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DTSTAMP:20260711T102924Z
UID:7f5708ce-accc-475a-8dd7-bed9cbd471ea
DTSTART:20220422T120000Z
DTEND:20220422T150000Z
DESCRIPTION:Screening for potential cancer therapies using existing large d
 atasets of drug perturbations requires expertise and resources not availab
 le to all. This is often a barrier for lab scientists to tap into these va
 luable resources. To address these issues\, one can take advantage of prio
 r knowledge especially those coded in standard formats such as causal biol
 ogical networks (CBN). Large datasets can be converted into appropriate st
 ructures\, analyzed once and the results made freely available in easy-to-
 use formats. **In this three parts tutorial\, we will give a full descript
 ion of one large scale analysis of using this approach\, one case study of
  building a network of metastasis suppressors from scratch\, and a walkthr
 ough example code to perform and adapt these tools for different use cases
 .**\n\n**Detailed description:**\n\n- Part One: a talk describing the cons
 truction of a database for cancer-cell-specific perturbations of biologica
 l networks (LINPS). We pre-computed cancer-cell-specific perturbation ampl
 itudes of several biological networks and made the output available in a d
 atabase with an interactive web interface.\n- Part Two: a talk describing 
 the building of a functional network model of the metastasis suppressor RK
 IP and its regulators in breast cancer cells. In this case study\, we appl
 ied text mining and a manual literature search to extract known interactio
 ns between several metastasis suppressors and their regulators. Then we ad
 opted a reverse causal reasoning approach to evaluate and prioritize pathw
 ays that are most consistent and responsive to drugs that inhibit cell gro
 wth. We further validated some of the predicted regulatory links in the br
 east cancer cell line MCF7 experimentally and highlighted the points of un
 certainty in our model.\n- Part Three: a code walkthrough encoding directe
 d interactions into the biological expression language (BEL)\, computing t
 he network perturbation amplitudes (NPA)\, and interpreting the output.\n\
 n**Pre-requisites:**\n\n● Knowledge of the mechanisms of transcriptional
  regulation\n● Familiarity with high-throughput gene expression data\n
 ● Basic knowledge of R and Bioconductor\n● Familiarity with Docker and
  Git (This is only required to execute the code on a local machine)
SUMMARY:Integrating gene expression and biological knowledge for drug disco
 very and repurposing
URL;VALUE=URI:https://www.iscb.org/cms_addon/academy/events#65
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