Xiaohui S. Xie

The paper introduces BiLO, a neural network method for solving PDE inverse problems by formulating them as bilevel optimization problems, showing it enforces PDE constraints and handles noisy data effectively.

This paper improves the computational efficiency of protein language models, achieving faster inference, reduced memory usage, and efficient fine-tuning for better accessibility.

This paper introduces EpiOut, a tool to detect chromatin accessibility outliers in ALS, showing they correlate with gene expression and protein level aberrations.

UFold predicts RNA secondary structures using deep learning, outperforming thermodynamic models and other learning-based methods with high accuracy and speed.

SAILER improves scATAC-seq analysis by learning invariant representations, enhancing clustering and imputation while scaling to large datasets.

This paper introduces CHYRON, a DNA recorder that writes new DNA to track lineage and record cellular stimuli, demonstrating its ability to reconstruct lineage relationships and respond to stress signals.

This paper introduces scFAN, a deep learning model that predicts genome-wide TF binding in single cells using single-cell ATAC-seq data and pre-trained on bulk ATAC-seq, DNA sequence, and ChIP-seq data.

FactorNet predicts cell type-specific TF binding from sequential data using a convolutional-recurrent neural network, improving efficiency and interpretability in gene regulation studies.

This study uses a crowdsourcing approach to stratify ALS patients, identifying four distinct clusters with unique predictors and improving outcome prediction.

The paper introduces a parallelized DPMM model for single-cell transcriptomic clustering, improving accuracy and speed compared to existing methods.

DeepEM improves pulmonary nodule detection in CT images by using weakly supervised labels from electronic medical records.

On LIDC-IDRI, gradient boosting on 3D dual path network features reaches 90.44% accuracy on annotated nodules; with a 3D Faster R-CNN detector, patient-level accuracy is 81.41%, 99% of doctors' average.

This study examines how Nipbl haploinsufficiency affects cohesin binding and gene expression in mice, linking reduced cohesin function to CdLS-related gene dysregulation.

The paper introduces DeepCons, a deep learning model to predict evolutionary conservation in DNA sequences and identifies both known and novel functional elements.

The paper uses a convolutional neural network to predict HLA class I-peptide binding and identifies 15 genes with potential for self-binding.

This paper proposes deep multi-instance networks for whole mammogram classification without requiring manual annotations, showing robustness on the INbreast dataset.

An end-to-end fully convolutional network with a positional prior, conditional random field structural learning, and adversarial training segments mammographic masses on INbreast and DDSM-BCRP.