Ensembling is a widely used and effective technique in Kaggle competitions for boosting final scores. From simple averaging to more sophisticated numerical operations, combining predictions from multiple models is a common practice that often leads to better generalization and robustness.
\nHowever, in competitions like this one—where the output consists of 3D coordinates (xyz)—ensembling becomes a non-trivial task. While it's tempting to simply average the coordinates from multiple predictions (e.g., using different random seeds such as 42 and 43), this approach can be fundamentally flawed. The reason is that the 3D structures are essentially spatial snapshots and may not be aligned in the same coordinate space. Directly averaging unaligned coordinates may produce physically meaningless or distorted structures.
\nIn fact, the evaluation metric used in this competition, TM-score, inherently accounts for this issue by performing structure alignment before calculating similarity. (see evaluation section)
\nhttps://www.kaggle.com/competitions/stanford-rna-3d-folding/overview/evaluation
\nThis clearly implies that alignment is a prerequisite for meaningful comparison or combination of 3D structures.
Generate predictions
\nObtain multiple 3D structure predictions from different models or different random seeds.
Choose a reference structure
\nSelect one structure as the alignment reference—this could be the best-performing model's output or a medoid based on pairwise TM-scores.
Align all other structures
\nUse a structural alignment algorithm (commonly the Kabsch algorithm) to rotate and translate all other predictions to match the reference structure in 3D space.
Average the aligned coordinates
\nWith all predictions now in the same coordinate frame, averaging their coordinates becomes geometrically meaningful.
Post-processing
\nOptionally refine the averaged structure using geometry checks (e.g., bond lengths, base-pairing constraints) or energy minimization.
I haven’t studied this topic in depth yet, but I believe it’s a crucial consideration for this kind of task. That’s why I’ve written it down here—for future reference and further investigation. I hope there would be creative solutions from Kagglers :)
", "messages": [ { "id": 3195222, "postDate": "2025-05-06T19:21:52.700Z", "content": "Ensembling is a widely used and effective technique in Kaggle competitions for boosting final scores. From simple averaging to more sophisticated numerical operations, combining predictions from multiple models is a common practice that often leads to better generalization and robustness.
\nHowever, in competitions like this one—where the output consists of 3D coordinates (xyz)—ensembling becomes a non-trivial task. While it's tempting to simply average the coordinates from multiple predictions (e.g., using different random seeds such as 42 and 43), this approach can be fundamentally flawed. The reason is that the 3D structures are essentially spatial snapshots and may not be aligned in the same coordinate space. Directly averaging unaligned coordinates may produce physically meaningless or distorted structures.
\nIn fact, the evaluation metric used in this competition, TM-score, inherently accounts for this issue by performing structure alignment before calculating similarity. (see evaluation section)
\nhttps://www.kaggle.com/competitions/stanford-rna-3d-folding/overview/evaluation
\nThis clearly implies that alignment is a prerequisite for meaningful comparison or combination of 3D structures.
Generate predictions
\nObtain multiple 3D structure predictions from different models or different random seeds.
Choose a reference structure
\nSelect one structure as the alignment reference—this could be the best-performing model's output or a medoid based on pairwise TM-scores.
Align all other structures
\nUse a structural alignment algorithm (commonly the Kabsch algorithm) to rotate and translate all other predictions to match the reference structure in 3D space.
Average the aligned coordinates
\nWith all predictions now in the same coordinate frame, averaging their coordinates becomes geometrically meaningful.
Post-processing
\nOptionally refine the averaged structure using geometry checks (e.g., bond lengths, base-pairing constraints) or energy minimization.
I haven’t studied this topic in depth yet, but I believe it’s a crucial consideration for this kind of task. That’s why I’ve written it down here—for future reference and further investigation. I hope there would be creative solutions from Kagglers :)
", "rawMarkdown": "Ensembling is a widely used and effective technique in Kaggle competitions for boosting final scores. From simple averaging to more sophisticated numerical operations, combining predictions from multiple models is a common practice that often leads to better generalization and robustness.\n\nHowever, in competitions like this one—where the output consists of **3D coordinates (xyz)**—ensembling becomes a non-trivial task. While it's tempting to simply average the coordinates from multiple predictions (e.g., using different random seeds such as 42 and 43), this approach can be **fundamentally flawed**. The reason is that the 3D structures are essentially spatial snapshots and may not be aligned in the same coordinate space. Directly averaging unaligned coordinates may produce physically meaningless or distorted structures.\n\nIn fact, the evaluation metric used in this competition, **TM-score**, inherently accounts for this issue by performing **structure alignment** before calculating similarity. (see evaluation section) \nhttps://www.kaggle.com/competitions/stanford-rna-3d-folding/overview/evaluation\nThis clearly implies that **alignment is a prerequisite for meaningful comparison or combination** of 3D structures.\n\n# A Practical Pipeline in my mind for Ensemble with 3D Coordinates\n\n1. Generate predictions\n Obtain multiple 3D structure predictions from different models or different random seeds.\n\n2. Choose a reference structure\n Select one structure as the alignment reference—this could be the best-performing model's output or a medoid based on pairwise TM-scores.\n\n3. Align all other structures\n Use a structural alignment algorithm (commonly the Kabsch algorithm) to rotate and translate all other predictions to match the reference structure in 3D space.\n\n4. Average the aligned coordinates\n With all predictions now in the same coordinate frame, averaging their coordinates becomes geometrically meaningful.\n\n5. Post-processing\n Optionally refine the averaged structure using geometry checks (e.g., bond lengths, base-pairing constraints) or energy minimization.\n\nI haven’t studied this topic in depth yet, but I believe it’s a crucial consideration for this kind of task. That’s why I’ve written it down here—for future reference and further investigation. I hope there would be creative solutions from Kagglers :) \n", "votes": 14 } ], "comments": [], "raw_markdown_by_id": { "3195222": "Ensembling is a widely used and effective technique in Kaggle competitions for boosting final scores. From simple averaging to more sophisticated numerical operations, combining predictions from multiple models is a common practice that often leads to better generalization and robustness.\n\nHowever, in competitions like this one—where the output consists of **3D coordinates (xyz)**—ensembling becomes a non-trivial task. While it's tempting to simply average the coordinates from multiple predictions (e.g., using different random seeds such as 42 and 43), this approach can be **fundamentally flawed**. The reason is that the 3D structures are essentially spatial snapshots and may not be aligned in the same coordinate space. Directly averaging unaligned coordinates may produce physically meaningless or distorted structures.\n\nIn fact, the evaluation metric used in this competition, **TM-score**, inherently accounts for this issue by performing **structure alignment** before calculating similarity. (see evaluation section) \nhttps://www.kaggle.com/competitions/stanford-rna-3d-folding/overview/evaluation\nThis clearly implies that **alignment is a prerequisite for meaningful comparison or combination** of 3D structures.\n\n# A Practical Pipeline in my mind for Ensemble with 3D Coordinates\n\n1. Generate predictions\n Obtain multiple 3D structure predictions from different models or different random seeds.\n\n2. Choose a reference structure\n Select one structure as the alignment reference—this could be the best-performing model's output or a medoid based on pairwise TM-scores.\n\n3. Align all other structures\n Use a structural alignment algorithm (commonly the Kabsch algorithm) to rotate and translate all other predictions to match the reference structure in 3D space.\n\n4. Average the aligned coordinates\n With all predictions now in the same coordinate frame, averaging their coordinates becomes geometrically meaningful.\n\n5. Post-processing\n Optionally refine the averaged structure using geometry checks (e.g., bond lengths, base-pairing constraints) or energy minimization.\n\nI haven’t studied this topic in depth yet, but I believe it’s a crucial consideration for this kind of task. That’s why I’ve written it down here—for future reference and further investigation. I hope there would be creative solutions from Kagglers :) \n" } }