English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Boolean functions and networks are commonly used in the modeling and analysisof complex biological systems, and this paradigm is highly relevant in otherimportant areas in data science and decision making, such as in the medicalfield and in the finance industry. Automated learning of a Boolean network andBoolean functions, from data, is a challenging task due in part to the largenumber of unknowns (including both the structure of the network and thefunctions) to be estimated, for which a brute force approach would beexponentially complex. In this paper we develop a new information theoreticmethodology that we show to be significantly more efficient than previousapproaches. Building on the recently developed optimal causation entropyprinciple (oCSE), that we proved can correctly infer networks distinguishingbetween direct versus indirect connections, we develop here an efficientalgorithm that furthermore infers a Boolean network (including both itsstructure and function) based on data observed from the evolving states atnodes. We call this new inference method, Boolean optimal causation entropy(BoCSE), which we will show that our method is both computationally efficientand also resilient to noise. Furthermore, it allows for selection of a set offeatures that best explains the process, a statement that can be described as anetworked Boolean function reduced order model. We highlight our method to thefeature selection in several real-world examples: (1) diagnosis of urinarydiseases, (2) Cardiac SPECT diagnosis, (3) informative positions in the gameTic-Tac-Toe, and (4) risk causality analysis of loans in default status. Ourproposed method is effective and efficient in all examples.

Data-Driven Learning of Boolean Networks and Functions by Optimal Causation Entropy Principle (BoCSE)