Generating Explainable and Effective Data Descriptors Using Relational Learning: Application to Cancer Biology
Paper i proceeding, 2020

The key to success in machine learning is the use of effective data representations. The success of deep neural networks (DNNs) is based on their ability to utilize multiple neural network layers, and big data, to learn how to convert simple input representations into richer internal representations that are effective for learning. However, these internal representations are sub-symbolic and difficult to explain. In many scientific problems explainable models are required, and the input data is semantically complex and unsuitable for DNNs. This is true in the fundamental problem of understanding the mechanism of cancer drugs, which requires complex background knowledge about the functions of genes/proteins, their cells, and the molecular structure of the drugs. This background knowledge cannot be compactly expressed propositionally, and requires at least the expressive power of Datalog. Here we demonstrate the use of relational learning to generate new data descriptors in such semantically complex background knowledge. These new descriptors are effective: adding them to standard propositional learning methods significantly improves prediction accuracy. They are also explainable, and add to our understanding of cancer. Our approach can readily be expanded to include other complex forms of background knowledge, and combines the generality of relational learning with the efficiency of standard propositional learning.

Relational learning

Gene expression

Inductive logic programming

Författare

Oghenejokpeme I. Orhobor

University of Cambridge

Joseph French

Manchester

Larisa N. Soldatova

Goldsmiths, University of London

Ross D. King

University of Cambridge

Alan Turing Institute

Chalmers, Biologi och bioteknik

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

03029743 (ISSN) 16113349 (eISSN)

Vol. 12323 LNAI 374-385

23rd International Conference on Discovery Science, DS 2020
Thessaloniki, Greece,

Ämneskategorier

Bioinformatik (beräkningsbiologi)

Bioinformatik och systembiologi

Datavetenskap (datalogi)

DOI

10.1007/978-3-030-61527-7_25

Mer information

Senast uppdaterat

2020-11-25