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Found 46 papers, 24 papers with code
Score-matching-based Structure Learning for Temporal Data on Networks
To enhance the scalability of score matching, we have developed a new parent-finding subroutine for leaf nodes in DAGs, significantly accelerating the most time-consuming part of the process: the pruning step.
Validation of an LLM-based Multi-Agent Framework for Protein Engineering in Dry Lab and Wet Lab
This study presents a comprehensive validation of TourSynbio-Agent through five diverse case studies spanning both computational (dry lab) and experimental (wet lab) protein engineering.
TourSynbio-Search: A Large Language Model Driven Agent Framework for Unified Search Method for Protein Engineering
The exponential growth in protein-related databases and scientific literature, combined with increasing demands for efficient biological information retrieval, has created an urgent need for unified and accessible search methods in protein engineering research.
AutoProteinEngine: A Large Language Model Driven Agent Framework for Multimodal AutoML in Protein Engineering
By bridging the gap between DL and biologists' domain expertise, AutoPE empowers researchers to leverage DL without extensive programming knowledge.
LaGDif: Latent Graph Diffusion Model for Efficient Protein Inverse Folding with Self-Ensemble
Protein inverse folding aims to identify viable amino acid sequences that can fold into given protein structures, enabling the design of novel proteins with desired functions for applications in drug discovery, enzyme engineering, and biomaterial development.
A Regressor-Guided Graph Diffusion Model for Predicting Enzyme Mutations to Enhance Turnover Number
kcatDiffuser is a graph diffusion model guided by a regressor, enabling the prediction of amino acid mutations at multiple random positions simultaneously.
A Survey for Large Language Models in Biomedicine
To address these challenges, we also identify future research directions of LLM in biomedicine including federated learning methods to preserve data privacy and integrating explainable AI methodologies to enhance the transparency of LLMs.
TourSynbio: A Multi-Modal Large Model and Agent Framework to Bridge Text and Protein Sequences for Protein Engineering
While large language models (LLMs) have achieved much progress in the domain of natural language processing, their potential in protein engineering remains largely unexplored.
Improving Antibody Design with Force-Guided Sampling in Diffusion Models
Our model, DiffForce, employs forces to guide the diffusion sampling process, effectively blending the two distributions.
A Fine-tuning Dataset and Benchmark for Large Language Models for Protein Understanding
The parallels between protein sequences and natural language in their sequential structures have inspired the application of large language models (LLMs) to protein understanding.
How Universal Polynomial Bases Enhance Spectral Graph Neural Networks: Heterophily, Over-smoothing, and Over-squashing
Our extensive experiments, conducted on a diverse range of real-world and synthetic datasets with varying degrees of heterophily, support the superiority of UniFilter.
ProteinEngine: Empower LLM with Domain Knowledge for Protein Engineering
Large language models (LLMs) have garnered considerable attention for their proficiency in tackling intricate tasks, particularly leveraging their capacities for zero-shot and in-context learning.
Layer-diverse Negative Sampling for Graph Neural Networks
Experiments on various real-world graph datasets demonstrate the effectiveness of our approach in improving the diversity of negative samples and overall learning performance.
Dirichlet Energy Enhancement of Graph Neural Networks by Framelet Augmentation
However, recursively aggregating neighboring information with graph convolutions leads to indistinguishable node features in deep layers, which is known as the over-smoothing issue.
Graph Denoising Diffusion for Inverse Protein Folding
In contrast, diffusion probabilistic models, as an emerging genre of generative approaches, offer the potential to generate a diverse set of sequence candidates for determined protein backbones.
Multi-level Protein Representation Learning for Blind Mutational Effect Prediction
Directed evolution plays an indispensable role in protein engineering that revises existing protein sequences to attain new or enhanced functions.
Accurate and Definite Mutational Effect Prediction with Lightweight Equivariant Graph Neural Networks
Directed evolution as a widely-used engineering strategy faces obstacles in finding desired mutants from the massive size of candidate modifications.
Graph Representation Learning for Interactive Biomolecule Systems
Advances in deep learning models have revolutionized the study of biomolecule systems and their mechanisms.
EqMotion: Equivariant Multi-agent Motion Prediction with Invariant Interaction Reasoning
In motion prediction tasks, maintaining motion equivariance under Euclidean geometric transformations and invariance of agent interaction is a critical and fundamental principle.
Ranked #1 on
Human Pose Forecasting
on HARPER
SESNet: sequence-structure feature-integrated deep learning method for data-efficient protein engineering
Here, we develop SESNet, a supervised deep-learning model to predict the fitness for protein mutants by leveraging both sequence and structure information, and exploiting attention mechanism.
Oversquashing in GNNs through the lens of information contraction and graph expansion
We compare the spectral expansion properties of our algorithm with that of an existing curvature-based non-local rewiring strategy.
Approximate Equivariance SO(3) Needlet Convolution
This paper develops a rotation-invariant needlet convolution for rotation group SO(3) to distill multiscale information of spherical signals.
How GNNs Facilitate CNNs in Mining Geometric Information from Large-Scale Medical Images
Existing solutions heavily rely on convolutional neural networks (CNNs) for global pixel-level analysis, leaving the underlying local geometric structure such as the interaction between cells in the tumor microenvironment unexplored.
ACMP: Allen-Cahn Message Passing for Graph Neural Networks with Particle Phase Transition
Neural message passing is a basic feature extraction unit for graph-structured data considering neighboring node features in network propagation from one layer to the next.
Lower and Upper Bounds for Numbers of Linear Regions of Graph Convolutional Networks
In particular, we obtain an optimal upper bound for the maximum number of linear regions for one-layer GCNs, and the upper and lower bounds for multi-layer GCNs.
Embedding Graphs on Grassmann Manifold
Learning efficient graph representation is the key to favorably addressing downstream tasks on graphs, such as node or graph property prediction.
Robust Graph Representation Learning for Local Corruption Recovery
The performance of graph representation learning is affected by the quality of graph input.
Graph Denoising with Framelet Regularizer
As graph data collected from the real world is merely noise-free, a practical representation of graphs should be robust to noise.
Weisfeiler and Lehman Go Cellular: CW Networks
Nevertheless, these models can be severely constrained by the rigid combinatorial structure of Simplicial Complexes (SCs).
Ranked #1 on
Graph Regression
on ZINC 100k
Anomaly Detection in Dynamic Graphs via Transformer
Detecting anomalies for dynamic graphs has drawn increasing attention due to their wide applications in social networks, e-commerce, and cybersecurity.
Grassmann Graph Embedding
Geometric deep learning that employs the geometric and topological features of data has attracted increasing attention in deep neural networks.
Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks
The pairwise interaction paradigm of graph machine learning has predominantly governed the modelling of relational systems.
How Framelets Enhance Graph Neural Networks
The graph neural networks with the proposed framelet convolution and pooling achieve state-of-the-art performance in many node and graph prediction tasks.
Decimated Framelet System on Graphs and Fast G-Framelet Transforms
Graph representation learning has many real-world applications, from super-resolution imaging, 3D computer vision to drug repurposing, protein classification, social networks analysis.
How Powerful are Shallow Neural Networks with Bandlimited Random Weights?
Despite the well-known fact that a neural network is a universal approximator, in this study, we mathematically show that when hidden parameters are distributed in a bounded domain, the network may not achieve zero approximation error.
MathNet: Haar-Like Wavelet Multiresolution-Analysis for Graph Representation and Learning
In this paper, we propose a framework for graph neural networks with multiresolution Haar-like wavelets, or MathNet, with interrelated convolution and pooling strategies.
Distributed Learning via Filtered Hyperinterpolation on Manifolds
Learning mappings of data on manifolds is an important topic in contemporary machine learning, with applications in astrophysics, geophysics, statistical physics, medical diagnosis, biochemistry, 3D object analysis.
Path Integral Based Convolution and Pooling for Graph Neural Networks
Borrowing ideas from physics, we propose a path integral based graph neural networks (PAN) for classification and regression tasks on graphs.
CosmoVAE: Variational Autoencoder for CMB Image Inpainting
The noise of the CMB map has a significant impact on the estimation precision for cosmological parameters.
Deep Learning Based Unsupervised and Semi-supervised Classification for Keratoconus
Currently, there is no cure for keratoconus other than corneal transplantation for advanced stage keratoconus or corneal cross-linking, which can only halt KC progression.
Distributed filtered hyperinterpolation for noisy data on the sphere
This paper develops distributed filtered hyperinterpolation for noisy data on the sphere, which assigns the data fitting task to multiple servers to find a good approximation of the mapping of input and output data.
Haar Graph Pooling
Deep Graph Neural Networks (GNNs) are useful models for graph classification and graph-based regression tasks.
HaarPooling: Graph Pooling with Compressive Haar Basis
The input of each pooling layer is transformed by the compressive Haar basis of the corresponding clustering.
FaVeST: Fast Vector Spherical Harmonic Transforms
The forward FaVeST evaluates the Fourier coefficients and has a computational cost proportional to $N\log \sqrt{N}$ for $N$ number of evaluation points.
Numerical Analysis Computational Complexity Numerical Analysis 65T50, 37C10, 33C55, 65D30 F.2.1; G.4; G.1.4; G.1.2
Fast Haar Transforms for Graph Neural Networks
Graph Neural Networks (GNNs) have become a topic of intense research recently due to their powerful capability in high-dimensional classification and regression tasks for graph-structured data.
