Renming Liu

The paper presents a longitudinal multimodal benchmarking dataset and competition to evaluate modality prediction methods for gene regulatory inference, showing top models outperform existing approaches.

This paper introduces HO_SCPLOWEIMDALLC_SCPLOW, a framework to analyze tokenizer design in scFMs, showing that specific tokenization choices improve generalization under distribution shift.

DANCE 2.0 automates and interprets single-cell preprocessing by recommending optimal pipelines for various tasks, improving performance across six major analyses.

DANCE provides a benchmark platform for single-cell analysis with 32 methods across 21 datasets, enabling easy reproduction and development of deep learning models.

GenePlexusZoo improves gene classification by integrating molecular networks from multiple species into a shared feature space, enhancing both single-species and cross-species predictions.

CONE generates context-specific gene embeddings from context-free interactomes to identify disease-associated genes using contextualized random walks and graph attention networks.

This paper introduces a general single-cell analysis framework using conditional diffusion models, showing competitive performance across tasks with added flexibility via prior information.

The paper introduces a large-scale spatial transcriptomic dataset and a Graph Neural Network-based method for cell type deconvolution in human immuno-oncology.

PyGenePlexus predicts gene associations with input gene sets using network-based machine learning and provides interpretability through comparison with known gene sets.

The paper introduces OBNB, a benchmarking resource for GNN-based network biology tasks, providing datasets and a Python toolkit for evaluating gene classification models.

The paper introduces node2vec+, an improved version of node2vec that incorporates edge weights in biased random walks, showing better performance on weighted gene networks.

PecanPy provides a fast and efficient Python implementation of node2vec for embedding nodes in biological networks, improving scalability for dense graphs.