13 papers · ranked by Valyu relevance
Meenu Ajith, Vince D. Calhoun
The development of diffusion models, such as Glide, DALLE 2, Imagen, and Stable Diffusion, marks a significant advancement in generative AI for image synthesis. In this paper, we introduce a novel framework for synthesizing intrinsic connectivity networks (ICNs) by utilizing the nonlinear capabilities of denoising…
Binze Shi, Jie Liu, Tong Pan, Yi Hao + 5 more
Proteolysis-targeting chimeras (PROTACs) enable targeted protein degradation through ternary complex formation with E3 ubiquitin ligase. However, the rational design of PROTACs remains highly challenging due to limited structure–activity relationship data and the vast conformational diversity of linkers. Existing…
Chuankai Dai, Kengo Sato
The 5′ untranslated region (UTR) and the start-codon-proximal region of the coding sequence (CDS) jointly influence translation efficiency and local RNA secondary-structure stability, while synonymous codon choices throughout the CDS shape codon adaptation. Because the encoded protein is often predetermined, practical…
Zixuan Jiang, Sitao Zhang, Rundong Huang, Shaoxun Mo + 7 more
Protein design with deep-learning based algorithms represents an emerging and highly promising field in molecular biology. Yet it remains a challenging task due to the complexity of protein sequences. Recent developments in deep generative models such as diffusion model have demonstrated impressive performance in…
Benjamin Kaufman, Edward C. Williams, Ryan Pederson, Carl Underkoffler + 8 more
Designing a small molecule therapeutic is a challenging multi-parameter optimization problem. Key properties, such as potency, selectivity, bioavailability, and safety must be jointly optimized to deliver an effective clinical candidate. We present COATI-LDM, a novel application of latent diffusion models to the…
Jingtian Xu, Yong Wang
Understanding and predicting the diverse conformational states of membrane proteins is essential for elucidating their biological functions. Despite advancements in computational methods, accurately capturing these complex structural changes remains a significant challenge. Here we introduce a computational approach to…
Xingnan Li, Priyanka Rana, Tuba N Gide, Nurudeen A Adegoke + 3 more
Stain imputation in multiplex immunofluorescence (mIF) imaging addresses the challenge of missing or damaged biomarker channels by reconstructing target biomarker images from a limited set of available stains. This approach offers a faster and more efficient alternative to full-panel staining, enabling detailed…
Wenzhuo Tang, Renming Liu, Hongzhi Wen, Xinnan Dai + 5 more
The fast-growing single-cell analysis community extends the horizon of quantitative analysis to numerous computational tasks. While the tasks hold vastly different targets from each other, existing works typically design specific model frameworks according to the downstream objectives. In this work, we propose a…
Robin Vloeberghs, Francis Tuerlinckx, Anne E. Urai, Kobe Desender
A widely used framework for studying the computational mechanisms of decision making is the Drift Diffusion Model (DDM). To account for the presence of both fast and slow errors in empirical data, the DDM incorporates across-trial variability in parameters such as the drift rate and the starting point. Although these…
Kai Trepka
Building models of organismal growth enables predictions of natural variability and responses to perturbations. Complex systems such as animal pattern development and bacterial colonies can be modeled numerically using a reaction-diffusion system with relatively few factors and yield qualitatively accurate results…
Vishesh Kumar, J. Shepard Bryan, Alex Rojewski, Carlo Manzo + 1 more
Diffusion coefficients often vary across regions, such as cellular membranes, and quantifying their variation can provide valuable insight into local membrane properties such as composition and stiffness. Toward quantifying diffusion coefficient spatial maps and uncertainties from particle tracks, we use a Bayesian…
José Augusto Fontenele Magalhães, Muhammad Fuady Emzir, Francesco Corona
In order to characterise the dynamics of a biochemical system such as the chemostat, we consider a differential description of the evolution of its state under environmental fluctuations. We present solutions to the filtering problem for a chemostat subjected to geometric Brownian motion. Under this modelling…
Shangying Wang, Sara Capponi, Simone Bianco
Biological systems are inherently noisy so that two genetically identical cells in the exact same environment will sometimes behave in dramatically different ways. This imposes a big challenge in building traditional supervised machine learning models that can only predict determined phenotypic variables or categories…