Frank Hutter

The paper predicts plant morphological complexity using transcription factor data with TabPFN, finding TF family expansion drives complexity evolution.

The paper develops a transcriptome-based method to match bulk RNA-seq data from kidney cell lines to single-cell reference datasets, identifying accurate cell type identities and evaluating method performance.

The paper improves intra-European ancestry prediction by selecting informative genetic markers using machine learning, achieving higher accuracy than existing marker panels.

This paper benchmarks forensic ancestry prediction tools against TabPFN, showing TabPFN outperforms existing methods in accuracy and ROC AUC.

This work introduces a dataset and a transformer-based model for classifying ncRNA-protein interactions, showing strong performance compared to existing methods.

KinPFN approximates RNA folding kinetics using prior-data fitted networks, achieving fast and accurate predictions with synthetic data training.

This paper introduces a new RNA design paradigm called partial RNA design and presents an algorithm, libLEARNA, that efficiently solves constraint RNA design tasks with variable lengths and precise control over sequence and structure constraints.

RNAinformer designs RNAs from secondary structures by inverting folding algorithms, including non-canonical and tertiary interactions, achieving state-of-the-art performance.

RNAformer predicts RNA secondary structure directly from sequence using a simple axial-attention model, achieving state-of-the-art performance while addressing homology concerns.

RnaBench provides a library for RNA deep learning research, offering curated datasets, benchmarks, and tools to address challenges in RNA structure prediction and design.