Brhanu Fentaw Znabu
Ph.D. student in Biomedical Engineering at the University of Nebraska–Lincoln,
advised by Nicole R. Sexton at the
Nebraska Center for Virology
and co-advised by Qiuming Yao in the
School of Computing.
I build and evaluate genomic foundation models for biological sequence analysis.
My current work focuses on domain-adaptive pre-training of large language models (DNABERT-2) for
viral host-range prediction and epidemic emergence forecasting, with an emphasis on
rigorous leakage-aware evaluation and model interpretability.
I am also exploring discrete diffusion models and conditional generative frameworks
for protein variant design, combining protein language model representations with fitness-guided generation.
News
Software & Benchmarks
Discrete-diffusion protein language model with ESM-2 conditioning and classifier-free guidance. Full training and evaluation pipeline with a leakage-aware ProteinGym harness whose ESM-2 baseline reproduces the public leaderboard (Pearson r = 0.973), plus a head-to-head evaluation quantifying where diffusion does and does not yet beat ESM-2 at this model scale.
Oracle-independence and novelty audit for generative DNA design with Proto (Hie lab, Arc Institute): tests whether sequences optimized against one in-silico oracle survive an independent oracle and remain genuinely novel. Across two independent oracles (Malinois, Enformer; agreement ρ = 0.865), 93% of gradient-optimized designs transfer to the held-out oracle, finding no evidence of oracle-hacking.
Open benchmark of splicing variant-effect predictors against a real experimental assay. On 27,733 exonic variants from the MFASS multiplexed assay (Cheung 2019), Pangolin most accurately predicts splice-disrupting variants (AUROC 0.888), ahead of SpliceAI and SpliceTransformer; a calibrated consensus does not beat Pangolin alone. Predictors are run through Proto, with every number reproducible from the released data.
Leakage-aware benchmark for antibody thermostability (Tm) prediction built on public data, with grouped splits that prevent near-duplicate sequences from leaking between train and test. On Jain 2017 (n = 137 clinical antibodies), a leakage-free split collapses accuracy from Spearman 0.24 to 0.08–0.14, exposing how clonal-sequence leakage inflates standard benchmarks.
Leakage-aware benchmark for single-cell perturbation-response prediction, quantifying how much measured performance depends on the train/test split design. On the Norman dataset, the conditional VAE predicts unseen double perturbations at ΔPearson 0.748 versus 0.551 for a mean-of-training baseline (131 held-out combinations).
Open benchmark and sequence-based triage model for native mass spectrometry suitability. The production model reaches cluster-aware ROC-AUC 0.835 ± 0.029 (n = 635) under homology-controlled cross-validation, and is shipped as a live web tool, a PyPI SDK, and a command-line interface.
Publications
ArboFM: domain-adapted DNABERT-2 via continued MLM pre-training on 120K+ arbovirus genome windows (9,299 genomes, 362 species, 6 families). Predicts epidemic emergence with AP = 0.978 (Flaviviridae). Retrospective temporal validation detects Zika, chikungunya, and West Nile epidemic lineages before documented emergence.
DNABERT-2 embeddings, k-mer TF-IDF, and composition features on 3,031 orthoflavivirus genomes. Quantifies a 16-percentage-point performance inflation from phylogenetic leakage across all three representations. Genome-localized attribution concentrates discriminative signal in the NS3–NS5 replication region (65% of arbovirus genomes) and identifies UpA-containing motifs linked to dinucleotide-mediated host restriction.
Experience
About
My research sits at the intersection of machine learning and genomics. I develop foundation models and rigorous evaluation frameworks for biological sequence analysis, with applications in viral emergence prediction, host-range classification, and model interpretability. My work increasingly spans generative modeling, where I apply discrete diffusion and classifier-free guidance techniques to design protein variants with targeted functional properties. I work primarily with PyTorch, Hugging Face Transformers, and the SciPy/PyData ecosystem on HPC GPU clusters.
Prior to my PhD, I earned an M.Sc. in Biomedical Science and Engineering from Gwangju Institute of Science and Technology (GIST), South Korea, where I applied unsupervised machine learning (autoregressive models, Hidden Markov models) to 3D behavioral phenotyping in diabetic neuropathy mouse models. I also hold a B.Sc. in Biomedical Engineering from Jimma University, Ethiopia, graduating in the top 2% of my Biomedical Engineering class.
When I'm not at the computer, you'll find me exploring coffee shops ☕ or at the gym 🏋.