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.
A new GNN module learns geometric scattering features for better graph classification and feature exploration.
We present a three-dimensional graph convolutional network (3DGCN), which predicts molecular properties and biochemical activities, based on 3D molecular graph. In the 3DGCN, graph convolution is unified with learning operations on the vector to handle the spatial information from molecular topology. The 3DGCN model ex…
Geometric modeling for human food and chemical sensitivities.
The problem of accelerating drug discovery relies heavily on automatic tools to optimize precursor molecules to afford them with better biochemical properties. Our work in this paper substantially extends prior state-of-the-art on graph-to-graph translation methods for molecular optimization. In particular, we realize …
Bayesian inference for biochemical reaction networks using jump-diffusion approximations.
Graph Neural Network (GNN) is a popular architecture for the analysis of chemical molecules, and it has numerous applications in material and medicinal science. Current lines of GNNs developed for molecular analysis, however, do not fit well on the training set, and their performance does not scale well with the comple…
Graph Beta Diffusion (GBD) generates graphs with mixed discrete and continuous components.
We present an analysis of the problem of identifying biological context and associating it with biochemical events in biomedical texts. This constitutes a non-trivial, inter-sentential relation extraction task. We focus on biological context as descriptions of the species, tissue type and cell type that are associated …
We propose the time-dependent generalization of an `ordinary' autonomous human biomechanics, in which total mechanical + biochemical energy is not conserved. We introduce a general framework for time-dependent biomechanics in terms of jet manifolds derived from the extended musculo-skeletal configuration manifold. The …
Novel parallel GNN predicts protein-ligand interactions with high accuracy.
Method learns drug-disease representations for repositioning opportunities.
NLP techniques improve drug discovery by analyzing chemical and protein text.
New model uses pretrained biochemical language models to generate drug compounds.
Complex biological systems have been successfully modeled by biochemical and genetic interaction networks, typically gathered from high-throughput (HTP) data. These networks can be used to infer functional relationships between genes or proteins. Using the intuition that the topological role of a gene in a network rela…
Neural network model improves leaf spectral reflectance prediction for grapevines.
MEP-Net uses MEP to generate solutions from limited data.
In many high-throughput experimental design settings, such as those common in biochemical engineering, batched queries are more cost effective than one-by-one sequential queries. Furthermore, it is often not possible to directly choose items to query. Instead, the experimenter specifies a set of constraints that genera…
Chemical networks outperform spiking neural networks in classification tasks.
Standard ChIP-seq peak calling pipelines seek to differentiate biochemically reproducible signals of individual genomic elements from background noise. However, reproducibility alone does not imply functional regulation (e.g., enhancer activation, alternative splicing). Here we present a general-purpose, interpretable …
Graphs are commonly used to characterise interactions between objects of interest. Because they are based on a straightforward formalism, they are used in many scientific fields from computer science to historical sciences. In this paper, we give an introduction to some methods relying on graphs for learning. This incl…
nUDEs use neural networks to model biology without negative values.
BBRT improves molecular properties through iterative translation.
SparseChem speeds up ML for small molecules.
Proteins are commonly used by biochemical industry for numerous processes. Refining these proteins' properties via mutations causes stability effects as well. Accurate computational method to predict how mutations affect protein stability are necessary to facilitate efficient protein design. However, accuracy of predic…
Bayesian approach infers signaling pathways from data.
We propose a method for learning cyclic causal models from a combination of observational and interventional equilibrium data. Novel aspects of the proposed method are its ability to work with continuous data (without assuming linearity) and to deal with feedback loops. Within the context of biochemical reactions, we a…
In systems biology, it is common to measure biochemical entities at different levels of the same biological system. One of the central problems for the data fusion of such data sets is the heterogeneity of the data. This thesis discusses two types of heterogeneity. The first one is the type of data, such as metabolomic…
Design of experiments improves validation of biomolecular networks.
Local search improves GFlowNets' ability to generate high-reward samples.
This thesis tackles causality in machine learning, improving OOD generalization and robustness.
Study shows annealing with adaptive schedule reduces mode collapse in NFs for parameter estimation.
New algorithm improves model generalization in structured biomedical domains.
Deep neural networks correct Mie scattering in FTIR spectra of biological samples.
During the past decade, with the significant progress of computational power as well as ever-rising data availability, deep learning techniques became increasingly popular due to their excellent performance on computer vision problems. The size of the Protein Data Bank has increased more than 15 fold since 1999, which …
PUMA interprets metabolomics data to predict pathway activity and assign chemical identities.
New method infers dynamical systems from population data.
Framework predicts mortality risk in MAFLD subjects.
MoleculeSTM learns from molecule structures and texts for better drug design.
Deviance-style normalization for sparse, jointly overdispersed count matrices
Networks have in recent years emerged as an invaluable tool for describing and quantifying complex systems in many branches of science. Recent studies suggest that networks often exhibit hierarchical organization, where vertices divide into groups that further subdivide into groups of groups, and so forth over multiple…
Enhances drug discovery models by understanding human language.
Proposes using entity embedding vectors to improve Gaussian Process models for knowledge transfer across cell lines.
Adaptive teacher improves sample efficiency and mode coverage in sampling tasks.
Understanding the adaptation process of plants to drought stress is essential in improving management practices, breeding strategies as well as engineering viable crops for a sustainable agriculture in the coming decades. Hyper-spectral imaging provides a particularly promising approach to gain such understanding since…
In this paper we propose the time-dependent generalization of an `ordinary' autonomous human biomechanics, in which total mechanical + biochemical energy is not conserved. We introduce a general framework for time-dependent biomechanics in terms of jet manifolds associated to the extended musculo-skeletal configuration…
New method optimises learning via surrogate PAC-Bayes bounds.
A fundamental aspect of biological information processing is the ubiquity of sequence-function relationships -- functions that map the sequence of DNA, RNA, or protein to a biochemically relevant activity. Most sequence-function relationships in biology are quantitative, but only recently have experimental techniques f…
Stochastic fluctuations of molecule numbers are ubiquitous in biological systems. Important examples include gene expression and enzymatic processes in living cells. Such systems are typically modelled as chemical reaction networks whose dynamics are governed by the Chemical Master Equation. Despite its simple structur…