Probabilistic Computational Causal Discovery for Systems Biology
نویسنده
چکیده
Discovering the causal mechanisms of biological systems is necessary to design new drugs and therapies. Computational Causal Discovery (CD) is a field that offers the potential to discover causal relations and causal models under certain conditions with a limited set of interventions / manipulations. This chapter reviews the basic concepts and principles of CD, the nature of the assumptions to enable it, potential pitfalls in its application, and recent advances and directions. Importantly, several success stories in molecular and systems biology are discussed in detail.
منابع مشابه
kleinberg A dissertation submitted in partial fulfillment of the requirements
In many domains we face the problem of determining the underlying causal structure from time-course observations of a system. Whether we have neural spike trains in neuroscience, gene expression levels in systems biology, or stock price movements in finance, we want to determine why these systems behave the way they do. For this purpose we must assess which of the myriad possible causes are sig...
متن کاملApplication of a Causal Discovery Algorithm to the Analysis of Arthroplasty Registry Data
Improving the quality of care for hip arthroplasty (replacement) patients requires the systematic evaluation of clinical performance of implants and the identification of "outlier" devices that have an especially high risk of reoperation ("revision"). Postmarket surveillance of arthroplasty implants, which rests on the analysis of large patient registries, has been effective in identifying outl...
متن کاملCausal Explorer: A Causal Probabilistic Network Learning Toolkit for Biomedical Discovery
Causal Probabilistic Networks (CPNs), (a.k.a. Bayesian Networks, or Belief Networks) are well-established representations in biomedical applications such as decision support systems and predictive modeling or mining of causal hypotheses. CPNs (a) have well-developed theory for induction of causal relationships, and (b) are suitable for creating sound and practical decision support systems. Whil...
متن کاملJoint Probabilistic Inference of Causal Structure
Causal directed acyclic graphical models (DAGs) are powerful reasoning tools in the study and estimation of cause and effect in scientific and socio-behavioral phenomena. In many domains where the cause and effect structure is unknown, a key challenge in studying causality with DAGs is learning the structure of causal graphs directly from observational data. Traditional approaches to causal str...
متن کاملCausal Bayesian NetworkX
Probabilistic graphical models are useful tools for modeling systems governed by probabilistic structure. Bayesian networks are one class of probabilistic graphical model that have proven useful for characterizing both formal systems and for reasoning with those systems. Probabilistic dependencies in Bayesian networks are graphically expressed in terms of directed links from parents to their ch...
متن کامل