Please note: This PhD seminar will take place in DC 2310 and online.
Pablo
Millán
Arias,
PhD
candidate
David
R.
Cheriton
School
of
Computer
Science
Supervisor: Professor Lila Kari
Understanding biodiversity is a global challenge, in which DNA barcodes — short snippets of DNA that cluster by species — play a pivotal role. In particular, invertebrates, a highly diverse and under-explored group, pose unique taxonomic complexities. We explore machine learning approaches, comparing supervised CNNs, fine-tuned foundation models, and a DNA barcode-specific masking strategy across datasets of varying complexity. While simpler datasets and tasks favor supervised CNNs or fine-tuned transformers, challenging species-level identification demands a paradigm shift towards self-supervised pretraining.
We propose BarcodeBERT, the first self-supervised method for general biodiversity analysis, leveraging a 1.5 M invertebrate DNA barcode reference library. This work highlights how dataset specifics and coverage impact model selection, and underscores the role of self-supervised pretraining in achieving high-accuracy DNA barcode-based identification at the species and genus level. Indeed, without the fine-tuning step, BarcodeBERT pretrained on a large DNA barcode dataset outperforms DNABERT and DNABERT-2 on multiple downstream classification tasks.
Full paper available at https://arxiv.org/abs/2311.02401.
To attend this PhD seminar in person, please go to DC 2310. You can also attend virtually using Zoom at https://uwaterloo.zoom.us/j/92398875467.