INDELpred: Improving the prediction and interpretation of indel pathogenicity within the clinical genome

Yilin Wei*, Tongda Zhang*, Bangyao Wang, Xiaosen Jiang, Mingyan Fang, Xin Jin, Yong Bai^#

Human Genetics and Genomics Advances (HGG Advances) 2024 Journal

Small insertions and deletions (indels) are critical yet challenging genetic variations with significant clinical implications. In this paper, we developed INDELpred, a machine-learning-based predictive model for discerning pathogenic from benign indels. We envisage INDELpred as a desirable tool for the detection of pathogenic indels within large-scale genomic datasets, thereby enhancing the precision of genetic diagnoses in clinical settings.

[Code] [pdf]

A blood cell classification method based on MAE and active learning

Qinghang Lu*, Bangyao Wang*, Quanhui He, Qingmao Zhang, Liang Guo, Jiaming Li, Jie Li, Qiongxiong Ma^#

Biomedical Signal Processing and Control 2023 Journal

Cell morphology analysis is a crucial diagnostic tool for identifying blood diseases. This paper proposes a blood cell classification method based on Masked Autoencoder (MAE) and active learning (AL) to select the most valuable samples for labeling. The proposed approach achieves comparable classification performance to SOTA method based on ResNeXt when utilizing only 20% of the labeled data.

[Link]