HPiP HPiP (Host-Pathogen Interaction Prediction)uses an ensemble learning algorithm for prediction of host-pathogenprotein-protein interactions (HP-PPIs) using structural andphysicochemical descriptors computed from amino acid-composition ofhost and pathogen proteins.The proposed package can effectivelyaddress data shortages and data unavailability for HP-PPI networkreconstructions. Moreover, establishing computational frameworks inthat regard will reveal mechanistic insights into infectiousdiseases and suggest potential HP-PPI targets, thus narrowing downthe range of possible candidates for subsequent wet-labexperimental validations.
Path of Giants 2.1.7 –
iPath iPath is the Bioconductor package used forcalculating personalized pathway score and test the associationwith survival outcomes. Abundant single-gene biomarkers have beenidentified and used in the clinics. However, hundreds of oncogenesor tumor-suppressor genes are involved during the process oftumorigenesis. We believe individual-level expression patterns ofpre-defined pathways or gene sets are better biomarkers than singlegenes. In this study, we devised a computational method named iPathto identify prognostic biomarker pathways, one sample at a time. Totest its utility, we conducted a pan-cancer analysis across 14cancer types from The Cancer Genome Atlas and demonstrated thatiPath is capable of identifying highly predictive biomarkers forclinical outcomes, including overall survival, tumor subtypes, andtumor stage classifications. We found that pathway-based biomarkersare more robust and effective than single genes.
metapone The package conducts pathway testingfrom untargetted metabolomics data. It requires the user to supplyfeature-level test results, from case-control testing, regression,or other suitable feature-level tests for the study design. Weightsare given to metabolic features based on how many metabolites theycould potentially match to. The package can combine positive andnegative mode results in pathway tests. 2ff7e9595c
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