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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.08204 |
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| _version_ | 1866916319957352448 |
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| author | Li, Juren Fu, Fanzhe Wei, Ran Sun, Yifei Lai, Zeyu Song, Ning Chen, Xin Yang, Yang |
| author_facet | Li, Juren Fu, Fanzhe Wei, Ran Sun, Yifei Lai, Zeyu Song, Ning Chen, Xin Yang, Yang |
| contents | Pathogenic chromosome abnormalities are very common among the general population. While numerical chromosome abnormalities can be quickly and precisely detected, structural chromosome abnormalities are far more complex and typically require considerable efforts by human experts for identification. This paper focuses on investigating the modeling of chromosome features and the identification of chromosomes with structural abnormalities. Most existing data-driven methods concentrate on a single chromosome and consider each chromosome independently, overlooking the crucial aspect of homologous chromosomes. In normal cases, homologous chromosomes share identical structures, with the exception that one of them is abnormal. Therefore, we propose an adaptive method to align homologous chromosomes and diagnose structural abnormalities through homologous similarity. Inspired by the process of human expert diagnosis, we incorporate information from multiple pairs of homologous chromosomes simultaneously, aiming to reduce noise disturbance and improve prediction performance. Extensive experiments on real-world datasets validate the effectiveness of our model compared to baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_08204 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Chromosomal Structural Abnormality Diagnosis by Homologous Similarity Li, Juren Fu, Fanzhe Wei, Ran Sun, Yifei Lai, Zeyu Song, Ning Chen, Xin Yang, Yang Artificial Intelligence Pathogenic chromosome abnormalities are very common among the general population. While numerical chromosome abnormalities can be quickly and precisely detected, structural chromosome abnormalities are far more complex and typically require considerable efforts by human experts for identification. This paper focuses on investigating the modeling of chromosome features and the identification of chromosomes with structural abnormalities. Most existing data-driven methods concentrate on a single chromosome and consider each chromosome independently, overlooking the crucial aspect of homologous chromosomes. In normal cases, homologous chromosomes share identical structures, with the exception that one of them is abnormal. Therefore, we propose an adaptive method to align homologous chromosomes and diagnose structural abnormalities through homologous similarity. Inspired by the process of human expert diagnosis, we incorporate information from multiple pairs of homologous chromosomes simultaneously, aiming to reduce noise disturbance and improve prediction performance. Extensive experiments on real-world datasets validate the effectiveness of our model compared to baselines. |
| title | Chromosomal Structural Abnormality Diagnosis by Homologous Similarity |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2407.08204 |