A systematic biology tool was developed to prioritize cancer subtype-specific drugs by integrating genetic perturbation, drug action, biological pathway, and cancer subtype. 
    The capabilities of this tool include inferring patient-specific subpathway activity profiles in the context of gene expression profiles with subtype labels, calculating differentially 
    expressed subpathways based on cultured human cells treated with drugs in the 'cMap' (connectivity map) database, prioritizing cancer subtype specific drugs according to drug-disease 
    reverse association score based on subpathway, and visualization of results (Castelo (2013) <doi:10.1186/1471-2105-14-7>; Han et al (2019) <doi:10.1093/bioinformatics/btz894>; Lamb and Justin (2006) <doi:10.1126/science.1132939>). Please cite using <doi:10.1093/bioinformatics/btab011>.

Substitution matrices are important parameters in protein alignment algorithms. These matrices represent the likelihood that an amino acid will be substituted for another during mutation. This tool allows users to apply predefined and custom matrices and then explore the resulting alignments with interactive visualizations.  'SubVis' requires the availability of a web browser.

Quality control charts for survival outcomes.
    Allows users to construct the Continuous Time Generalized
    Rapid Response CUSUM (CGR-CUSUM) <doi:10.1093/biostatistics/kxac041>, 
    the Biswas & Kalbfleisch (2008)  <doi:10.1002/sim.3216> CUSUM, 
    the Bernoulli CUSUM and the risk-adjusted funnel plot for survival data 
    <doi:10.1002/sim.1970>. 
    These procedures can be used to monitor survival processes for a change 
    in the failure rate.

Interface to 'Sudachi' <https://github.com/WorksApplications/Sudachi>,
    a Japanese morphological analyzer. This is a port of what is available in Python.

Identify sudden gains based on the three criteria outlined by Tang and DeRubeis (1999) <doi:10.1037/0022-006X.67.6.894> to a selection of repeated measures. Sudden losses, defined as the opposite of sudden gains can also be identified. Two different datasets can be created, one including all sudden gains/losses and one including one selected sudden gain/loss for each case. It can extract scores around sudden gains/losses. It can plot the average change around sudden gains/losses and trajectories of individual cases.