Hierarchy Aligned Commonality Through Prototypical Networks: Discovering Evolutionary Traits over Tree-of-Life

A grand challenge in biology is to discover evolutionary traits, which are features of organisms common to a group of species with a shared ancestor in the Tree of Life (also referred to as phylogenetic tree). With the recent availability of large-scale image repositories in biology and advances in the field of explainable machine learning (ML) such as ProtoPNet and other prototype-based methods, there is a tremendous opportunity to discover evolutionary traits directly from images in the form of a hierarchy of prototypes learned at internal nodes of the phylogenetic tree. However, current prototype-based methods are mostly designed to operate over a flat structure of classes and face several challenges in discovering hierarchical prototypes on a tree, including the problem of learning over-specific features at internal nodes in the tree. To overcome these challenges, we introduce the framework of Hierarchy aligned Commonality through Prototypical Networks (HComP-Net), which learns common features shared by all descendant species of an internal node and avoids the learning of over-specific prototypes. We empirically show that HComP-Net learns prototypes that are of high accuracy, semantically consistent, and generalizable to unseen species in comparison to baselines. While we focus on the biological problem of discovering evolutionary traits, our work can be applied to any domain involving a hierarchy of classes.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Computer Engineering

Department dc:contributor.department

Electrical and Computer Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2024

Author dc:creator

• Manogaran, Harish Babu

Chairs dc:contributor.committeechair

• Abbott, Amos L.
• Karpatne, Anuj

Committee member dc:contributor.committeemember

• Jones, Creed Farris

• Deep learning
• Interpretable Machine Learning
• Explainable AI
• Prototype-based Neural Networks
• Phylogeny
• Evolutionary Biology

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:41589

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/121329