Summary of Deep-ace: Lstm-based Prokaryotic Lysine Acetylation Site Predictor, by Maham Ilyas et al.
Deep-Ace: LSTM-based Prokaryotic Lysine Acetylation Site Predictor
by Maham Ilyas, Abida Yasmeen, Yaser Daanial Khan, Arif Mahmood
First submitted to arxiv on: 13 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cell Behavior (q-bio.CB)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Deep-Ace framework uses a Long-Short-Term-Memory (LSTM) network to identify acetylation sites on lysine residues, which is crucial for understanding disease pathology and cell biology. This approach improves upon previous machine learning-based methods by incorporating long-term relationships within sequences, leading to more accurate predictions. The LSTM network is used to extract deep features and predict K-Ace sites in eight prokaryotic species, achieving state-of-the-art accuracy of 0.80, 0.79, 0.71, 0.75, 0.80, 0.83, 0.756, and 0.82 respectively. This method has the potential to be adapted for eukaryotic systems and could serve as a tool for disease diagnosis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep-Ace is a new way to find acetylation sites on proteins. It uses a special kind of artificial intelligence called Long-Short-Term-Memory (LSTM) to look at long stretches of protein sequence. This helps the model understand what makes some parts of the sequence important for identifying where acetylation happens. The team tested Deep-Ace on eight different types of bacteria and found that it worked really well, making accurate predictions in all cases. This new method could be useful for diagnosing diseases and understanding how proteins work. |
Keywords
* Artificial intelligence * Lstm * Machine learning
