HomNet: Chromosomal Structural Abnormality Diagnosis by Homologous Similarity

Juren Li$^{1\bullet}$, Fanzhe Fu$^{1\bullet}$, Ran Wei$^2$, Yifei Sun$^1$, Zeyu Lai$^1$, Ning Song$^2$, Xin Chen$^3$, Yang Yang$^{1*}$. ($^\bullet$ Equal contribution; $^*$ Correspondence)

$^1$ College of Computer Science and Technology, Zhejiang University

$^2$ Hangzhou Diagens Biotechnology Co., Ltd., China

$^3$ Zhejiang University-University of Illinois Urbana-Champaign Institute

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This repository provides the code of "Chromosomal Structural Abnormality Diagnosis by Homologous Similarity", which is accepted by the KDD'24 ADS track (20% acceptance rate).

HomNet

This work proposes a method, HomNet, for diagnosing chromosomal structural abnormalities by leveraging homologous similarity. By aligning homologous chromosomes and considering information from multiple pairs simultaneously, HomNet can detect chromosomes with structural abnormalities.

Code is no longer public

Due to commercial considerations, the code is no longer available publicly.

Contact

If you have any question about the paper, feel free to contact me through Email: jrlee@zju.edu.cn.

Citation

If you find HomNet useful in your research or application, please kindly cite:

@inproceedings{
    juren2024chromosomal, 
    title={Chromosomal Structural Abnormality Diagnosis by Homologous Similarity}, author={Juren L, Fanzhe F, Ran W, et al.}, 
    booktitle={Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining}, year={2024},
    note={Accepted}
    }