A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
ART automates synthetic biology design with machine learning.
ProGen models protein sequences for synthetic biology.
DECAT framework evaluates multimodal models for shared biology, detecting confounders and false positives.
We consider continuous time Markovian processes where populations of individual agents interact stochastically according to kinetic rules. Despite the increasing prominence of such models in fields ranging from biology to smart cities, Bayesian inference for such systems remains challenging, as these are continuous tim…
MSBM extends SB for multi-marginal trajectory inference.
CHANI learns classification tasks with local transformations inspired by biology.
Active learning selects optimal measurement times for inferring continuous paths from sparse data.
ABI adapts to graph data for fast, scalable inference.
Optimal algorithm selects biological models without prior info.
SyNGLER generates synthetic networks efficiently while preserving key structural properties.
Serial crystallography is the field of science that studies the structure and properties of crystals via diffraction patterns. In this paper, we introduce a new serial crystallography dataset comprised of real and synthetic images; the synthetic images are generated through the use of a simulator that is both scalable …
Two simulation-based methods improve optimal sampling design in systems biology.
DAMNETS generates complex network dynamics models.
Quantum computing promises faster bioinformatics, but challenges remain.
When learning a hidden Markov model (HMM), sequen- tial observations can often be complemented by real-valued summary response variables generated from the path of hid- den states. Such settings arise in numerous domains, includ- ing many applications in biology, like motif discovery and genome annotation. In this pape…
A scalable Bayesian inference method for mixed-effects models in systems biology.
In many applications of finance, biology and sociology, complex systems involve entities interacting with each other. These processes have the peculiarity of evolving over time and of comprising latent factors, which influence the system without being explicitly measured. In this work we present latent variable time-va…
A new diffusion model improves cryo-EM structure sampling.
Method uses network biology to construct gene expression models for cancer.
Likelihood-free methods are an established approach for performing approximate Bayesian inference for models with intractable likelihood functions. However, they can be computationally demanding. Bayesian synthetic likelihood (BSL) is a popular such method that approximates the likelihood function of the summary statis…
MNIST-Nd offers synthetic datasets to benchmark clustering across dimensions.
We consider the problem of change-point detection in multivariate time-series. The multivariate distribution of the observations is supposed to follow a graphical model, whose graph and parameters are affected by abrupt changes throughout time. We demonstrate that it is possible to perform exact Bayesian inference when…
Estimates support in distributions with sampling artifacts and errors.
Deep learning reduces artifacts in limited angle X-ray microscopy.
In this paper, we provide conditions which ensure that stochastic Lipschitz BSDEs admit Malliavin differentiable solutions. We investigate the problem of existence of densities for the first components of solutions to general path-dependent stochastic Lipschitz BSDEs and obtain results for the second components in part…
We study the sample complexity of learning a high-dimensional simplex from a set of points uniformly sampled from its interior. Learning of simplices is a long studied problem in computer science and has applications in computational biology and remote sensing, mostly under the name of `spectral unmixing'. We theoretic…
Kernel-based support vector machines (SVMs) are supervised machine learning algorithms for classification and regression problems. We introduce a method to train SVMs on a D-Wave 2000Q quantum annealer and study its performance in comparison to SVMs trained on conventional computers. The method is applied to both synth…
Group factor analysis (GFA) methods have been widely used to infer the common structure and the group-specific signals from multiple related datasets in various fields including systems biology and neuroimaging. To date, most available GFA models require Gibbs sampling or slice sampling to perform inference, which prev…
Method infers basic features of composite data using replicated autoencoders.
Normalizing flow regression approximates posterior distributions without additional sampling.
nUDEs use neural networks to model biology without negative values.
The paper presents a new method to represent directed graphs using pseudo-Riemannian manifolds.
Design of experiments improves validation of biomolecular networks.
New technologies have enabled the investigation of biology and human health at an unprecedented scale and in multiple dimensions. These dimensions include a myriad of properties describing genome, epigenome, transcriptome, microbiome, phenotype, and lifestyle. No single data type, however, can capture the complexity of…
We introduce a new method for estimating the support size of an unknown distribution which provably matches the performance bounds of the state-of-the-art techniques in the area and outperforms them in practice. In particular, we present both theoretical and computer simulation results that illustrate the utility and p…
hyperSBINN improves drug cardiosafety assessment by efficiently modeling cardiac action potentials.
dtSNE preserves local densities in low-dimensional embeddings.
Non-linear systems of differential equations have attracted the interest in fields like system biology, ecology or biochemistry, due to their flexibility and their ability to describe dynamical systems. Despite the importance of such models in many branches of science they have not been the focus of systematic statisti…
We consider the problem of joint modelling of metabolic signals and gene expression in systems biology applications. We propose an approach based on input-output factorial hidden Markov models and propose a structured variational inference approach to infer the structure and states of the model. We start from the class…
BaCaDI discovers causal structures from unknown interventions.
Develops a method to infer cell trajectories from RNA sequencing data.
In this paper, we describe a dataset relating to cellular and physical conditions of patients who are operated upon to remove colorectal tumours. This data provides a unique insight into immunological status at the point of tumour removal, tumour classification and post-operative survival. Attempts are made to learn re…
Determining the 3D structures of biological molecules is a key problem for both biology and medicine. Electron Cryomicroscopy (Cryo-EM) is a promising technique for structure estimation which relies heavily on computational methods to reconstruct 3D structures from 2D images. This paper introduces the challenging Cryo-…
Generating novel graph structures that optimize given objectives while obeying some given underlying rules is fundamental for chemistry, biology and social science research. This is especially important in the task of molecular graph generation, whose goal is to discover novel molecules with desired properties such as …
X-SHAP assesses multiplicative variable contributions in machine learning models.
Generative Distribution Embeddings learn multiscale representations of distributions.
Reconstruction of structure and parameters of an Ising model from binary samples is a problem of practical importance in a variety of disciplines, ranging from statistical physics and computational biology to image processing and machine learning. The focus of the research community shifted towards developing universal…
BioBO optimizes gene perturbation design using Bayesian optimization with biological priors.