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.
TNDE quantifies dynamic gene drivers from single-cell snapshots.
DASH simplifies neural networks for gene regulatory dynamics using domain knowledge.
Efficiently infers gene regulatory networks from spatial data.
InfoSEM infers gene regulatory networks without GT labels, improving performance.
Develops probabilistic models for gene regulatory network inference.
Reconstructing transcriptional regulatory networks is an important task in functional genomics. Data obtained from experiments that perturb genes by knockouts or RNA interference contain useful information for addressing this reconstruction problem. However, such data can be limited in size and/or are expensive to acqu…
Constructing gene regulatory networks is a critical step in revealing disease mechanisms from transcriptomic data. In this work, we present NO-BEARS, a novel algorithm for estimating gene regulatory networks. The NO-BEARS algorithm is built on the basis of the NOTEARS algorithm with two improvements. First, we propose …
Gene regulatory networks play a crucial role in controlling an organism's biological processes, which is why there is significant interest in developing computational methods that are able to extract their structure from high-throughput genetic data. A typical approach consists of a series of conditional independence t…
We present a Bayesian hierarchical multi-view mixture model termed Symphony that simultaneously learns clusters of cells representing cell types and their underlying gene regulatory networks by integrating data from two views: single-cell gene expression data and paired epigenetic data, which is informative of gene-gen…
SEISM tests neural network features for regulatory genomics.
Regshock visualizes financial risks to help regulators manage systemic shocks.
LAGE is a systematic framework developed in Java. The motivation of LAGE is to provide a scalable and parallel solution to reconstruct Gene Regulatory Networks (GRNs) from continuous gene expression data for very large amount of genes. The basic idea of our framework is motivated by the philosophy of divideand-conquer.…
Model explains money creation under regulatory constraints.
Gene regulatory networks play a crucial role in controlling an organism's biological processes, which is why there is significant interest in developing computational methods that are able to extract their structure from high-throughput genetic data. Many of these computational methods are designed to infer individual …
Algorithm recovers large causal tree from small samples.
New method infers causal factors from large-scale data without full graph reconstruction.
New method constructs confidence bands for ODE models with unknown regulatory effects.
Quantitatively predicting phenotype variables by the expression changes in a set of candidate genes is of great interest in molecular biology but it is also a challenging task for several reasons. First, the collected biological observations might be heterogeneous and correspond to different biological mechanisms. Seco…
This paper is concerned with the problem of stochastic control of gene regulatory networks (GRNs) observed indirectly through noisy measurements and with uncertainty in the intervention inputs. The partial observability of the gene states and uncertainty in the intervention process are accounted for by modeling GRNs us…
Generative model for inferring graph from time series data.
Monotonic neural additive models simplify machine learning for credit scoring.
Inference of gene regulatory network from expression data is a challenging task. Many methods have been developed to this purpose but a comprehensive evaluation that covers unsupervised, semi-supervised and supervised methods, and provides guidelines for their practical application, is lacking. We performed an extensiv…
Over the last 23 years, the U.S. Securities and Exchange Commission has required over 34,000 companies to file over 165,000 annual reports. These reports, the so-called "Form 10-Ks," contain a characterization of a company's financial performance and its risks, including the regulatory environment in which a company op…
Neural GDEs improve graph prediction by blending discrete structures and differential equations.
In this paper we propose network methodology to infer prognostic cancer biomarkers based on the epigenetic pattern DNA methylation. Epigenetic processes such as DNA methylation reflect environmental risk factors, and are increasingly recognised for their fundamental role in diseases such as cancer. DNA methylation is a…
ZICO learns DAGs from zero-inflated count data efficiently.
We consider the task of detecting regulatory elements in the human genome directly from raw DNA. Past work has focused on small snippets of DNA, making it difficult to model long-distance dependencies that arise from DNA's 3-dimensional conformation. In order to study long-distance dependencies, we develop and release …
The paper develops a scalable method to infer GRNs from sparse data.
This paper examines market misconduct in DeFi and proposes regulatory solutions.
A standard technique for understanding underlying dependency structures among a set of variables posits a shared conditional probability distribution for the variables measured on individuals within a group. This approach is often referred to as module networks, where individuals are represented by nodes in a network, …
Paper develops a framework to discover bioprocessing regulatory mechanisms using symbolic and statistical learning.
A new method infers causal gene regulatory networks from parallel CRISPR interventions and transcriptomic data.
regvis.net offers a visual survey of regulatory visualization.
Optimizes insurance profits under regulatory constraints.
Oscillations lie at the core of many biological processes, from the cell cycle, to circadian oscillations and developmental processes. Time-keeping mechanisms are essential to enable organisms to adapt to varying conditions in environmental cycles, from day/night to seasonal. Transcriptional regulatory networks are one…
In a market system, regulations are designed to prevent or rectify market failures that inhibit fair exchange, such as monopoly or transactions with hidden costs. Because regulations reduce profits to those possessing unfair advantage, these advantaged corporations (whether individuals, companies, or other collective o…
GO-CBED optimizes experiments for specific causal queries, improving efficiency.
Convolutional neural networks learn effective summary statistics for ABC inference.
Paper constructs a CRRIX index to assess cryptocurrency market risks from regulatory changes.
We report a scalable hybrid quantum-classical machine learning framework to build Bayesian networks (BN) that captures the conditional dependence and causal relationships of random variables. The generation of a BN consists of finding a directed acyclic graph (DAG) and the associated joint probability distribution of t…
Causal methods for GRN inference from single-cell data often fail in real-world benchmarks.
Proposes a new method for determining LGD discount rates based on cost of capital.
Funding is a cost to trading desks that they see as an input. Current FVA-related literature reflects this by also taking funding costs as an input, usually constant, and always risk-neutral. However, this funding curve is the output from a Treasury point of view. Treasury must consider Regulatory-required liquidity bu…
Simple method calculates WWR for regulatory and accounting purposes.
Model predicts Chinese stock market liquidity and customer order behavior.
This paper explains tax policy for crypto assets in a rapidly evolving tech landscape.
Cryptocurrency markets show similar returns but different volatility responses to infrastructure and regulatory shocks.
New framework for adaptive clinical trials to address real-world challenges.