intra-species PPIs (Human-Human & HIV1-HIV1) to generate the inter-species predictions. The training samples were obtained from BioGRID. The human proteome was obtained from Uniprot (Proteome Id: UP000005640) and filtered for Reviewed/Swiss-Prot status; resulting in 20,350 proteins (7 proteins excluded due to sequence length). The HIV1 proteome was similarly obtained (Proteome Id: UP000002241); resulting in 9 proteins. All 183,087 predictions are provided for each method except SPPS for which 25 human protein sequences were excluded for having non-standard amino acids. Each file contains three columns of comma separated values representing: human-protein,hiv1-protein,score where the score column represents the likelihood of interaction for that given PPI. Files are sorted on the Human protein and then on the HIV1 protein.

Producer Prices in Estonia increased 6.10 percent in February of 2025 over the same month in the previous year. This dataset provides the latest reported value for - Estonia Producer Prices Change - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The global proton pump inhibitors (PPIs) market size was valued at USD 3.34 Billion in 2024, driven by the rising incidence of gastroesophageal reflux disease (GERD) cases across the globe. The market is expected to grow at a CAGR of 4.50% during the forecast period of 2025-2034, to reach USD 5.19 Billion by 2034.

In November 2024, the producer price index (PPI) in the United States was the highest in the four countries/areas under consideration. That month, its index score stood at above 146, compared to roughly 127 in the Euro Area, which was the second highest in the four areas. Contrarily, China is struggling with a decreasing PPI. The producer price index (PPI) measures the average change over time in the selling prices received by domestic producers for their output.

Six human PPI networks. (ZIP)

Database to catalog experimentally determined interactions between proteins combining information from a variety of sources to create a single, consistent set of protein-protein interactions that can be downloaded in a variety of formats. The data were curated, both, manually and also automatically using computational approaches that utilize the the knowledge about the protein-protein interaction networks extracted from the most reliable, core subset of the DIP data. Because the reliability of experimental evidence varies widely, methods of quality assessment have been developed and utilized to identify the most reliable subset of the interactions. This CORE set can be used as a reference when evaluating the reliability of high-throughput protein-protein interaction data sets, for development of prediction methods, as well as in the studies of the properties of protein interaction networks. Tools are available to analyze, visualize and integrate user's own experimental data with the information about protein-protein interactions available in the DIP database. The DIP database lists protein pairs that are known to interact with each other. By interact they mean that two amino acid chains were experimentally identified to bind to each other. The database lists such pairs to aid those studying a particular protein-protein interaction but also those investigating entire regulatory and signaling pathways as well as those studying the organization and complexity of the protein interaction network at the cellular level. Registration is required to gain access to most of the DIP features. Registration is free to the members of the academic community. Trial accounts for the commercial users are also available.

Producer Prices In the Euro Area increased 1.80 percent in January of 2025 over the same month in the previous year. This dataset provides the latest reported value for - Euro Area Producer Prices Change - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Producer Prices in Canada increased 5.80 percent in January of 2025 over the same month in the previous year. This dataset provides the latest reported value for - Canada Producer Prices Change - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Combined-scores: PPIs that have combined scores are considered positive cases. Experimental-700: PPIs that have experimental scores over 700 are considered positive cases. Direct comparison: the results of embeddings using the same method (cbow) and same hyperparameters. Different embedding methods: the results of BioConceptVec (skip-gram), BioConceptVec (GloVe) and BioConceptVec (fastText). The highest results of each section are marked as bold.

Producer Prices in Austria decreased 0.40 percent in January of 2025 over the same month in the previous year. This dataset provides - Austria Producer Prices Change - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Collection description

Subject species

homo sapiens

Modality

fMRI-BOLD

Analysis level

group

Cognitive paradigm (task)

gambling fMRI task paradigm

Map type

T

Protein protein interaction prediction datasets related to 2 different species. These datasets have been comprehensively used in published literature to assess the performance of protein-protein interaction predictors.

Protein interactions shape proteome function and thus biology. Identification of protein interactions is a major goal in molecular biology, but biochemical methods, although improving, remain limited in coverage and accuracy. Whereas computational predictions can guide biochemical experiments, low validation rates of predictions remain a major limitation. Here, we investigated computational methods in the prediction of a specific type of interaction, the inhibitory interactions between proteases and their inhibitors. Proteases generate thousands of proteoforms that dynamically shape the functional state of proteomes. Despite the important regulatory role of proteases, knowledge of their inhibitors remains largely incomplete with the vast majority of proteases lacking an annotated inhibitor. To link inhibitors to their target proteases on a large scale, we applied computational methods to predict inhibitory interactions between proteases and their inhibitors based on complementary data including coexpression, phylogenetic similarity, structural information, co-annotation, and colocalization, and also surveyed general protein interaction networks for potential inhibitory interactions. In testing nine predicted interactions biochemically, we validated the inhibition of kallikrein 5 by serpin B12. Despite the use of a wide array of complementary data, we found a high false positive rate of computational predictions in biochemical follow-up. Based on a protease-specific definition of true negatives derived from the biochemical classification of proteases and inhibitors, we analyzed prediction accuracy of individual features. Thereby we identified feature-specific limitations, which also affected general protein interaction prediction methods. Interestingly, proteases were often not coexpressed with most of their functional inhibitors, contrary to what is commonly assumed and extrapolated predominantly from cell culture experiments. Predictions of inhibitory interactions were indeed more challenging than predictions of non-proteolytic and non-inhibitory interactions. In summary, we describe a novel and well-defined but difficult protein interaction prediction task, and thereby highlight limitations of computational interaction prediction methods.