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Unified Variational Approach Description of Ground-State Phases of the Two-Dimensional Electron Gas.
Phys Rev Lett
December 2024
Flatiron Institute, Center for Computational Quantum Physics, New York, New York 10010, USA.
The two-dimensional electron gas (2DEG) is a fundamental model, which is drawing increasing interest because of recent advances in experimental and theoretical studies of 2D materials. Current understanding of the ground state of the 2DEG relies on quantum Monte Carlo calculations, based on variational comparisons of different Ansätze for different phases. We use a single variational ansatz, a general backflow-type wave function using a message-passing neural quantum state architecture, for a unified description across the entire density range.
View Article and Find Full Text PDFVAE-Surv: A novel approach for genetic-based clustering and prognosis prediction in myelodysplastic syndromes.
Comput Methods Programs Biomed
January 2025
Computational Biomedicine Unit, Department of Medical Sciences, University of Torino, Via Santena 19, 10126, Torino, Italy.
Background And Objectives: Several computational pipelines for biomedical data have been proposed to stratify patients and to predict their prognosis through survival analysis. However, these analyses are usually performed independently, without integrating the information derived from each of them. Clustering of survival data is an underexplored problem, and current approaches are limited for biomedical applications, whose data are usually heterogeneous and multimodal, with poor scalability for high-dimensionality.
View Article and Find Full Text PDFUnsupervised data imputation with multiple importance sampling variational autoencoders.
Sci Rep
January 2025
School of Mathematics and Statistics, Shaoguan University, Shaoguan, 512005, China.
Recently, deep latent variable models have made significant progress in dealing with missing data problems, benefiting from their ability to capture intricate and non-linear relationships within the data. In this work, we further investigate the potential of Variational Autoencoders (VAEs) in addressing the uncertainty associated with missing data via a multiple importance sampling strategy. We propose a Missing data Multiple Importance Sampling Variational Auto-Encoder (MMISVAE) method to effectively model incomplete data.
View Article and Find Full Text PDFA variational graph-partitioning approach to modeling protein liquid-liquid phase separation.
Cell Rep Phys Sci
November 2024
Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.
Graph neural networks (GNNs) have emerged as powerful tools for representation learning. Their efficacy depends on their having an optimal underlying graph. In many cases, the most relevant information comes from specific subgraphs.
View Article and Find Full Text PDFMapping conformational landscape in protein folding: Benchmarking dimensionality reduction and clustering techniques on the Trp-Cage mini-protein.
Biophys Chem
January 2025
Department of Chemical and Biological Sciences, S. N. Bose National Centre for Basic Sciences, Kolkata 700106, India. Electronic address:
Quantitative characterization of protein conformational landscapes is a computationally challenging task due to their high dimensionality and inherent complexity. In this study, we systematically benchmark several widely used dimensionality reduction and clustering methods to analyze the conformational states of the Trp-Cage mini-protein, a model system with well-documented folding dynamics. Dimensionality reduction techniques, including Principal Component Analysis (PCA), Time-lagged Independent Component Analysis (TICA), and Variational Autoencoders (VAE), were employed to project the high-dimensional free energy landscape onto 2D spaces for visualization.
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