7 papers · ranked by Valyu relevance
Ray Zirui Zhang, Christopher E. Miles, Xiaohui Xie, John S. Lowengrub
We propose a new neural network based method for solving inverse problems for partial differential equations (PDEs) by formulating the PDE inverse problem as a bilevel optimization problem. At the upper level, we minimize the data loss with respect to the PDE parameters. At the lower level, we train a neural network to…
Madalina Ciortan, Matthieu Defrance
Subspace clustering identifies multiple feature subspaces embedded in a dataset together with the underlying sample clusters. When applied to omic data, subspace clustering is a challenging task, as additional problems have to be addressed: the curse of dimensionality, the imperfect data quality and cluster separation…
Samuel A. Budoff, Alon Poleg-Polsky
This paper introduces a machine learning approach, GraSP, for retinal cell classification that addresses key challenges in spatial biology, alongside a novel neural network architecture, CuttleNet, tailored for class and subclass inference with incomplete datasets. We propose an innovative, unbiased gene selection…
Huaxu Yu, Puja Biswas, Elizabeth Rideout, Yankai Cao + 1 more
Liquid chromatography (LC) with gradient elution is a routine practice for separating complex chemical mixtures in mass spectrometry (MS)-based untargeted analysis. Despite its prevalence, systematic optimization of LC gradients has remained challenging. Here we develop a Bayesian optimization method, BAGO, for…
An Wang, Donald Geman, Uthsav Chitra, Laurent Younes
Spatial transcriptomics (ST) technologies measure gene expression at thousands of locations within a two-dimensional tissue slice, enabling the study of spatial gene expression patterns. Spatial variation in gene expression is characterized by spatial gradients, or the collection of vector fields describing the…
Karl-Heinz Nenning, Ting Xu, Arielle Tambini, Alexandre R. Franco + 3 more
Brain connectome analysis suffers from the high dimensionality of connectivity data, often forcing a reduced representation of the brain at a lower spatial resolution or parcellation. However, maintaining high spatial resolution can both allow fine-grained topographical analysis and preserve subtle individual…
Or Zuk
We define and study the problem of genomic block selection for multiple complex traits. In this problem, one constructs a genome by selecting different genomic parts (e.g. chromosomes) from different source genomes. The constructed genome is associated with a vector of polygenic scores, obtained by summing the…