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Variational Monte Carlo is one of the most accurate methods to solve the many-electron Schrödinger equation, but suffers from high computational cost. A recent study uses a weight-sharing technique to accelerate the neural network-based variational Monte Carlo method, allowing accurate and effective simulations of molecules.
Determining the origin of engineered DNA can help to foster responsible innovation within the biotechnology community. A convolutional neural network approach that learns distances between engineered DNA sequences and various labs that could have created them is used to accurately predict the lab-of-origin.
A dynamic model of SARS-CoV-2 transmission is integrated with a 63-sector economic model to identify control strategies for optimizing economic production while keeping schools and universities operational, and for constraining infections such that emergency hospital capacity is not exceeded.
Biomimetic nanoparticles can form complexes with proteins. Structural descriptors have been identified to predict nanoparticle–protein complex formation and their interaction sites. These descriptors include geometrical and graph-theoretical molecular features that are universally applicable to all nanoscale macromolecules of both organic and inorganic chemistries.
A robust and reliable codec is the backbone for any digital DNA storage. A recent work introduces a codec based on ancient Chinese philosophy, yin–yang, that outperforms other codecs in terms of reliability and physical information density.
Stochastic modeling of antibody binding dynamics on patterned antigen substrates suggests the separation distance between adjacent antigens could be a control mechanism for the directed bipedal migration of bound antibodies.
Predicting the risk of acute graft-versus-host-disease after transplantation is challenging due to the presence of multimodal data and continuous evolution of disease states. A dynamic probabilistic algorithm has recently been proposed to address these challenges.
Scallop2, a tool that enables accurate reference-guided transcriptome assembly, capitalizes on recent developments in short-read sequencing protocols for single-cell RNA sequencing by leveraging multi-end and paired-end information.
A recent study proposes a mathematical model of SARS-CoV-2 to help identify mechanistic correlates of protection, which can be used to assist in determining vaccine efficacy.
A fully automated, high-throughput computational framework accurately predicts stable species in liquid solutions by computing the nuclear magnetic resonance chemical shifts. Data collected from the framework can provide fingerprints to guide the rational design of liquid solutions with optimal properties.
A combination of Bayesian inference, physics modeling, and Markov chain Monte Carlo sampling allows for accurate inference of biomolecule numbers and their photophysical state in cellular clusters.
Networks offer a powerful visual representation of complex systems. Cartographs introduce a diverse set of network layouts for highlighting and visually inspecting chosen characteristics of a network. The resulting visualizations are interpretable and can be used to explore complex datasets, such as large-scale biological networks.
A fast and accurate time–frequency analysis is challenging for many applications, especially in the current big data era. A recent work introduces a fast continuous wavelet transform that effectively boosts the analysis speed without sacrificing the resolution of the result.
Precise numerical simulations of turbulent flows in practical applications are still challenging. A recent quantum-inspired computational method improves the way to account for the interscale correlations in turbulence, and further sheds light on the development of quantum computing algorithms for efficient turbulence simulations.
Integrating multi-modal features is challenging due to the differences in the underlying distributions of each data type and the nonlinear associations across modalities. The deepManReg model improves the identification and interpretability of associations between modalities defining complex phenotypes.
There is still a wide variety of challenges that restrict the rapid growth of neuromorphic algorithmic and application development. Addressing these challenges is essential for the research community to be able to effectively use neuromorphic computers in the future.
An adaptive and computationally efficient machine-learning-based biasing technique for rare-event sampling is introduced, allowing an effective generation of high-dimensional free energy surfaces associated with complex processes, such as protein folding.
The accurate determination of correlation energy is a challenging task in many-electron quantum chemistry calculations, especially for metals. A recent work proposes an efficient scheme to speed up the calculation of correlation energy, reducing the computational time by up to two orders of magnitude.
A new study proposes a full-scale model of the entorhinal cortex–dentate gyrus–CA3 network, providing a conceptual overview of the computational properties of this brain network, to show that it is an efficient pattern separator.
