MotiFiesta learns network motifs efficiently.
problem Efficiently identifying network motifs in real-world datasets.
method Formulated as a node labelling task, using machine learning.
result Demonstrated efficient motif discovery on challenging datasets.
PGEL learns embeddings to diversify protein motifs while maintaining biological function.
problem Generating diverse protein structures while preserving biological function.
method Embedding learning framework that enhances motif diversity in a diffusion model's frozen denoiser.
result PGEL achieves greater structural diversity, better designability, and improved self-consistency compared to partial diffusion.
Proposes a motif-preserving Graph Neural Network for financial default prediction.
problem Weak connectivity and imbalance in motif patterns in graph-based models.
method MotifGNN with curriculum learning to capture higher-order topology structures.
result Significantly improved financial default prediction accuracy on public and industrial datasets.
Method identifies multi-scale behavioral motifs in rat locomotion.
problem Challenging to identify behavioral motifs across different time scales.
method Preprocessing and parallel HMM training for different scales.
result Rat locomotion is composed of distinct motifs from second to minute scale.
New method learns diverse protein scaffolds for motif design.
problem Designing long, diverse protein scaffolds for specific motifs.
method E(3)-equivariant graph neural network for diffusion modeling.
result First to guarantee conditional sampling from diffusion models.
Model improves robustness of neural network sequences without transition failures.
problem Learning and generating complex sequences of motor primitives without interference.
method Inspired by thalamocortical circuit, uses specific module for motif transitions.
result Improved robustness of sequence generation with no transition failures.
MASA discovers motifs in noisy time series data.
problem Discovering common sequences of states in noisy time series data.
method MASA uses an expectation-maximization approach to solve a large optimization problem.
result MASA outperforms state-of-the-art baselines by up to 38.2%.
Method finds motifs in knowledge graphs, revealing their structure.
problem Identifying meaningful subunits in knowledge graphs.
method Inspired by simple graphs, the approach uses compression techniques to find motifs.
result Motifs found reflect the basic structure of the graph.
Graph classification improved with motif counts and graphon theory.
problem Classifying large graphs with high accuracy.
method Using motif homomorphisms and graphon theory to provide bounds and a classifier.
result Explicit quantitative bounds for graph classification under noise.
New methods for faster ranking and link prediction using higher-order motifs.
problem Real-time ranking and link prediction in applications like web search.
method Higher-order ranking and link prediction methods based on closing higher-order network motifs.
result The methods are faster and more efficient than existing methods based on closing triangles.
Improved scaffold generation for protein motifs using SE(3) flow matching.
problem Lack of structural diversity in generated scaffolds for protein motifs.
method Extended FrameFlow for motif-scaffolding with motif amortization and motif guidance.
result 2.5 times more designable and unique motif-scaffolds compared to state-of-the-art.
When analyzing the genome, researchers have discovered that proteins bind to DNA based on certain patterns of the DNA sequence known as "motifs". However, it is difficult to manually construct motifs due to their complexity. Recently, externally learned memory models have proven to be effective methods for reasoning ov…
Synthetic Petri Dish predicts neural architecture performance faster.
problem Expensive NAS evaluation process with ground-truth data.
method Instantiates motifs in small networks, evaluates with few synthetic samples.
result Significantly higher accuracy in predicting motif performance.
New approach learns latent motifs in networks for mesoscale structure analysis.
problem Understanding large-scale behavior in complex systems through mesoscale structures.
method Network dictionary learning (NDL) combining network sampling and nonnegative matrix factorization.
result Networks can be approximated using a small set of latent motifs.
Networks are a fundamental tool for modeling complex systems in a variety of domains including social and communication networks as well as biology and neuroscience. Small subgraph patterns in networks, called network motifs, are crucial to understanding the structure and function of these systems. However, the role of…
Generalized belief propagation converges to optimal solutions on graphs with motifs.
problem Understanding belief propagation on loopy graphs.
method Study of generalized belief propagation on graphs with motifs.
result Generalized belief propagation converges to the global optimum of the Bethe free energy.
Paper proposes DTW-SOM for visual exploration of time-series motifs.
problem Exploring motifs extracted by time-series motif discovery algorithms.
method Adapted Self-Organizing Map (DTW-SOM) using Dynamic Time Warping distance.
result DTW-SOM effectively visualizes motifs from time-series data.
New method uses diffusion models to generate proteins with specific motifs.
problem Generating proteins with specific functional substructures (motifs) using diffusion models.
method Adapting SMC-aided diffusion posterior samplers to zero-shot scaffolding tasks.
result Proposed potentials and samplers improve performance in generating proteins with desired motifs.
Researchers create exact minimal surfaces with helical motifs in biological structures.
problem Analyzing helical motifs in minimal surfaces of biological structures.
method Developed a method to construct exact minimal surfaces with arbitrary helical motifs.
result Exact minimal surfaces with helical motifs can be created and analyzed.
New clustering methods use motifs to organize networks.
problem Organizing directed graphs efficiently.
method Construct clustering methods parametrized by motifs.
result New clustering methods can organize networks.
MMGAN creates graphs with higher-order motifs for better network simulation.
problem Generative models fail to capture higher-order connectivity patterns in real-world networks.
method Combines multiple biased random walks to capture different motif structures.
result Outperforms NetGAN at creating graphs with accurate network motif statistics.
MDF represents time series motifs as images for improved classification.
problem Classifying time series data with high-order patterns.
method Motif Difference Field (MDF) using Fully Convolutional Networks (FCN).
result MDF outperforms other methods on UCR time series datasets.
odeN efficiently approximates multiple temporal motifs in large networks.
problem Efficiently counting multiple temporal motifs in large temporal networks.
method odeN is a sampling-based algorithm that provides accurate probabilistic approximations of motif counts.
result odeN provides accurate approximations of motif counts in a fraction of the time needed by state-of-the-art methods.
New method discovers time series motifs in datasets with missing data.
problem Missing data hinders motif discovery in time series.
method Admissible time series motif discovery technique for datasets with missing data.
result Proves method is admissible, producing no false negatives.
TF-MoDISco finds transcription factor motifs from genomic data.
problem Identifying transcription factor motifs from genomic sequence data.
method Algorithm for motif discovery from basepair-level importance scores.
result Improved version v0.5.6.5 of TF-MoDISco.
Proposes VILMAP for finding motifs and segmenting words in time series.
problem Finding motifs and segmenting words in time series data.
method Self-Organizing Maps with variable input length.
result VILMAP identifies motifs of varying lengths and avoids catastrophic forgetting.
WideDTA predicts drug-target binding affinity using text-based information.
problem Predicting drug-target binding affinity is a major challenge in drug discovery.
method WideDTA uses chemical and biological textual sequence information, including protein sequence, ligand SMILES, protein domains and motifs, and maximum common substructure words.
result WideDTA outperformed DeepDTA on the KIBA dataset, indicating the word-based sequence representation is a promising alternative.
Paper discovers manoeuvres from vehicle telematics data.
problem Analyzing driving behaviour from vehicle data.
method Used motif detection in time-series with a modified EMD algorithm.
result Validated motif discovery for complex manoeuvres.
A motif-based framework identifies local spillover structures in financial markets.
problem Aggregate risk spillovers obscure local interaction patterns in systemic risk.
method Develops a motif-based framework using multiscale backbones and colored motifs.
result Motif-based portfolios outperform traditional benchmarks on risk-adjusted returns.
New method clusters weighted directed networks using motifs.
problem Clustering directed networks fails to consider higher-order structure and edge weights.
method Motif-based weighted spectral clustering with new matrix formulae.
result Scalable and effective clustering on large graphs and real-world data.
New method discovers time series motifs under DTW, significantly reducing computations.
problem Discovering time series motifs under DTW is computationally challenging.
method Exact scalable method using novel lower bounds hierarchy.
result Prunes up to 99.99% of DTW computations under realistic settings.
Paper constructs motifs from planar tilings for DP weaves and polycatenanes.
problem Creating complex entangled structures from periodic tilings.
method Combinatorial methodology using polygonal link transformations.
result Predicting the type of motif from a given tiling and polygonal link method.
The paper models musical motif transformations in Beethoven's works.
problem Understanding how motifs transform in symbolic music.
method Developed a probabilistic framework using Conditional Random Fields.
result Identified patterns of motif transformations and their co-occurrences.
New model generates larger molecules more effectively.
problem Previous graph generation techniques struggle with larger molecules.
method Hierarchical graph encoder-decoder using structural motifs.
result Model significantly outperforms previous baselines on molecule generation tasks.
The study identifies persistent motifs in stock correlations for sector-neutral portfolio diversification.
problem Forecasting and diversification of sector-neutral portfolios using long-term correlations.
method Analysis of Triangulated Maximally Filtered Graphs (TMFG) generated from rolling windows of stock price log-returns, identifying persistent motifs.
result Persistent motifs in stock correlations can be used to forecast and diversify sector-neutral portfolios, reducing volatility.
iDeepA predicts RNA-protein binding sites from RNA sequences using a CNN with attention.
problem Predicting RNA-protein binding sites from raw RNA sequences efficiently.
method Attention based convolutional neural network (iDeepA) encoding RNA sequences into one-hot encoding, followed by a CNN with an attention mechanism.
result iDeepA achieves comparable performance to state-of-the-art methods on CLIP-seq data.
Feature engineering remains a major bottleneck when creating predictive systems from electronic medical records. At present, an important missing element is detecting predictive regular clinical motifs from irregular episodic records. We present Deepr (short for Deep record), a new end-to-end deep learning system that …
HiGraphDTI learns drug and target representations from molecular graphs to predict DTIs.
problem Inaccurate drug-target interaction prediction due to insufficient chemical information extraction.
method Hierarchical graph representation learning to extract chemical information from atoms, motifs, and molecules.
result HiGraphDTI outperforms state-of-the-art methods in DTI prediction and interaction interpretation.
Develops tools to analyze higher-order network motifs.
problem Lower-order connectivity patterns are insufficient for understanding complex systems.
method Three tools: node clustering, higher-order closure patterns, and temporal network motifs.
result New insights into network datasets from higher-order connectivity patterns.
LISBET automates social behavior analysis using machine learning.
problem Manual annotation of social behaviors is time-consuming, biased, and misses subtle interactions.
method Self-supervised learning on body tracking data.
result Automated detection and segmentation of social interactions.
HONE learns higher-order network embeddings from graph data.
problem Capturing higher-order structures in network data.
method HONE framework based on network motifs, with interchangeable components.
result HONE outperforms other embedding methods by up to 75% in AUC.
Predicts unseen links in graphs using motifs of 3-5 nodes.
problem Predicting new links in graphs for various applications.
method Uses motifs of 3-5 nodes for link prediction, optimizing feature construction and negative example sampling.
result Higher classification accuracy compared to prior methods.
KCoreMotif clusters large networks efficiently by exploiting k-core decomposition and motifs.
problem Efficiently clustering large networks for trust evaluation.
method Exploits k-core decomposition and motifs to perform motif-based spectral clustering on k-core subgraphs.
result The proposed algorithm is accurate and efficient for large networks.
SEISM tests neural network features for regulatory genomics.
problem Testing neural network features for regulatory genomics.
method Selective inference procedure for sequence motifs.
result Sampling under specific parameters characterizes composite null hypothesis.
Bayesian models predict antimicrobial and multifunctional peptides with high accuracy.
problem Classifying peptides with antimicrobial and multifunctional properties.
method Bayesian network models to identify amino acid sequence motifs and predict antimicrobial activity.
result Models achieve 94% accuracy in predicting antimicrobial activity, with interpretability of parameters.
CAWs learn temporal network dynamics without node identities or edge attributes.
problem Learning temporal network dynamics without node identities or edge attributes.
method Causal Anonymous Walks (CAWs) using temporal random walks and hitting counts.
result CAW-N outperforms previous methods in predicting links over 6 real temporal networks.
This paper classifies periodic weaves and their universal cover, extending Tait's conjectures.
problem Classifying periodic weaves and their universal cover in thickened surfaces.
method Introducing hyperbolic periodic weaves, extending Tait's conjectures, and using a generalized Kauffman bracket polynomial.
result Tait's conjectures are extended to minimal reduced alternating weaving motifs.
Prototype Matching Network (PMN) improves genomic TFBS prediction.
problem Predicting Transcription Factor Binding Sites (TFBSs) with hundreds of TFs as labels.
method Prototype Matching Network (PMN) that learns motif-like features and TF-TF interactions.
result PMN significantly outperforms baselines on a large TFBS dataset.