Search Results for author:
Found 33 papers, 14 papers with code
Causal Explainability of Machine Learning in Heart Failure Prediction from Electronic Health Records
This paper uses clinical variables from a heart failure (HF) patient cohort to investigate the causal explainability of important variables obtained in statistical and ML contexts.
VenusX: Unlocking Fine-Grained Functional Understanding of Proteins
Deep learning models have driven significant progress in predicting protein function and interactions at the protein level.
VenusFactory: A Unified Platform for Protein Engineering Data Retrieval and Language Model Fine-Tuning
Natural language processing (NLP) has significantly influenced scientific domains beyond human language, including protein engineering, where pre-trained protein language models (PLMs) have demonstrated remarkable success.
VenusMutHub: A systematic evaluation of protein mutation effect predictors on small-scale experimental data
Therefore, the desired testing datasets, will be small-size (~10-100) experimental data for each protein, and involve as many proteins as possible and as many properties as possible, which is, however, lacking.
Conformal prediction of future insurance claims in the regression problem
In the current insurance literature, prediction of insurance claims in the regression problem is often performed with a statistical model.
AI-Driven Secure Data Sharing: A Trustworthy and Privacy-Preserving Approach
These comprehensive analyses underscore its robustness, making it a trustworthy solution for secure data sharing in critical applications.
Retrieval-Enhanced Mutation Mastery: Augmenting Zero-Shot Prediction of Protein Language Model
Enzyme engineering enables the modification of wild-type proteins to meet industrial and research demands by enhancing catalytic activity, stability, binding affinities, and other properties.
Advancing Healthcare: Innovative ML Approaches for Improved Medical Imaging in Data-Constrained Environments
Healthcare industries face challenges when experiencing rare diseases due to limited samples.
Immunogenicity Prediction with Dual Attention Enables Vaccine Target Selection
Immunogenicity prediction is a central topic in reverse vaccinology for finding candidate vaccines that can trigger protective immune responses.
ReactZyme: A Benchmark for Enzyme-Reaction Prediction
Enzymes, with their specific catalyzed reactions, are necessary for all aspects of life, enabling diverse biological processes and adaptations.
Autoregressive Enzyme Function Prediction with Multi-scale Multi-modality Fusion
Accurate prediction of enzyme function is crucial for elucidating biological mechanisms and driving innovation across various sectors.
Secondary Structure-Guided Novel Protein Sequence Generation with Latent Graph Diffusion
The advent of deep learning has introduced efficient approaches for de novo protein sequence design, significantly improving success rates and reducing development costs compared to computational or experimental methods.
ProtSolM: Protein Solubility Prediction with Multi-modal Features
We provide a comprehensive leaderboard of existing statistical learning and deep learning methods on independent datasets with computational and experimental labels.
Protein Representation Learning with Sequence Information Embedding: Does it Always Lead to a Better Performance?
To this end, we propose ProtLOCA, a local geometry alignment method based solely on amino acid structure representation.
Science based AI model certification for new operational environments with application in traffic state estimation
Addressing this issue, this paper proposes a science-based certification methodology to assess the viability of employing pre-trained data-driven models in new operational environments.
Simple, Efficient and Scalable Structure-aware Adapter Boosts Protein Language Models
Fine-tuning Pre-trained protein language models (PLMs) has emerged as a prominent strategy for enhancing downstream prediction tasks, often outperforming traditional supervised learning approaches.
RiboDiffusion: Tertiary Structure-based RNA Inverse Folding with Generative Diffusion Models
A fundamental challenge is to find functional RNA sequences that satisfy given structural constraints, known as the inverse folding problem.
Science based AI model certification for untrained operational environments with application in traffic state estimation
Addressing this issue, this paper proposes a science-based certification methodology to assess the viability of employing pre-trained data-driven models in untrained operational environments.
Empowering Healthcare through Privacy-Preserving MRI Analysis
Integrating DL within the Federated Learning (FL) framework has yielded a methodology that offers precise and dependable diagnostics for detecting brain tumors.
Causal Prototype-inspired Contrast Adaptation for Unsupervised Domain Adaptive Semantic Segmentation of High-resolution Remote Sensing Imagery
Then, a causal prototypical contrast module is used to learn domain invariant causal features.
OpenMEDLab: An Open-source Platform for Multi-modality Foundation Models in Medicine
The emerging trend of advancing generalist artificial intelligence, such as GPTv4 and Gemini, has reshaped the landscape of research (academia and industry) in machine learning and many other research areas.
Enhancing the efficiency of protein language models with minimal wet-lab data through few-shot learning
Accurately modeling the protein fitness landscapes holds great importance for protein engineering.
PETA: Evaluating the Impact of Protein Transfer Learning with Sub-word Tokenization on Downstream Applications
Moreover, despite the wealth of benchmarks and studies in the natural language community, there remains a lack of a comprehensive benchmark for systematically evaluating protein language model quality.
Pro-PRIME: A general Temperature-Guided Language model to engineer enhanced Stability and Activity in Proteins
Designing protein mutants of both high stability and activity is a critical yet challenging task in protein engineering.
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.
TemPL: A Novel Deep Learning Model for Zero-Shot Prediction of Protein Stability and Activity Based on Temperature-Guided Language Modeling
We introduce TemPL, a novel deep learning approach for zero-shot prediction of protein stability and activity, harnessing temperature-guided language modeling.
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.
A Ligand-and-structure Dual-driven Deep Learning Method for the Discovery of Highly Potent GnRH1R Antagonist to treat Uterine Diseases
Gonadotrophin-releasing hormone receptor (GnRH1R) is a promising therapeutic target for the treatment of uterine diseases.
Accurate RNA 3D structure prediction using a language model-based deep learning approach
Accurate prediction of RNA three-dimensional (3D) structure remains an unsolved challenge.
Interpretable RNA Foundation Model from Unannotated Data for Highly Accurate RNA Structure and Function Predictions
Non-coding RNA structure and function are essential to understanding various biological processes, such as cell signaling, gene expression, and post-transcriptional regulations.
ParaFold: Paralleling AlphaFold for Large-Scale Predictions
We evaluated the accuracy and efficiency of optimizations on CPUs and GPUs, and showed the large-scale prediction capability by running ParaFold inferences of 19, 704 small proteins in five hours on one NVIDIA DGX-2.
SCAttNet: Semantic Segmentation Network with Spatial and Channel Attention Mechanism for High-Resolution Remote Sensing Images
High-resolution remote sensing images (HRRSIs) contain substantial ground object information, such as texture, shape, and spatial location.
