CISPA
Browse
diffnaps-walter,fischer,vreeken.pdf (534.35 kB)

Finding Interpretable Class-Specific Patterns through Efficient Neural Search.

Download (534.35 kB)
journal contribution
posted on 2024-02-19, 09:35 authored by Nils WalterNils Walter, Jonas Fischer, Jilles VreekenJilles Vreeken
Discovering patterns in data that best describe the differences between classes allows to hypothesize and reason about class-specific mechanisms. In molecular biology, for example, this bears promise of advancing the understanding of cellular processes differing between tissues or diseases, which could lead to novel treatments. To be useful in practice, methods that tackle the problem of finding such differential patterns have to be readily interpretable by domain experts, and scalable to the extremely high-dimensional data. In this work, we propose a novel, inherently interpretable binary neural network architecture DIFFNAPS that extracts differential patterns from data. DiffNaps is scalable to hundreds of thousands of features and robust to noise, thus overcoming the limitations of current state-of-the-art methods in large-scale applications such as in biology. We show on synthetic and real world data, including three biological applications, that, unlike its competitors, DiffNaps consistently yields accurate, succinct, and interpretable class descriptions

History

Primary Research Area

  • Trustworthy Information Processing

Journal

CoRR

Volume

abs/2312.04311

Sub Type

  • Article

BibTeX

@article{Walter:Fischer:Vreeken:2023, title = "Finding Interpretable Class-Specific Patterns through Efficient Neural Search.", author = "Walter, Nils Philipp" AND "Fischer, Jonas" AND "Vreeken, Jilles", year = 2023, month = 12, journal = "CoRR" }

Usage metrics

    Categories

    No categories selected

    Licence

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC