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| Title: | MCIENet : 基於 CNN 的 DNA 序列多尺度資訊提取模型用於三維染色質交互作用預測
MCIENet : Multi-scale CNN-based Information Extraction from DNA Sequences for 3D chromatin interactions Prediction |
| Authors: | 何彥南
Ho, Yen-Nan |
| Contributors: | 張家銘
Chang, Jia-Ming
何彥南
Ho, Yen-Nan |
| Keywords: | 染色質環預測
深度學習
DNA序列
Inception架構
三維基因組學
Chromatin loop prediction
Deep learning
DNA sequence
Inception architecture
3D genomics |
| Date: | 2024 |
| Issue Date: | 2024-12-02 11:21:52 (UTC+8) |
| Abstract: | 染色質三維結構對於基因調控具有重要影響，染色質環(Chromatin loops)作為其基本單位，其結構和功能在不同細胞類型中存在差異，研究染色質三維結構可以幫助科學家們進一步理解細胞功能與運作。可是實際透過儀器與實體實驗去獲取三維結構資訊需要較高的設備、時間與樣本取得上的成本，也因為如此，許多計算預測方法被提出來，目的是透過 DNA 序列資訊、蛋白質或是開放染色質(open chromatin)等資訊去預測是否存在 CTCF 環的結構，而其中僅使用 DNA 序列資訊進行預測是最為困難的任務。本研究提出了一種新型深度學習模型 MCIENet (Multi-scale CNN-based Information Extraction Net)，MCIENet採用Inception架構，對DNA序列進行多尺度特徵提取。我們在正常細胞 (GM12878) 與癌症細胞 (Helas3) 上進行了驗證，結果表明 MCIENet在不同細胞類型上均取得了優異的預測性能，尤其是在較長的DNA序列作為輸入時效果顯著。並揭示了在預測不同細胞類型時，在模型模型架構的設計上是存在差異性的。此外，我們使用 DNABERT2-512 基於大量基因資料所訓練的預訓練模型進行微調，發現在癌症細胞(Helas3) 上的效果很差，證實了這種基於大量基因資訊訓練的預訓練模型無法應用在所有種類的細胞結構預測上。此外，透過 DeepLIFT 可解釋性分析，進一步去觀察 MCIENet 的效果，發現其在長序列輸入時對於細節的捕捉更優秀，此外本研究還透過可解釋分析證實了 anchor-base 方法在錨點中心偏移時所存在的問題，導致其在後續使用上缺乏穩定性，且有所限制。
The three-dimensional structure of chromatin plays a crucial role in gene regulation. Chromatin loops, as the fundamental units of chromatin structure, exhibit diverse structures and functions across different cell types. Investigating the three-dimensional chromatin structure can help scientists gain a deeper understanding of cellular functions and operations. However, experimentally obtaining three-dimensional structural information through instruments and physical experiments requires substantial equipment, time, and sample acquisition costs. Consequently, numerous computational prediction methods have been proposed to predict CTCF loops using DNA sequence information, protein information, or open chromatin information. Among these methods, prediction solely based on DNA sequence information is the most challenging task. In this study, we propose a novel deep learning model, MCIENet (Multi-scale CNN-based Information Extraction Net), which employs an Inception architecture to extract multi-scale features from DNA sequences. We validated MCIENet on normal cells (GM12878) and cancer cells (Helas3). The results demonstrate that MCIENet performs better prediction on different cell types, especially when longer DNA sequences are used as input. Furthermore, our findings reveal differences in model architecture design when predicting different cell types. Additionally, we fine-tuned the DNABERT2-512 pre-trained model, which was trained on a large amount of genetic data, and found that its performance on cancer cells (Helas3) was poor. This confirms that pre-trained models trained on large amounts of genetic information cannot be applied to all types of cell structure prediction. Moreover, through DeepLIFT interpretability analysis, we further observed that MCIENet excels at capturing details when inputting long sequences. This study also confirms, through interpretability analysis, the limitations of anchor-based methods when the anchor center is shifted, leading to a lack of stability and restrictions in subsequent applications. |
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| Description: | 碩士
國立政治大學
資訊科學系
110753202 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0110753202 |
| Data Type: | thesis |
| Appears in Collections: | [資訊科學系] 學位論文
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