{
  "id": 566208,
  "title": "Suggestions for My GCN Approach in Stanford RNA 3D Folding?",
  "url": "/competitions/stanford-rna-3d-folding/discussion/566208",
  "author_name": "",
  "post_date": "2025-03-04T10:36:40.955280900Z",
  "votes": 6,
  "comment_count": 3,
  "views": 0,
  "content": "<p>I’m tackling the Stanford RNA 3D Folding Competition with a Graph Convolutional Network (GCN) and could use your wisdom! Here’s my approach—any tips to level it up?</p>\n<p>My GCN Setup<br>\nPreprocessing: Nucleotides as nodes (A=1, C=2, G=3, U=4), backbone edges only, padded to max length (4298).<br>\nModel: spektral-based GCN with Embedding (16 dim), 2x GCNConv (64 units, ReLU), Dropout (0.2), and Dense layer for 3D coords.<br>\nLoss: Masked MSE for padded regions.<br>\nTraining: Adam + early stopping.<br>\nSubmission: Predict coordss, replicate 5x for submission.</p>",
  "messages": [
    {
      "id": "3140151",
      "postDate": "03/04/2025 10:36:40",
      "content": "<p>I’m tackling the Stanford RNA 3D Folding Competition with a Graph Convolutional Network (GCN) and could use your wisdom! Here’s my approach—any tips to level it up?</p>\n<p>My GCN Setup<br>\nPreprocessing: Nucleotides as nodes (A=1, C=2, G=3, U=4), backbone edges only, padded to max length (4298).<br>\nModel: spektral-based GCN with Embedding (16 dim), 2x GCNConv (64 units, ReLU), Dropout (0.2), and Dense layer for 3D coords.<br>\nLoss: Masked MSE for padded regions.<br>\nTraining: Adam + early stopping.<br>\nSubmission: Predict coordss, replicate 5x for submission.</p>",
      "rawMarkdown": "I’m tackling the Stanford RNA 3D Folding Competition with a Graph Convolutional Network (GCN) and could use your wisdom! Here’s my approach—any tips to level it up?\n\nMy GCN Setup\nPreprocessing: Nucleotides as nodes (A=1, C=2, G=3, U=4), backbone edges only, padded to max length (4298).\nModel: spektral-based GCN with Embedding (16 dim), 2x GCNConv (64 units, ReLU), Dropout (0.2), and Dense layer for 3D coords.\nLoss: Masked MSE for padded regions.\nTraining: Adam + early stopping.\nSubmission: Predict coordss, replicate 5x for submission.",
      "votes": null
    },
    {
      "id": "3140351",
      "postDate": "03/04/2025 14:03:33",
      "content": "<p>Hi, <br>\nThis is a good starting point. However, one of the challenges here is that the 3D structure also depends on interactions between nucleotides that are far apart in the sequence. So I wonder if this architecture would be able to model it correctly.</p>",
      "rawMarkdown": "Hi, \nThis is a good starting point. However, one of the challenges here is that the 3D structure also depends on interactions between nucleotides that are far apart in the sequence. So I wonder if this architecture would be able to model it correctly.",
      "votes": null
    },
    {
      "id": "3140627",
      "postDate": "03/04/2025 19:30:50",
      "content": "<p>You may want to encode two additional concepts.</p>\n<ul>\n<li>In a vast majority of cases, RNA base-pairing is done according to what's known as Watson-Crick rules: A pairs with U (and <em>vice versa</em>) while G pairs with C. Note that G can pair with U as well, but A-U is the prefered  pairing.</li>\n<li>Co-variance means that mutating one base in a base pair leads to a matching mutation in the other base. In the image below, look through the two boxed columns, from which nucleotides are base-pairing with each other. Whenever there is a C on the left, there will be a G on the right. Same for the A-U pattern. Mutations are normally not good, but double-mutations, especially of the matching kind, are acceptable when it comes to structured RNA molecules. That's what co-variance means: a mutation (variation) in a single base is not good, but add a matching mutation (co-variation) and it becomes fine.</li>\n</ul>\n<p><img src=\"https://i.ibb.co/gb6KmQJX/co-variance.png\" alt=\"RNA co-variance\"></p>",
      "rawMarkdown": "You may want to encode two additional concepts.\n\n- In a vast majority of cases, RNA base-pairing is done according to what's known as Watson-Crick rules: A pairs with U (and *vice versa*) while G pairs with C. Note that G can pair with U as well, but A-U is the prefered  pairing.\n- Co-variance means that mutating one base in a base pair leads to a matching mutation in the other base. In the image below, look through the two boxed columns, from which nucleotides are base-pairing with each other. Whenever there is a C on the left, there will be a G on the right. Same for the A-U pattern. Mutations are normally not good, but double-mutations, especially of the matching kind, are acceptable when it comes to structured RNA molecules. That's what co-variance means: a mutation (variation) in a single base is not good, but add a matching mutation (co-variation) and it becomes fine.\n\n![RNA co-variance](https://i.ibb.co/gb6KmQJX/co-variance.png)",
      "votes": null
    },
    {
      "id": "3141238",
      "postDate": "03/05/2025 11:18:11",
      "content": "<p>I implemented a quick version here <a href=\"https://www.kaggle.com/code/salmanahmedtamu/a-simple-gnn-implementation\" target=\"_blank\">GNN RNA Notebook</a></p>",
      "rawMarkdown": "I implemented a quick version here [GNN RNA Notebook](https://www.kaggle.com/code/salmanahmedtamu/a-simple-gnn-implementation)",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 3140351,
      "author_name": "dalloliogm",
      "author_url": "",
      "post_date": "03/04/2025 14:03:33",
      "content": "<p>Hi, <br>\nThis is a good starting point. However, one of the challenges here is that the 3D structure also depends on interactions between nucleotides that are far apart in the sequence. So I wonder if this architecture would be able to model it correctly.</p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 3140627,
      "author_name": "tilii7",
      "author_url": "",
      "post_date": "03/04/2025 19:30:50",
      "content": "<p>You may want to encode two additional concepts.</p>\n<ul>\n<li>In a vast majority of cases, RNA base-pairing is done according to what's known as Watson-Crick rules: A pairs with U (and <em>vice versa</em>) while G pairs with C. Note that G can pair with U as well, but A-U is the prefered  pairing.</li>\n<li>Co-variance means that mutating one base in a base pair leads to a matching mutation in the other base. In the image below, look through the two boxed columns, from which nucleotides are base-pairing with each other. Whenever there is a C on the left, there will be a G on the right. Same for the A-U pattern. Mutations are normally not good, but double-mutations, especially of the matching kind, are acceptable when it comes to structured RNA molecules. That's what co-variance means: a mutation (variation) in a single base is not good, but add a matching mutation (co-variation) and it becomes fine.</li>\n</ul>\n<p><img src=\"https://i.ibb.co/gb6KmQJX/co-variance.png\" alt=\"RNA co-variance\"></p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 3141238,
      "author_name": "salmanahmedtamu",
      "author_url": "",
      "post_date": "03/05/2025 11:18:11",
      "content": "<p>I implemented a quick version here <a href=\"https://www.kaggle.com/code/salmanahmedtamu/a-simple-gnn-implementation\" target=\"_blank\">GNN RNA Notebook</a></p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "3140151": "I’m tackling the Stanford RNA 3D Folding Competition with a Graph Convolutional Network (GCN) and could use your wisdom! Here’s my approach—any tips to level it up?\n\nMy GCN Setup\nPreprocessing: Nucleotides as nodes (A=1, C=2, G=3, U=4), backbone edges only, padded to max length (4298).\nModel: spektral-based GCN with Embedding (16 dim), 2x GCNConv (64 units, ReLU), Dropout (0.2), and Dense layer for 3D coords.\nLoss: Masked MSE for padded regions.\nTraining: Adam + early stopping.\nSubmission: Predict coordss, replicate 5x for submission.",
    "3140351": "Hi, \nThis is a good starting point. However, one of the challenges here is that the 3D structure also depends on interactions between nucleotides that are far apart in the sequence. So I wonder if this architecture would be able to model it correctly.",
    "3140627": "You may want to encode two additional concepts.\n\n- In a vast majority of cases, RNA base-pairing is done according to what's known as Watson-Crick rules: A pairs with U (and *vice versa*) while G pairs with C. Note that G can pair with U as well, but A-U is the prefered  pairing.\n- Co-variance means that mutating one base in a base pair leads to a matching mutation in the other base. In the image below, look through the two boxed columns, from which nucleotides are base-pairing with each other. Whenever there is a C on the left, there will be a G on the right. Same for the A-U pattern. Mutations are normally not good, but double-mutations, especially of the matching kind, are acceptable when it comes to structured RNA molecules. That's what co-variance means: a mutation (variation) in a single base is not good, but add a matching mutation (co-variation) and it becomes fine.\n\n![RNA co-variance](https://i.ibb.co/gb6KmQJX/co-variance.png)",
    "3141238": "I implemented a quick version here [GNN RNA Notebook](https://www.kaggle.com/code/salmanahmedtamu/a-simple-gnn-implementation)"
  },
  "source": "meta"
}