Copertina

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Zhang, Xiang, Wei, Jiaqi, Qiu, Zijie, Xu, Sheng, Dong, Nanqing, Gao, Zhiqiang, Sun, Siqi
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Biomolecules Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Universal Biological Sequence Reranking for Improved De Novo Peptide Sequencing
di: Qiu, Zijie, et al.
Pubblicazione: (2025)

Disentangling the Complex Multiplexed DIA Spectra in De Novo Peptide Sequencing
di: Ma, Zheng, et al.
Pubblicazione: (2024)

AdaNovo: Adaptive \emph{De Novo} Peptide Sequencing with Conditional Mutual Information
di: Xia, Jun, et al.
Pubblicazione: (2024)

AF2-Mutation: Adversarial Sequence Mutations against AlphaFold2 on Protein Tertiary Structure Prediction
di: Yuan, Zhongju, et al.
Pubblicazione: (2023)

Biology-Instructions: A Dataset and Benchmark for Multi-Omics Sequence Understanding Capability of Large Language Models
di: He, Haonan, et al.
Pubblicazione: (2024)

Peptide Sequencing Via Protein Language Models
di: Pham, Thuong Le Hoai, et al.
Pubblicazione: (2024)

Improved Off-policy Reinforcement Learning in Biological Sequence Design
di: Kim, Hyeonah, et al.
Pubblicazione: (2024)

Minimal-Action Discrete Schrödinger Bridge Matching for Peptide Sequence Design
di: Goel, Shrey, et al.
Pubblicazione: (2026)

Bidirectional Representations Augmented Autoregressive Biological Sequence Generation
di: Zhang, Xiang, et al.
Pubblicazione: (2025)

Remodeling Peptide-MHC-TCR Triad Binding as Sequence Fusion for Immunogenicity Prediction
di: Ma, Jiahao, et al.
Pubblicazione: (2025)

Multi-Objective-Guided Discrete Flow Matching for Controllable Biological Sequence Design
di: Chen, Tong, et al.
Pubblicazione: (2025)

Gumbel-Softmax Flow Matching with Straight-Through Guidance for Controllable Biological Sequence Generation
di: Tang, Sophia, et al.
Pubblicazione: (2025)

De Novo Molecular Design Enabled by Direct Preference Optimization and Curriculum Learning
di: Hou, Junyu
Pubblicazione: (2025)

Consistent Synthetic Sequences Unlock Structural Diversity in Fully Atomistic De Novo Protein Design
di: Reidenbach, Danny, et al.
Pubblicazione: (2025)

Tree Search-Based Evolutionary Bandits for Protein Sequence Optimization
di: Qiu, Jiahao, et al.
Pubblicazione: (2024)

Multi-domain Distribution Learning for De Novo Drug Design
di: Schneuing, Arne, et al.
Pubblicazione: (2025)

Beyond ESM2: Graph-Enhanced Protein Sequence Modeling with Efficient Clustering
di: Jiao, Shujian, et al.
Pubblicazione: (2024)

Target-Specific De Novo Peptide Binder Design with DiffPepBuilder
di: Wang, Fanhao, et al.
Pubblicazione: (2024)

Fast and Accurate Antibody Sequence Design via Structure Retrieval
di: Zhang, Xingyi, et al.
Pubblicazione: (2025)

COMET: Benchmark for Comprehensive Biological Multi-omics Evaluation Tasks and Language Models
di: Ren, Yuchen, et al.
Pubblicazione: (2024)

RNACG: A Universal RNA Sequence Conditional Generation model based on Flow-Matching
di: Gao, Letian, et al.
Pubblicazione: (2024)

BetterBodies: Reinforcement Learning guided Diffusion for Antibody Sequence Design
di: Vogt, Yannick, et al.
Pubblicazione: (2024)

CoPRA: Bridging Cross-domain Pretrained Sequence Models with Complex Structures for Protein-RNA Binding Affinity Prediction
di: Han, Rong, et al.
Pubblicazione: (2024)

A Comprehensive Review of Emerging Approaches in Machine Learning for De Novo PROTAC Design
di: Gharbi, Yossra, et al.
Pubblicazione: (2024)

Multi-Modal Representation Learning for Molecular Property Prediction: Sequence, Graph, Geometry
di: Wang, Zeyu, et al.
Pubblicazione: (2024)

Dirichlet Flow Matching with Applications to DNA Sequence Design
di: Stark, Hannes, et al.
Pubblicazione: (2024)

Importance Weighted Expectation-Maximization for Protein Sequence Design
di: Song, Zhenqiao, et al.
Pubblicazione: (2023)

ProDCARL: Reinforcement Learning-Aligned Diffusion Models for De Novo Antimicrobial Peptide Design
di: Sheng, Fang, et al.
Pubblicazione: (2026)

UniMoMo: Unified Generative Modeling of 3D Molecules for De Novo Binder Design
di: Kong, Xiangzhe, et al.
Pubblicazione: (2025)

Molecular Fingerprints Are Strong Models for Peptide Function Prediction
di: Adamczyk, Jakub, et al.
Pubblicazione: (2025)

Exploring Discrete Flow Matching for 3D De Novo Molecule Generation
di: Dunn, Ian, et al.
Pubblicazione: (2024)

A Review on Fragment-based De Novo 2D Molecule Generation
di: Voloboev, Sergei
Pubblicazione: (2024)

Bayesian Optimization of Antibodies Informed by a Generative Model of Evolving Sequences
di: Amin, Alan Nawzad, et al.
Pubblicazione: (2024)

Efficiently Predicting Mutational Effect on Homologous Proteins by Evolution Encoding
di: Zhong, Zhiqiang, et al.
Pubblicazione: (2024)

Mixed Continuous and Categorical Flow Matching for 3D De Novo Molecule Generation
di: Dunn, Ian, et al.
Pubblicazione: (2024)

Prot2Chat: Protein LLM with Early-Fusion of Text, Sequence and Structure
di: Wang, Zhicong, et al.
Pubblicazione: (2025)

Beyond Sequence: Impact of Geometric Context for RNA Property Prediction
di: Xu, Junjie, et al.
Pubblicazione: (2024)

Sequence-based protein-protein interaction prediction and its applications in drug discovery
di: Charih, François, et al.
Pubblicazione: (2025)

RiboGen: RNA Sequence and Structure Co-Generation with Equivariant MultiFlow
di: Rubin, Dana, et al.
Pubblicazione: (2025)

Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Backbone Generation
di: Huguet, Guillaume, et al.
Pubblicazione: (2024)