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.
ProGen models protein sequences for synthetic biology.
Method uses network biology to construct gene expression models for cancer.
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…
Quantum computing promises faster bioinformatics, but challenges remain.
DAG-FOCI learns causal relationships without parametric assumptions.
DECAT framework evaluates multimodal models for shared biology, detecting confounders and false positives.
Biology has changed radically in the last two decades, transitioning from a descriptive science into a design science. Synthetic biology allows us to bioengineer cells to synthesize novel valuable molecules such as renewable biofuels or anticancer drugs. However, traditional synthetic biology approaches involve ad-hoc …
Optimal algorithm selects biological models without prior info.
Two simulation-based methods improve optimal sampling design in systems biology.
Despite an explosion in the number of experimentally determined, atomically detailed structures of biomolecules, many critical tasks in structural biology remain data-limited. Whether performance in such tasks can be improved by using large repositories of tangentially related structural data remains an open question. …
Several real problems ranging from text classification to computational biology are characterized by hierarchical multi-label classification tasks. Most of the methods presented in literature focused on tree-structured taxonomies, but only few on taxonomies structured according to a Directed Acyclic Graph (DAG). In thi…
New method learns SDEs with structured noise from data.
We compute Khovanov homology for tangles using TQFT.
This paper proposes a new method to generate protein structures using deep learning.
A new diffusion model improves cryo-EM structure sampling.
A scalable Bayesian inference method for mixed-effects models in systems biology.
Generative models of graph structure have applications in biology and social sciences. The state of the art is GraphRNN, which decomposes the graph generation process into a series of sequential steps. While effective for modest sizes, it loses its permutation invariance for larger graphs. Instead, we present a permuta…
Mathematician summarizes protein geometry and mutation effects.
Capsule Networks have great potential to tackle problems in structural biology because of their attention to hierarchical relationships. This paper describes the implementation and application of a Capsule Network architecture to the classification of RAS protein family structures on GPU-based computational resources. …
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…
nUDEs use neural networks to model biology without negative values.
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…
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…
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…
hyperSBINN improves drug cardiosafety assessment by efficiently modeling cardiac action potentials.
ABI adapts to graph data for fast, scalable inference.
Bridging the exponentially growing gap between the numbers of unlabeled and labeled protein sequences, several studies adopted semi-supervised learning for protein sequence modeling. In these studies, models were pre-trained with a substantial amount of unlabeled data, and the representations were transferred to variou…
Structure learning in random fields has attracted considerable attention due to its difficulty and importance in areas such as remote sensing, computational biology, natural language processing, protein networks, and social network analysis. We consider the problem of estimating the probabilistic graph structure associ…
As proteins with similar structures often have similar functions, analysis of protein structures can help predict protein functions and is thus important. We consider the problem of protein structure classification, which computationally classifies the structures of proteins into pre-defined groups. We develop a weight…
Many applications in speech, robotics, finance, and biology deal with sequential data, where ordering matters and recurrent structures are common. However, this structure cannot be easily captured by standard kernel functions. To model such structure, we propose expressive closed-form kernel functions for Gaussian proc…
X-SHAP assesses multiplicative variable contributions in machine learning models.
BioBO optimizes gene perturbation design using Bayesian optimization with biological priors.
Two new minor minimal intrinsically chiral graphs identified.
Geometric modeling for human food and chemical sensitivities.
BaCaDI discovers causal structures from unknown interventions.
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-…
Optimizes causal effects on unknown graphs using Causal Entropy Optimization.
Graph kernels assess graph similarity for various applications.
New method finds all thin film structures from reflectometry data.
New algorithm optimizes matrix reordering for noisy disordered matrices.
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…
New model for detecting communities in weighted bipartite networks.
GCML preserves geometric structure in manifold clustering for diverse data types.
COMRECGC finds common recourse for global counterfactual explanations in GNNs.
DAMNETS generates complex network dynamics models.
Physics analogies explain machine learning overfitting control.
SCOTCH learns system structure from irregular time series using neural SDEs.
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…