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Found 56 papers, 31 papers with code
dyAb: Flow Matching for Flexible Antibody Design with AlphaFold-driven Pre-binding Antigen
The development of therapeutic antibodies heavily relies on accurate predictions of how antigens will interact with antibodies.
GLTW: Joint Improved Graph Transformer and LLM via Three-Word Language for Knowledge Graph Completion
Knowledge Graph Completion (KGC), which aims to infer missing or incomplete facts, is a crucial task for KGs.
Deep EEG Super-Resolution: Upsampling EEG Spatial Resolution with Generative Adversarial Networks
The proposed SR EEG by GAN is a promising approach to improve the spatial resolution of low density EEG headsets.
A Simple yet Effective DDG Predictor is An Unsupervised Antibody Optimizer and Explainer
For the target antibody, we propose a novel Mutation Explainer to learn mutation preferences, which accounts for the marginal benefit of each mutation per residue.
Large Language Model Safety: A Holistic Survey
The rapid development and deployment of large language models (LLMs) have introduced a new frontier in artificial intelligence, marked by unprecedented capabilities in natural language understanding and generation.
Relation-Aware Equivariant Graph Networks for Epitope-Unknown Antibody Design and Specificity Optimization
Antibodies are Y-shaped proteins that protect the host by binding to specific antigens, and their binding is mainly determined by the Complementary Determining Regions (CDRs) in the antibody.
ACDiT: Interpolating Autoregressive Conditional Modeling and Diffusion Transformer
The analysis of the trade-off between autoregressive modeling and diffusion demonstrates the potential of ACDiT to be used in long-horizon visual generation tasks.
Ranked #8 on
Video Generation
on UCF-101
MeToken: Uniform Micro-environment Token Boosts Post-Translational Modification Prediction
Post-translational modifications (PTMs) profoundly expand the complexity and functionality of the proteome, regulating protein attributes and interactions that are crucial for biological processes.
Configurable Foundation Models: Building LLMs from a Modular Perspective
We first formalize modules into emergent bricks - functional neuron partitions that emerge during the pre-training phase, and customized bricks - bricks constructed via additional post-training to improve the capabilities and knowledge of LLMs.
CMoralEval: A Moral Evaluation Benchmark for Chinese Large Language Models
These help us curate CMoralEval that encompasses both explicit moral scenarios (14, 964 instances) and moral dilemma scenarios (15, 424 instances), each with instances from different data sources.
FastFiD: Improve Inference Efficiency of Open Domain Question Answering via Sentence Selection
Open Domain Question Answering (ODQA) has been advancing rapidly in recent times, driven by significant developments in dense passage retrieval and pretrained language models.
Teach Harder, Learn Poorer: Rethinking Hard Sample Distillation for GNN-to-MLP Knowledge Distillation
To bridge the gaps between powerful Graph Neural Networks (GNNs) and lightweight Multi-Layer Perceptron (MLPs), GNN-to-MLP Knowledge Distillation (KD) proposes to distill knowledge from a well-trained teacher GNN into a student MLP.
CBGBench: Fill in the Blank of Protein-Molecule Complex Binding Graph
To broaden the scope, we have adapted these models to a range of tasks essential in drug design, which are considered sub-tasks within the graph fill-in-the-blank tasks.
GenBench: A Benchmarking Suite for Systematic Evaluation of Genomic Foundation Models
The Genomic Foundation Model (GFM) paradigm is expected to facilitate the extraction of generalizable representations from massive genomic data, thereby enabling their application across a spectrum of downstream applications.
UniIF: Unified Molecule Inverse Folding
We do such unification in two levels: 1) Data-Level: We propose a unified block graph data form for all molecules, including the local frame building and geometric feature initialization.
Learning to Predict Mutation Effects of Protein-Protein Interactions by Microenvironment-aware Hierarchical Prompt Learning
Protein-protein bindings play a key role in a variety of fundamental biological processes, and thus predicting the effects of amino acid mutations on protein-protein binding is crucial.
VQDNA: Unleashing the Power of Vector Quantization for Multi-Species Genomic Sequence Modeling
In this paper, we introduce VQDNA, a general-purpose framework that renovates genome tokenization from the perspective of genome vocabulary learning.
Deep Lead Optimization: Leveraging Generative AI for Structural Modification
Through this lens, de novo design can incorporate strategies from lead optimization to address the challenge of generating hard-to-synthesize molecules; inversely, lead optimization can benefit from the innovations in de novo design by approaching it as a task of generating molecules conditioned on certain substructures.
OpenEval: Benchmarking Chinese LLMs across Capability, Alignment and Safety
In addition to these benchmarks, we have implemented a phased public evaluation and benchmark update strategy to ensure that OpenEval is in line with the development of Chinese LLMs or even able to provide cutting-edge benchmark datasets to guide the development of Chinese LLMs.
Deep Geometry Handling and Fragment-wise Molecular 3D Graph Generation
Most earlier 3D structure-based molecular generation approaches follow an atom-wise paradigm, incrementally adding atoms to a partially built molecular fragment within protein pockets.
PPFlow: Target-aware Peptide Design with Torsional Flow Matching
Therapeutic peptides have proven to have great pharmaceutical value and potential in recent decades.
Decoupling Weighing and Selecting for Integrating Multiple Graph Pre-training Tasks
In this paper, we identify two important collaborative processes for this topic: (1) select: how to select an optimal task combination from a given task pool based on their compatibility, and (2) weigh: how to weigh the selected tasks based on their importance.
Enhancing Protein Predictive Models via Proteins Data Augmentation: A Benchmark and New Directions
Augmentation is an effective alternative to utilize the small amount of labeled protein data.
Unified View of Grokking, Double Descent and Emergent Abilities: A Perspective from Circuits Competition
Recent studies have uncovered intriguing phenomena in deep learning, such as grokking, double descent, and emergent abilities in large language models, which challenge human intuition and are crucial for a deeper understanding of neural models.
MAPE-PPI: Towards Effective and Efficient Protein-Protein Interaction Prediction via Microenvironment-Aware Protein Embedding
In addition, microenvironments defined in previous work are largely based on experimentally assayed physicochemical properties, for which the "vocabulary" is usually extremely small.
Re-Dock: Towards Flexible and Realistic Molecular Docking with Diffusion Bridge
Accurate prediction of protein-ligand binding structures, a task known as molecular docking is crucial for drug design but remains challenging.
PSC-CPI: Multi-Scale Protein Sequence-Structure Contrasting for Efficient and Generalizable Compound-Protein Interaction Prediction
Compound-Protein Interaction (CPI) prediction aims to predict the pattern and strength of compound-protein interactions for rational drug discovery.
FoldToken: Learning Protein Language via Vector Quantization and Beyond
Is there a foreign language describing protein sequences and structures simultaneously?
Understanding YTHDF2-mediated mRNA Degradation By m6A-BERT-Deg
N6-methyladenosine (m6A) is the most abundant mRNA modification within mammalian cells, holding pivotal significance in the regulation of mRNA stability, translation, and splicing.
SRNI-CAR: A comprehensive dataset for analyzing the Chinese automotive market
The automotive industry plays a critical role in the global economy, and particularly important is the expanding Chinese automobile market due to its immense scale and influence.
Leveraging Foundation Models to Improve Lightweight Clients in Federated Learning
Federated Learning (FL) is a distributed training paradigm that enables clients scattered across the world to cooperatively learn a global model without divulging confidential data.
Evaluating Large Language Models: A Comprehensive Survey
We hope that this comprehensive overview will stimulate further research interests in the evaluation of LLMs, with the ultimate goal of making evaluation serve as a cornerstone in guiding the responsible development of LLMs.
Protein 3D Graph Structure Learning for Robust Structure-based Protein Property Prediction
To study this problem, we identify a Protein 3D Graph Structure Learning Problem for Robust Protein Property Prediction (PGSL-RP3), collect benchmark datasets, and present a protein Structure embedding Alignment Optimization framework (SAO) to mitigate the problem of structure embedding bias between the predicted and experimental protein structures.
Large Language Model Alignment: A Survey
We also envision bridging the gap between the AI alignment research community and the researchers engrossed in the capability exploration of LLMs for both capable and safe LLMs.
CBBQ: A Chinese Bias Benchmark Dataset Curated with Human-AI Collaboration for Large Language Models
In this work, we present a Chinese Bias Benchmark dataset that consists of over 100K questions jointly constructed by human experts and generative language models, covering stereotypes and societal biases in 14 social dimensions related to Chinese culture and values.
Quantifying the Knowledge in GNNs for Reliable Distillation into MLPs
To bridge the gaps between topology-aware Graph Neural Networks (GNNs) and inference-efficient Multi-Layer Perceptron (MLPs), GLNN proposes to distill knowledge from a well-trained teacher GNN into a student MLP.
Fair Patient Model: Mitigating Bias in the Patient Representation Learned from the Electronic Health Records
Methods: We defined a new loss function, called weighted loss function, in the deep representation learning model to balance the importance of different groups of patients and features.
Functional-Group-Based Diffusion for Pocket-Specific Molecule Generation and Elaboration
In this way, however, it is hard to generate realistic fragments with complicated structures.
Extracting Low-/High- Frequency Knowledge from Graph Neural Networks and Injecting it into MLPs: An Effective GNN-to-MLP Distillation Framework
Furthermore, we identified a potential information drowning problem for existing GNN-to-MLP distillation, i. e., the high-frequency knowledge of the pre-trained GNNs may be overwhelmed by the low-frequency knowledge during distillation; we have described in detail what it represents, how it arises, what impact it has, and how to deal with it.
Tool Learning with Foundation Models
Considering the lack of a systematic tool learning evaluation in prior works, we experiment with 18 representative tools and show the potential of current foundation models in skillfully utilizing tools.
Data-Efficient Protein 3D Geometric Pretraining via Refinement of Diffused Protein Structure Decoy
Learning meaningful protein representation is important for a variety of biological downstream tasks such as structure-based drug design.
A Survey on Protein Representation Learning: Retrospect and Prospect
To pave the way for AI researchers with little bioinformatics background, we present a timely and comprehensive review of PRL formulations and existing PRL methods from the perspective of model architectures, pretext tasks, and downstream applications.
Protein Language Models and Structure Prediction: Connection and Progression
The prediction of protein structures from sequences is an important task for function prediction, drug design, and related biological processes understanding.
DiffBP: Generative Diffusion of 3D Molecules for Target Protein Binding
Previous works usually generate atoms in an auto-regressive way, where element types and 3D coordinates of atoms are generated one by one.
FPT: Improving Prompt Tuning Efficiency via Progressive Training
Inspired by these observations, we propose Fast Prompt Tuning (FPT), which starts by conducting PT using a small-scale partial PLM, and then progressively expands its depth and width until the full-model size.
Using Context-to-Vector with Graph Retrofitting to Improve Word Embeddings
Although contextualized embeddings generated from large-scale pre-trained models perform well in many tasks, traditional static embeddings (e. g., Skip-gram, Word2Vec) still play an important role in low-resource and lightweight settings due to their low computational cost, ease of deployment, and stability.
Ranked #1 on
Word Similarity
on WS353
Automated Graph Self-supervised Learning via Multi-teacher Knowledge Distillation
Self-supervised learning on graphs has recently achieved remarkable success in graph representation learning.
Driving Safety Prediction and Safe Route Mapping Using In-vehicle and Roadside Data
In this paper, the Safe Route Mapping (SRM) model, a methodology for developing dynamic risk heat maps of roadways, is extended to consider driver behaviors when making predictions.
Toward Deep Learning Based Access Control
A common trait of current access control approaches is the challenging need to engineer abstract and intuitive access control models.
Deep learning tackles single-cell analysis A survey of deep learning for scRNA-seq analysis
Here we present a processing pipeline of single-cell RNA-seq data, survey a total of 25 DL algorithms and their applicability for a specific step in the processing pipeline.
TR-BERT: Dynamic Token Reduction for Accelerating BERT Inference
To address this issue, we propose a dynamic token reduction approach to accelerate PLMs' inference, named TR-BERT, which could flexibly adapt the layer number of each token in inference to avoid redundant calculation.
Modeling EEG data distribution with a Wasserstein Generative Adversarial Network to predict RSVP Events
Electroencephalography (EEG) data are difficult to obtain due to complex experimental setups and reduced comfort with prolonged wearing.
Convolutional neural network models for cancer type prediction based on gene expression
In breast cancer, for instance, our model identified well-known markers, such as GATA3 and ESR1.
GSAE: an autoencoder with embedded gene-set nodes for genomics functional characterization
We introduced the concept of the gene superset, an unbiased combination of gene sets with weights trained by the autoencoder, where each node in the latent layer is a superset.
Predicting drug response of tumors from integrated genomic profiles by deep neural networks
We trained and tested the model on a dataset of 622 cancer cell lines and achieved an overall prediction performance of mean squared error at 1. 96 (log-scale IC50 values).
