文章目录Deep Learning for Sequence-to-Relation Reasoning1. Tool 1: RNA/DNA Contact Map PredictorPurposeInputOutputKey CapabilitiesTypical Use Cases2. Tool 2: Graph Coloring Conflict DetectorPurposeInputOutputKey CapabilitiesTypical Use Cases3. Shared Architecture Features4. Key Formula (Simplified)5. Important Notes6. SummaryDeep Learning for Sequence-to-Relation ReasoningThis document provides a high‑level description of two Python‑based tools built on a shared Transformer architecture. Both tools solverelation prediction problemsfrom sequential inputs, but each addresses a different domain.1. Tool 1: RNA/DNA Contact Map Predictor(File:train_dna.py– inferred name)PurposePredicts which positions in a nucleotide sequence are spatially close (form contacts) in the folded 3D structure. This is a core step in RNA secondary/tertiary structure prediction.InputA sequence of symbols representing nucleotides (e.g., A, U, G, C for RNA).Length is fixed (e.g., 64 positions).OutputAcontact matrix– a square grid where each cell indicates the probability that the two corresponding nucleotides are paired/bound in space.Key CapabilitiesLearns from synthetic sequences with simulated contacts.Can be retrained on real experimental data (e.g., from PDB or RNAcentral).Provides fast, approximate contact predictions without molecular dynamics simulations.Typical Use CasesRapid screening of RNA folding hypotheses.Generating contact priors for downstream structure reconstruction.Educational/research prototyping in computational biology.2. Tool 2: Graph Coloring Conflict Detector(File:train_coloring.py)PurposeGiven a color assignment to the vertices of an unknown graph, this tool detectsconflicts– pairs of adjacent vertices that share the same color. It implicitly learns the graph’s edge structure from training examples.InputA sequence of color labels (integers) for a fixed number of vertices (default 20).The number of available colors is predefined (default 3).OutputAconflict matrix– a square grid where each cell indicates the predicted probability that the two vertices are connected by an edgeandhave the same color.Key CapabilitiesWorks without explicit graph input (only colors are needed).Achieves high recall (few missed conflicts) on graphs similar to its training distribution (Erdős–Rényi random graphs).Can serve as a rapid validity checker for candidate colorings.Typical Use CasesChecking the legality of coloring solutions in combinatorial problems.Guiding search algorithms (e.g., genetic algorithms) by providing a conflict score as a fitness metric.Educational demonstrations of constraint satisfaction and neural reasoning.3. Shared Architecture FeaturesBoth tools use the same underlying model – a5‑layer folding‑aware Transformer– but are trained on different data distributions to learn different rules:Thecontact predictorlearns physical/chemical rules (hydrogen bonding, distance decay).Theconflict detectorlearns logical rules (adjacency same color → error).This design makes the framework adaptable to any pairwise constraint problem.4. Key Formula (Simplified)For a given input sequenceSof lengthN, the model computes a relation matrixRof sizeN×N, where each entry ( R_{ij} ) is a probability (or score) indicating how strongly the positions ( i ) and ( j ) satisfy the target relation.The specific meaning of the relation depends on the training data:Contact predictor: ( R_{ij} ) probability that nucleotides ( i ) and ( j ) form a physical contact (hydrogen bond).Conflict detector: ( R_{ij} ) probability that vertices ( i ) and ( j ) are adjacentandhave the same color (conflict).In essence, the model learns a mapping:Sequence ⟶ Relation Matrix \text{Sequence} \longrightarrow \text{Relation Matrix}Sequence⟶Relation Matrixwithout requiring explicit prior knowledge of the underlying structure (e.g., graph adjacency or physical forces). All necessary relational information is inferred from training examples of (sequence, relation) pairs.5. Important NotesData Generation:Both tools use synthetic data for training. For real‑world applications, replace the data generators with actual domain‑specific datasets.Generalization:The conflict detector trained on random graphsdoes notgeneralize well to structured graphs like grids or maps. A separate script (train_grid_coloring.py) is provided for grid‑based problems.Flexibility:The model skeleton is general – you can adapt it to other pairwise constraint problems (e.g., Sudoku validation, N‑Queens) by modifying only the data generation logic.6. SummaryToolInputOutputDomainContact PredictorNucleotide sequenceContact (pairing) matrixBioinformaticsConflict DetectorColor sequenceConflict matrixGraph theory / Constraint satisfactionBoth tools are lightweight (~850k parameters), support GPU acceleration and mixed‑precision training, and include checkpointing and model export for reproducible workflows.
认知神经科学研究报告【20260105】
文章目录Deep Learning for Sequence-to-Relation Reasoning1. Tool 1: RNA/DNA Contact Map PredictorPurposeInputOutputKey CapabilitiesTypical Use Cases2. Tool 2: Graph Coloring Conflict DetectorPurposeInputOutputKey CapabilitiesTypical Use Cases3. Shared Architecture Features4. Key Formula (Simplified)5. Important Notes6. SummaryDeep Learning for Sequence-to-Relation ReasoningThis document provides a high‑level description of two Python‑based tools built on a shared Transformer architecture. Both tools solverelation prediction problemsfrom sequential inputs, but each addresses a different domain.1. Tool 1: RNA/DNA Contact Map Predictor(File:train_dna.py– inferred name)PurposePredicts which positions in a nucleotide sequence are spatially close (form contacts) in the folded 3D structure. This is a core step in RNA secondary/tertiary structure prediction.InputA sequence of symbols representing nucleotides (e.g., A, U, G, C for RNA).Length is fixed (e.g., 64 positions).OutputAcontact matrix– a square grid where each cell indicates the probability that the two corresponding nucleotides are paired/bound in space.Key CapabilitiesLearns from synthetic sequences with simulated contacts.Can be retrained on real experimental data (e.g., from PDB or RNAcentral).Provides fast, approximate contact predictions without molecular dynamics simulations.Typical Use CasesRapid screening of RNA folding hypotheses.Generating contact priors for downstream structure reconstruction.Educational/research prototyping in computational biology.2. Tool 2: Graph Coloring Conflict Detector(File:train_coloring.py)PurposeGiven a color assignment to the vertices of an unknown graph, this tool detectsconflicts– pairs of adjacent vertices that share the same color. It implicitly learns the graph’s edge structure from training examples.InputA sequence of color labels (integers) for a fixed number of vertices (default 20).The number of available colors is predefined (default 3).OutputAconflict matrix– a square grid where each cell indicates the predicted probability that the two vertices are connected by an edgeandhave the same color.Key CapabilitiesWorks without explicit graph input (only colors are needed).Achieves high recall (few missed conflicts) on graphs similar to its training distribution (Erdős–Rényi random graphs).Can serve as a rapid validity checker for candidate colorings.Typical Use CasesChecking the legality of coloring solutions in combinatorial problems.Guiding search algorithms (e.g., genetic algorithms) by providing a conflict score as a fitness metric.Educational demonstrations of constraint satisfaction and neural reasoning.3. Shared Architecture FeaturesBoth tools use the same underlying model – a5‑layer folding‑aware Transformer– but are trained on different data distributions to learn different rules:Thecontact predictorlearns physical/chemical rules (hydrogen bonding, distance decay).Theconflict detectorlearns logical rules (adjacency same color → error).This design makes the framework adaptable to any pairwise constraint problem.4. Key Formula (Simplified)For a given input sequenceSof lengthN, the model computes a relation matrixRof sizeN×N, where each entry ( R_{ij} ) is a probability (or score) indicating how strongly the positions ( i ) and ( j ) satisfy the target relation.The specific meaning of the relation depends on the training data:Contact predictor: ( R_{ij} ) probability that nucleotides ( i ) and ( j ) form a physical contact (hydrogen bond).Conflict detector: ( R_{ij} ) probability that vertices ( i ) and ( j ) are adjacentandhave the same color (conflict).In essence, the model learns a mapping:Sequence ⟶ Relation Matrix \text{Sequence} \longrightarrow \text{Relation Matrix}Sequence⟶Relation Matrixwithout requiring explicit prior knowledge of the underlying structure (e.g., graph adjacency or physical forces). All necessary relational information is inferred from training examples of (sequence, relation) pairs.5. Important NotesData Generation:Both tools use synthetic data for training. For real‑world applications, replace the data generators with actual domain‑specific datasets.Generalization:The conflict detector trained on random graphsdoes notgeneralize well to structured graphs like grids or maps. A separate script (train_grid_coloring.py) is provided for grid‑based problems.Flexibility:The model skeleton is general – you can adapt it to other pairwise constraint problems (e.g., Sudoku validation, N‑Queens) by modifying only the data generation logic.6. SummaryToolInputOutputDomainContact PredictorNucleotide sequenceContact (pairing) matrixBioinformaticsConflict DetectorColor sequenceConflict matrixGraph theory / Constraint satisfactionBoth tools are lightweight (~850k parameters), support GPU acceleration and mixed‑precision training, and include checkpointing and model export for reproducible workflows.
