ProphTools General Prioritization Tools for Heterogeneous Biological Networks Go to ProphTools repository »

ProphTools is a general-purpose hetreogeneous network prioritization tool based on the successful ad-hoc prioritization methodologies in ProphNet and DrugNet. ProphTools is open-source and multiplatform and can be downloaded from its GitHub repository and easily installed via pip.

Additional datasets for lncRNA-disease prioritization: general, specific.

Cite us: Navarro, C., Martínez, V., Blanco, A., Cano, C. (2017). ProphTools: General Prioritization Tools for Heterogeneous Biological Networks. GigaScience. Submitted.

CisMiner Regulatory module predictionGo to CisMiner site »

CisMiner allows to perform a blind search of CRMs without any prior information about target CRMs nor limitation in the number of motifs. CisMiner tackles the combinatorial complexity of genome-wide cis-regulatory module extraction using a natural representation of motif combinations as itemsets and applying the Top-Down Fuzzy Frequent-Pattern Tree algorithm to identify significant itemsets. Fuzzy technology allows CisMiner to better handle the imprecision and noise inherent to regulatory processes.

ProphNet Disease-gene prioritizationGo to ProphNet site »

ProphNet is a network disease-gene prioritization tool. Given a set of diseases of interest, ProphNet returns a list of prioritized diseases that are related to the disease.

DrugNet Computational drug repositioningGo to DrugNet site »

Computational drug repositioning can lead to a considerable reduction in cost and time in any drug development process. Recent approaches have addressed the network-based nature of biological information for performing complex prioritization tasks. In this work, we apply a new methodology based on heterogeneous network prioritization that can aid researchers in the drug repositioning process.

SC Intuit Intuitionistic sequence-motif scoring Download SCIntuit

SC_intuit is a scoring method for measuring sequence-motif affinity based on IFS theory. Unlike other methods that consider dependencies between positions, SC_intuit is designed to prevent overestimation of less conserved positions of TFBSs. For a given pair of bases, SC_intuit is computed not only as a function of their combined probability of occurrence, but also taking into account the individual importance of each single base at its corresponding position.

Cite us: Garcia-Alcalde, F., Blanco, A., Shepherd, A. J. (2010). An intuitionistic approach to scoring DNA sequences against transcription factor binding site motifs. BMC bioinformatics, 11(1), 551.