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Position: Genomic Model Research Must Move Beyond Anecdotal Evaluation of Interpretability Methods
Shasha Zhou, Mingyu Huang, Ke Li
Advances in machine learning and computational power have unlocked the predictive potential of the human genome, yet biologists now demand that these models also elucidate the unde…
Assessing Automated Fact-Checking for Medical LLM Responses with Knowledge Graphs
Shasha Zhou, Mingyu Huang, Jack Cole +4
The recent proliferation of large language models (LLMs) holds the potential to revolutionize healthcare, with strong capabilities in diverse medical tasks. Yet, deploying LLMs in…
Augmenting Biological Fitness Prediction Benchmarks with Landscapes Features from GraphFLA
Mingyu Huang, Shasha Zhou, Ke Li
Machine learning models increasingly map biological sequence-fitness landscapes to predict mutational effects. Effective evaluation of these models requires benchmarks curated from…
Towards the Inferrence of Structural Similarity of Combinatorial Landscapes
Mingyu Huang, Ke Li
One of the most common problem-solving heuristics is by analogy. For a given problem, a solver can be viewed as a strategic walk on its fitness landscape. Thus if a solver works fo…
On the Hyperparameter Loss Landscapes of Machine Learning Models: An Exploratory Study
Mingyu Huang, Ke Li
Previous efforts on hyperparameter optimization (HPO) of machine learning (ML) models predominately focus on algorithmic advances, yet little is known about the topography of the u…