Mark Basham

This paper introduces Adaptive Polishing, a machine learning-driven method for improving the consistency and thinness of cryo-lamellae during focused ion beam milling.

The paper evaluates feature representations for predicting cancer-associated sORFs in non-coding RNA using traditional machine learning models and finds that k-mer features outperform genomic language model embeddings.

The paper benchmarks siRNA prediction methods, showing that rigorous cross-validation and z-curve representation improve performance and reliability.

This study introduces GENNUS, a generative approach using GANs and SMOTE to improve ncRNA classification by addressing class imbalance through synthetic data generation.

This paper reviews existing biological segmentation datasets and recommends improvements to make them more reusable following FAIR principles.

CHiMP tools automate protein crystallisation experiments by classifying micrographs and detecting crystals and droplets using deep learning.

Okapi-EM is a Napari plugin that processes and analyses cryogenic serial FIB/SEM images by aligning slices, evaluating image quality, and mitigating charging artefacts.

Ot2Rec is a semi-automatic, extensible workflow for cryo-ET image processing that enables flexible, automated tomographic reconstruction through plugins.