Real-time vocal feature extraction for automated suicidal risk assessment in hotlines

General behaviours of cancer systems can be studied in prior-knowledge frameworks using logical modelling, in which cancer players are binary variables representing activation states connected by signed interactions. This coarse-grained method has been applied to design a generic prostate cancer logical model summarising the major hallmarks and pathways involved. • The scope of this study is to design methods to specify this logical model to individual models based on patient data: mutations and RNA/protein levels. It requires preliminary data processing steps with interpretation of mutations data (point mutations, fusions and copy number alterations) along with discretisation/normalisation of expression data. Patient-profiles thus computed are used to shape patientspecific models defining either mutated nodes constrained to the active/inactive state or the initial values of some nodes. • These data integration strategies are assessed by measuring the ability, with model simulation results, to accurately infer patients Gleason score, a prognostic factor of prostate cancer. Best logical models achieve comparable performances to prostate signature approaches, by also adding a knowledge layer which allows more precise analyses, hypotheses generation or validation experiments. 2 Current prostate cancer model and first simulation Each s imulat ion = 1’000 stochast ic trajectories. Node score is % of activation across all trajectories at asymptotic state. 1 Logical modelling principles • Each gene in the model (A, B or C) is a binary variable (0 or 1) representing its activity level. They are connected by logical rules corresponding to signed interactions (activation/inhibition) • S i m u l a t i o n s a r e p e r f o r m e d w i t h a s y n c h ro n o u s updates (i.e. considering the rates to order potential node states updates) using MaBoSS software, based on the stochastic Gillespie algorithm. 3 Original data and specification methods Therefore, mapping data to specification methods requires data processing to interpret, binarise, or normalise between 0 and 1 4Mutation profiles 5 Expression data processing and profiles 6 Qualitative analysis of different simulations cases •Defining mutated nodes is the only method r e s u l t i n g i n r e a l l y dispersed simulation results and significantly personalised models 7 Validation with Gleason Groups (GG) inference GG is a prognostic factor of prostate cancer divided in 5 stages, from indolent GG1 to agressive GG5. Using different combination of data and the resulting nodes scores, we classify GG1 from GG4&5 (binary classif. assessed with AUC). Reference 0.91 AUC is reached with a 133-gene expression signature in Penney et al. (2011). Classifications are also performed with raw (or network-propagated) inputs to assess model influence. Transient dynamics with inactivated P53 Data types Fusion genes CNA Mutations Method Enabling ERG/ ETV1 activation by androgens Assign most significant GISTIC values Infer biological effect of mutations by type or using databases for missense mutations Input CNA -2 CNA +2 Non-sense, frameshift indels, splice site. Missense assumed to be loss/gain of function Binary output 1 0 0 0/1 Model specification method Deviation from WT Model results Inputs alone Inputs with net. propag. Mutants Rates Initial conditions Mut. & CNA 0.109 0.767 0.687 0.718 Mut. CNA 0.050 0.690 RNA 0.009 0.578 0.924 RNA 0.142 0.819 Mut. , CNA & RNA 0.198 0.851 0.925 Mut. & CNA RNA 0.113 0.786 Classic nodes Input nodes Phenotype nodes Number 115 7 9 Meaning Key prostate cancer players Cell environment variables Biological readouts to summarise network state Examples MYC, P53, PTEN Nutrients, Androgen Proliferation, Apoptosis Optimizing single cell encapsulation in droplet microfluidic platforms for directed evolution applications Carried out in the Shih Microfluidics Laboratory at Concordia University, under the supervision of Professor Steve Shih Under the direction of Professor Philippe Renaud in the Microsystems Laboratory 4