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PSICQUIC / Proteomics Standard Initiative Common QUery InterfaCe
It was created by the Human Proteome Organization Proteomics Standards Initiative (HUPO-PSI) to enable computational access to molecular-interaction data resources by means of a standard Web Service and query language. Currently providing >150 million binary interaction evidences from 28 servers globally, the PSICQUIC interface allows the concurrent search of multiple molecular-interaction information resources using a single query.
HIPPIE / Human Integrated Protein-Protein Interaction rEference
A human PPI dataset with a normalized scoring scheme that integrates multiple experimental PPI datasets. HIPPIE's scoring scheme has been optimized by human experts and a computer algorithm to reflect the amount and quality of evidence for a given PPI and we show that these scores correlate to the quality of the experimental characterization. The HIPPIE web tool allows researchers to do network analyses focused on likely true PPI sets by generating subnetworks around proteins of interest at a specified confidence level.
Affords the integration, analysis and qualitative assessment of distributed sources of interaction data in a dynamic fashion. Since DASMIweb allows for querying many different resources of protein and domain interactions simultaneously, it serves as an important starting point for interactome studies and assists the user in finding publicly accessible interaction data with minimal effort. The pool of queried resources is fully configurable and supports the inclusion of own interaction data or confidence scores. In particular, DASMIweb integrates confidence measures like functional similarity scores to assess individual interactions. The retrieved results can be exported in different file formats like MITAB or SIF.
Performs systematic statistical evaluation of scoring systems in a dataset. Qisampler is an R script that systematically evaluates several scoring schemes for high throughput experiments versus given golden sets using a sampling strategy. It is also able to reproduce the superiority of the DN score over the z-score. Modularity of the input format allows the use of this application with various dataset types, such as protein-protein interactions (PPIs), gene-expression microarray, or deep sequencing datasets.
Provides an R package of important dependency estimators for gene network inference algorithms. DepEst contains 11 different association estimators that works in parallel or in series programming which are as follows: Pearson Correlation Coefficient (PCC), Spearman Correlation Coefficient (SCC), Pearson Based Gaussian Estimator (PBG), Spearman Based Gaussian Estimator (SBG), Nth Order Partial Pearson Correlation Coefficient (PPCN), Miller-Madow Mutual Information Estimator (MM), Chao-Shen Mutual Information Estimator (CS), B-spline Mutual Information Estimator (BS), Heller Heller Gorfine Estimator (HHG), Kernel Density Estimator (KDE), K Nearest Neighborhood Estimator (KNN). The R package also includes auxiliary functions for equal frequency discretization, equal wideness discretization, elimination of non-significant interactions, copula transformation and cluster installation for parallel computing.
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Calculates confidence scores for user-specified sets of interactions. IntScore provides six network topology- and annotation-based confidence scoring methods. It also enables the integration of scores calculated by the different methods into an aggregate score using machine learning approaches. IntScore can serve experimentalists to increase the quality of data produced by interaction screens and assess the performance of those screens, and can help computational biologists to increase the reliability of network-based inferences by controlling the accuracy of the input interaction data.
PRINCESS / PRotein INteraction Confidence Evaluation System with multiple Sources
A web tool to combine multiple heterogeneous biological evidences, including model organism protein-protein interaction, interaction domain, functional annotation, gene expression, genome context, and network topology structure, to assign reliability to the human protein-protein interactions identified by high-throughput experiments. The main strategy of PRINCESS is to use likelihood ratios to assess the reliability of individual biological evidences based on golden standard data sets and then to combine these individual likelihood ratios by a Bayesian model to assign confidence scores to the high throughput protein interactions.
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