Duolin Wang
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View article: ProtLoc-GRPO: Cell line-specific subcellular localization prediction using a graph-based model and reinforcement learning
ProtLoc-GRPO: Cell line-specific subcellular localization prediction using a graph-based model and reinforcement learning Open
Subcellular localization prediction is crucial for understanding protein functions and cellular processes. Subcellular localization is dependent on tissue and cell lines derived from different cell types. Predicting cell line-specific subc…
View article: MULoc-target: Targeting peptide classification and detection using a protein language model
MULoc-target: Targeting peptide classification and detection using a protein language model Open
Protein targeting, often guided by targeting peptides, is a critical biological process that directs proteins to their specific cellular destinations, ensuring proper cellular functionality and organization. Accurate classification and det…
View article: MTPrompt-PTM: A Multi-Task Method for Post-Translational Modification Prediction Using Prompt Tuning on a Structure-Aware Protein Language Model
MTPrompt-PTM: A Multi-Task Method for Post-Translational Modification Prediction Using Prompt Tuning on a Structure-Aware Protein Language Model Open
Post-translational modifications (PTMs) regulate protein function, stability, and interactions, playing essential roles in cellular signaling, localization, and disease mechanisms. Computational approaches enable scalable PTM site predicti…
View article: Prot2Token: A Unified Framework for Protein Modeling via Next-Token Prediction
Prot2Token: A Unified Framework for Protein Modeling via Next-Token Prediction Open
The diverse nature of protein prediction tasks has traditionally necessitated specialized models, hindering the development of broadly applicable and computationally efficient Protein Language Models (PLMs). In this work, we introduce Prot…
View article: Enhancing Structure-aware Protein Language Models with Efficient Fine-tuning for Various Protein Prediction Tasks
Enhancing Structure-aware Protein Language Models with Efficient Fine-tuning for Various Protein Prediction Tasks Open
Proteins are crucial in a wide range of biological and engineering processes. Large protein language models (PLMs) can significantly advance our understanding and engineering of proteins. However, the effectiveness of PLMs in prediction an…
View article: Kinase-substrate prediction using an autoregressive model
Kinase-substrate prediction using an autoregressive model Open
Kinase-specific phosphorylation plays a critical role in cellular signaling and various diseases. However, even in model organisms, the substrates of most kinases remain unidentified. Currently, there is no reliable method to predict kinas…
View article: S‐PLM: Structure‐Aware Protein Language Model via Contrastive Learning Between Sequence and Structure
S‐PLM: Structure‐Aware Protein Language Model via Contrastive Learning Between Sequence and Structure Open
Proteins play an essential role in various biological and engineering processes. Large protein language models (PLMs) present excellent potential to reshape protein research by accelerating the determination of protein functions and the de…
View article: IRnet: Immunotherapy response prediction using pathway knowledge-informed graph neural network
IRnet: Immunotherapy response prediction using pathway knowledge-informed graph neural network Open
View article: Parameter-efficient fine-tuning on large protein language models improves signal peptide prediction
Parameter-efficient fine-tuning on large protein language models improves signal peptide prediction Open
Signal peptides (SPs) play a crucial role in protein translocation in cells. The development of large protein language models (PLMs) and prompt-based learning provide a new opportunity for SP prediction, especially for the categories with …
View article: Prot2Token: A multi-task framework for protein language processing using autoregressive language modeling
Prot2Token: A multi-task framework for protein language processing using autoregressive language modeling Open
This paper proposes a versatile tokenization method and introduces Prot2Token, a model that combines autoregressive language modeling with protein language models (PLMs) to tackle various protein prediction tasks using protein sequences. L…
View article: A contrastive learning approach to integrate spatial transcriptomics and histological images
A contrastive learning approach to integrate spatial transcriptomics and histological images Open
View article: Integrating Protein Structure Prediction and Bayesian Optimization for Peptide Design
Integrating Protein Structure Prediction and Bayesian Optimization for Peptide Design Open
Peptide design, with the goal of identifying peptides possessing unique biological properties, stands as a crucial challenge in peptide-based drug discovery. While traditional and computational methods have made significant strides, they o…
View article: PEFT-SP: Parameter-Efficient Fine-Tuning on Large Protein Language Models Improves Signal Peptide Prediction
PEFT-SP: Parameter-Efficient Fine-Tuning on Large Protein Language Models Improves Signal Peptide Prediction Open
Signal peptides (SP) play a crucial role in protein translocation in cells. The development of large protein language models (PLMs) provides a new opportunity for SP prediction, especially for the categories with limited annotated data. We…
View article: DescribePROT in 2023: more, higher-quality and experimental annotations and improved data download options
DescribePROT in 2023: more, higher-quality and experimental annotations and improved data download options Open
The DescribePROT database of amino acid-level descriptors of protein structures and functions was substantially expanded since its release in 2020. This expansion includes substantial increase in the size, scope, and quality of the underly…
View article: Diffusion models in bioinformatics and computational biology
Diffusion models in bioinformatics and computational biology Open
View article: Prediction of Protein Ion–Ligand Binding Sites with ELECTRA
Prediction of Protein Ion–Ligand Binding Sites with ELECTRA Open
Interactions between proteins and ions are essential for various biological functions like structural stability, metabolism, and signal transport. Given that more than half of all proteins bind to ions, it is becoming crucial to identify i…
View article: Prediction of Protein Ion-Ligand Binding Sites with ELECTRA
Prediction of Protein Ion-Ligand Binding Sites with ELECTRA Open
Interactions between proteins and ions are essential for various biological functions like structural stability, metabolism, and signal transport. Given that more than half of all proteins bind to ions, it becomes crucial to identify ion-b…
View article: S-PLM: Structure-aware Protein Language Model via Contrastive Learning between Sequence and Structure
S-PLM: Structure-aware Protein Language Model via Contrastive Learning between Sequence and Structure Open
Proteins play an essential role in various biological and engineering processes. Large protein language models (PLMs) present excellent potential to reshape protein research by accelerating the determination of protein function and the des…
View article: Spatial-Aware Transformer (SAT): Enhancing Global Modeling in Transformer Segmentation for Remote Sensing Images
Spatial-Aware Transformer (SAT): Enhancing Global Modeling in Transformer Segmentation for Remote Sensing Images Open
In this research, we present the Spatial-Aware Transformer (SAT), an enhanced implementation of the Swin Transformer module, purposed to augment the global modeling capabilities of existing transformer segmentation mechanisms within remote…
View article: MULocDeep web service for protein localization prediction and visualization at subcellular and suborganellar levels
MULocDeep web service for protein localization prediction and visualization at subcellular and suborganellar levels Open
Predicting protein localization and understanding its mechanisms are critical in biology and pathology. In this context, we propose a new web application of MULocDeep with improved performance, result interpretation, and visualization. By …
View article: Meta-learning for T cell receptor binding specificity and beyond
Meta-learning for T cell receptor binding specificity and beyond Open
View article: Single-cell biological network inference using a heterogeneous graph transformer
Single-cell biological network inference using a heterogeneous graph transformer Open
Single-cell multi-omics (scMulti-omics) allows the quantification of multiple modalities simultaneously to capture the intricacy of complex molecular mechanisms and cellular heterogeneity. Existing tools cannot effectively infer the active…
View article: Amniotes co-opt intrinsic genetic instability to protect germ-line genome integrity
Amniotes co-opt intrinsic genetic instability to protect germ-line genome integrity Open
View article: Diffusion Models in Bioinformatics: A New Wave of Deep Learning Revolution in Action
Diffusion Models in Bioinformatics: A New Wave of Deep Learning Revolution in Action Open
Denoising diffusion models have emerged as one of the most powerful generative models in recent years. They have achieved remarkable success in many fields, such as computer vision, natural language processing (NLP), and bioinformatics. Al…
View article: Global video object segmentation with spatial constraint module
Global video object segmentation with spatial constraint module Open
We present a lightweight and efficient semi-supervised video object segmentation network based on the space-time memory framework. To some extent, our method solves the two difficulties encountered in traditional video object segmentation:…
View article: Semi-Supervised Contrastive Learning for Few-Shot Segmentation of Remote Sensing Images
Semi-Supervised Contrastive Learning for Few-Shot Segmentation of Remote Sensing Images Open
Deep learning has been widely used in remote sensing image segmentation, while a lack of training data remains a significant issue. The few-shot segmentation of remote sensing images refers to the segmenting of novel classes with a few ann…
View article: DeepMAPS: Single-cell biological network inference using heterogeneous graph transformer
DeepMAPS: Single-cell biological network inference using heterogeneous graph transformer Open
We present DeepMAPS (Deep learning-based Multi-omics Analysis Platform for Single-cell data) for biological network inference from single-cell multi-omics (scMulti-omics). DeepMAPS includes both cells and genes in a heterogeneous graph to …
View article: G2PDeep: a web-based deep-learning framework for quantitative phenotype prediction and discovery of genomic markers
G2PDeep: a web-based deep-learning framework for quantitative phenotype prediction and discovery of genomic markers Open
G2PDeep is an open-access web server, which provides a deep-learning framework for quantitative phenotype prediction and discovery of genomics markers. It uses zygosity or single nucleotide polymorphism (SNP) information from plants and an…
View article: DM3Loc: multi-label mRNA subcellular localization prediction and analysis based on multi-head self-attention mechanism
DM3Loc: multi-label mRNA subcellular localization prediction and analysis based on multi-head self-attention mechanism Open
Subcellular localization of messenger RNAs (mRNAs), as a prevalent mechanism, gives precise and efficient control for the translation process. There is mounting evidence for the important roles of this process in a variety of cellular even…
View article: MULocDeep: A deep-learning framework for protein subcellular and suborganellar localization prediction with residue-level interpretation
MULocDeep: A deep-learning framework for protein subcellular and suborganellar localization prediction with residue-level interpretation Open
Prediction of protein localization plays an important role in understanding protein function and mechanisms. In this paper, we propose a general deep learning-based localization prediction framework, MULocDeep, which can predict multiple l…