:: Library Catalog

Slika na naslovnici

Shranjeno v:

Bibliografske podrobnosti
Main Authors:	Fannjiang, Clara, Listgarten, Jennifer
Format:	Preprint
Izdano:	2020
Teme:	Machine Learning Quantitative Methods
Online dostop:	https://arxiv.org/abs/2006.08052
Oznake:	Označite Brez oznak, prvi označite!

Podobne knjige/članki

Is novelty predictable?
od: Fannjiang, Clara, et al.
Izdano: (2023)

Conformal Prediction Under Feedback Covariate Shift for Biomolecular Design
od: Fannjiang, Clara, et al.
Izdano: (2022)

Reliable algorithm selection for machine learning-guided design
od: Fannjiang, Clara, et al.
Izdano: (2025)

Controllable protein design with particle-based Feynman-Kac steering
od: Hartman, Erik, et al.
Izdano: (2025)

Why risk matters for protein binder design
od: Cotet, Tudor-Stefan, et al.
Izdano: (2025)

Deep learning-guided evolutionary optimization for protein design
od: Hartman, Erik, et al.
Izdano: (2026)

Assay2Mol: large language model-based drug design using BioAssay context
od: Deng, Yifan, et al.
Izdano: (2025)

Understanding temperature tuning in energy-based models
od: Fields, Peter W, et al.
Izdano: (2025)

Learning surrogate equations for the analysis of an agent-based cancer model
od: Burrage, Kevin, et al.
Izdano: (2025)

T-cell receptor specificity landscape revealed through de novo peptide design
od: Visani, Gian Marco, et al.
Izdano: (2025)

Loop-Diffusion: an equivariant diffusion model for designing and scoring protein loops
od: Borisiak, Kevin, et al.
Izdano: (2024)

Exploring the limits of pre-trained embeddings in machine-guided protein design: a case study on predicting AAV vector viability
od: Rodrigues, Ana F., et al.
Izdano: (2026)

From thermodynamics to protein design: Diffusion models for biomolecule generation towards autonomous protein engineering
od: Li, Wen-ran, et al.
Izdano: (2025)

gRNAde: Geometric Deep Learning for 3D RNA inverse design
od: Joshi, Chaitanya K., et al.
Izdano: (2023)

Prenatal Stress Detection from Electrocardiography Using Self-Supervised Deep Learning: Development and External Validation
od: Frasch, Martin G., et al.
Izdano: (2026)

Inferring processes within dynamic forest models using hybrid modeling
od: Pichler, Maximilian, et al.
Izdano: (2025)

The limits of bio-molecular modeling with large language models : a cross-scale evaluation
od: Xu, Yaxin, et al.
Izdano: (2026)

Benchmarking community drug response prediction models: datasets, models, tools, and metrics for cross-dataset generalization analysis
od: Partin, Alexander, et al.
Izdano: (2025)

CoVAE: correlated multimodal generative modeling
od: Caretti, Federico, et al.
Izdano: (2026)

Simulation-based Inference for Cardiovascular Models
od: Wehenkel, Antoine, et al.
Izdano: (2023)

Simulation-based inference of yeast centromeres
od: Touron, Eloïse, et al.
Izdano: (2025)

Do we need equivariant models for molecule generation?
od: Nowara, Ewa M., et al.
Izdano: (2025)

Protenix-Mini+: efficient structure prediction model with scalable pairformer
od: Qiang, Bo, et al.
Izdano: (2025)

Omics-driven hybrid dynamic modeling of bioprocesses with uncertainty estimation
od: Espinel-Ríos, Sebastián, et al.
Izdano: (2024)

Improved prediction of ligand-protein binding affinities by meta-modeling
od: Lee, Ho-Joon, et al.
Izdano: (2023)

The CAST package for training and assessment of spatial prediction models in R
od: Meyer, Hanna, et al.
Izdano: (2024)

Enhancing selectivity using Wasserstein distance based reweighing
od: Worah, Pratik
Izdano: (2024)

ImmunoLingo: Linguistics-based formalization of the antibody language
od: Vu, Mai Ha, et al.
Izdano: (2022)

Graph Residual based Method for Molecular Property Prediction
od: Sen, Kanad, et al.
Izdano: (2024)

Understanding protein function with a multimodal retrieval-augmented foundation model
od: Truong Jr, Timothy Fei, et al.
Izdano: (2025)

PoET: A generative model of protein families as sequences-of-sequences
od: Truong Jr, Timothy F., et al.
Izdano: (2023)

New tools for comparing classical and neural ODE models for tumor growth
od: Blaom, Anthony D., et al.
Izdano: (2025)

Linguistically inspired roadmap for building biologically reliable protein language models
od: Vu, Mai Ha, et al.
Izdano: (2022)

How to make the most of your masked language model for protein engineering
od: McCarter, Calvin, et al.
Izdano: (2026)

Accurate RNA 3D structure prediction using a language model-based deep learning approach
od: Shen, Tao, et al.
Izdano: (2022)

Transition Path Sampling with Boltzmann Generator-based MCMC Moves
od: Plainer, Michael, et al.
Izdano: (2023)

Edge-aware GAT-based protein binding site prediction
od: Yang, Weisen, et al.
Izdano: (2026)

The cell as a token: high-dimensional geometry in language models and cell embeddings
od: Gilpin, William
Izdano: (2025)

DeepDR: an integrated deep-learning model web server for drug repositioning
od: Jin, Shuting, et al.
Izdano: (2025)

An inferential measure of dependence between two systems using Bayesian model comparison
od: Marrelec, Guillaume, et al.
Izdano: (2024)