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.
Two simulation-based methods improve optimal sampling design in systems biology.
Optimal algorithm selects biological models without prior info.
A scalable Bayesian inference method for mixed-effects models in systems biology.
ART automates synthetic biology design with machine learning.
hyperSBINN improves drug cardiosafety assessment by efficiently modeling cardiac action potentials.
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…
New method learns cell trajectories and network interactions from single-cell data.
We address the problem of parameter estimation in models of systems biology from noisy observations. The models we consider are characterized by simultaneous deterministic nonlinear differential equations whose parameters are either taken from in vitro experiments, or are hand-tuned during the model development process…
nUDEs use neural networks to model biology without negative values.
This thesis tackles data fusion issues across different biological scales and types.
New method integrates sparse parametric and nonparametric techniques for complex system modeling.
Method infers latent factors influencing time-varying networks.
Complex behaviour in many systems arises from the stochastic interactions of spatially distributed particles or agents. Stochastic reaction-diffusion processes are widely used to model such behaviour in disciplines ranging from biology to the social sciences, yet they are notoriously difficult to simulate and calibrate…
Quantitative modeling of post-transcriptional regulation process is a challenging problem in systems biology. A mechanical model of the regulatory process needs to be able to describe the available spatio-temporal protein concentration and mRNA expression data and recover the continuous spatio-temporal fields. Rigorous…
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…
New method learns SDEs with structured noise from data.
Polynomial-time algorithm finds short non-orientable loops intersecting graph edges up to 30 times.
MSBM extends SB for multi-marginal trajectory inference.
Graphical modelling has a long history in statistics as a tool for the analysis of multivariate data, starting from Wright's path analysis and Gibbs' applications to statistical physics at the beginning of the last century. In its modern form, it was pioneered by Lauritzen and Wermuth and Pearl in the 1980s, and has si…
Machine learning integrates diverse biological data to understand complex phenomena.
In this paper, we show how simple logistic growth that was studied intensively during the last 200 years in many domains of science could be extended in a rather simple way and with these extensions is capable to produce a collection of behaviors widely observed in an enormous number of real-life systems in Economics, …
A new method for inferring latent states in Markov jump processes.
Study tackles high-dimensional datasets in molecular biology by reducing intrinsic variability.
DECAT framework evaluates multimodal models for shared biology, detecting confounders and false positives.
The aim of this paper is to construct a natural Riemann-Lagrange differential geometry on 1-jet spaces, in the sense of nonlinear connections, generalized Cartan connections, d-torsions, d-curvatures, jet electromagnetic fields and jet electromagnetic Yang-Mills energies, starting from some given nonlinear evolution OD…
The aim of this chapter is twofold. In the first part we will provide a brief overview of the mathematical and statistical foundations of graphical models, along with their fundamental properties, estimation and basic inference procedures. In particular we will develop Markov networks (also known as Markov random field…
COMRECGC finds common recourse for global counterfactual explanations in GNNs.
Popular online enrichment analysis tools from the field of molecular systems biology provide users with the ability to submit their experimental results as gene sets for individual analysis. Such queries are kept private, and have never before been considered as a resource for integrative analysis. By harnessing gene s…
Purely data driven approaches for machine learning present difficulties when data is scarce relative to the complexity of the model or when the model is forced to extrapolate. On the other hand, purely mechanistic approaches need to identify and specify all the interactions in the problem at hand (which may not be feas…
High dimensional time series are endemic in applications of machine learning such as robotics (sensor data), computational biology (gene expression data), vision (video sequences) and graphics (motion capture data). Practical nonlinear probabilistic approaches to this data are required. In this paper we introduce the v…
By analyzing the relationships between a socioeconomical system modeled through evolutionary game theory and a physical system modeled through quantum mechanics we show how although both systems are described through two theories apparently different both are analogous and thus exactly equivalents. The extensions of qu…
A new method uses bandits to select summary statistics for Bayesian inference.
Mathematical advances needed for Digital Twins, differing from traditional models.
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…
Bayesian framework for robust model discovery from noisy data.
Quantum computing promises faster bioinformatics, but challenges remain.
In Biology, all motor enzymes operate on the same principle: they trap favourable brownian fluctuations in order to generate directed forces and to move. Whether it is possible or not to copy one such strategy to play the market was the starting point of our investigations. We found the answer is yes. In this paper we …
ProGen models protein sequences for synthetic biology.
Method uses network biology to construct gene expression models for cancer.
The relationships between game theory and quantum mechanics let us propose certain quantization relationships through which we could describe and understand not only quantum but also classical, evolutionary and the biological systems that were described before through the replicator dynamics. Quantum mechanics could be…
New algorithm for continuous-time switching systems using variational inference.
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…
Critical learning periods found in deep linear networks too.
DCBO optimizes interventions in evolving causal systems.
This work develops a learning theory for inferring interaction kernels in complex agent systems.
DyMoN models complex systems from short snapshots using deep neural networks.
New framework for online control in evolving populations.
GP-NODE combines Gaussian processes and NeuralODEs for Bayesian system identification.