The Bayesian hierarchical model named antigen-T cell interaction estimation is to estimate the history of the immune pressure on the evolution of the tumor clones.The model is based on the estimation result from Andrew Roth (2014) <doi:10.1038/nmeth.2883>.

Builds, validates, analyzes, and visualizes network data from
    dyadic, event, matrix, 'igraph', and 'network' inputs. Supports
    cross-sectional, longitudinal, bipartite, and multi-layer networks, with
    conversion helpers for common modeling workflows and plotting utilities
    for exploratory analysis. Network methods are described in Wasserman and
    Faust (1994, ISBN:9780521387071), Cranmer et al. (2021)
    <doi:10.1017/9781316662915>, and Minhas et al. (2022)
    <doi:10.1017/psrm.2021.56>.

Given a network (e.g. a food web), estimates several network indices. These include: Ascendency network indices, Direct and indirect dependencies, Effective measures, Environ network indices, General network indices, Pathway analysis, Network uncertainty indices and constraint efficiencies and the trophic level and omnivory indices of food webs.

Implementation of network integration approaches
    comprising unweighted and weighted integration methods. Unweighted integration
    is performed considering the average, per-edge average, maximum and minimum of
    networks edges. Weighted integration takes into account a weight for each 
    network during the fusion process, where the weights express
    the ''predictiveness strength'' of each network considering a specific predictive
    task. Weights can be learned using a machine learning algorithm able to associate
    the weights to the assessment of the accuracy of the learning algorithm 
    trained on the network itself. The implemented methods can be applied to 
    effectively integrate different biological networks modelling a wide range
    of problems in bioinformatics (e.g. disease gene prioritization, protein
    function prediction, drug repurposing, clinical outcome prediction).