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.
We propose a method to model multi-agent behaviors with limited observation and mechanical constraints.
Oja's rule improves neural network training without engineered tricks.
The paper relaxes constraints on predictive coding models, making them more biologically plausible.
PGEL learns embeddings to diversify protein motifs while maintaining biological function.
BioBO optimizes gene perturbation design using Bayesian optimization with biological priors.
This study benchmarks transcriptomics models for perturbation analysis, finding scVI and PCA superior.
MicroRNAs (miRNAs) play crucial roles in multifarious biological processes associated with human diseases. Identifying potential miRNA-disease associations contributes to understanding the molecular mechanisms of miRNA-related diseases. Most of the existing computational methods mainly focus on predicting whether a miR…
This paper deals with sparse feature selection and grouping for classification and regression. The classification or regression problems under consideration consists in minimizing a convex empirical risk function subject to an constraint, a pairwise constraint, or a pairwise constraint. …
New system constructs cell-type taxonomy across multiple samples.
Chance-constrained ActInf allows for small violations of constraints to drive goal-directed behavior.
Analyzes learning dynamics of RNNs under locality constraints.
Synaptic strength can be seen as probability to propagate impulse, and according to synaptic plasticity, function could exist from propagation activity to synaptic strength. If the function satisfies constraints such as continuity and monotonicity, neural network under external stimulus will always go to fixed point, a…
The method integrates survival constraints into NMF for identifying survival-associated gene clusters.
Multi-omic data provides multiple views of the same patients. Integrative analysis of multi-omic data is crucial to elucidate the molecular underpinning of disease etiology. However, multi-omic data has the "big p, small N" problem (the number of features is large, but the number of samples is small), it is challenging…
Optimal algorithm selects biological models without prior info.
Genomic models learn DNA sequences to predict functions.
Researchers develop flexible kernels for biological sequences with guaranteed reliability.
New learning algorithm mimics biological neural networks.
Bayesian Cox model identifies biomarkers from multi-omics data.
Generalized Lotka-Volterra (GLV) models extending the (70 year old) logistic equation to stochastic systems consisting of a multitude of competing auto-catalytic components lead to power distribution laws of the (100 year old) Pareto-Zipf type. In particular, when applied to economic systems, GLV leads to power laws in…
SENA-discrepancy-VAE interprets latent causal factors in biological pathways.
The functions of proteins and RNAs are determined by a myriad of interactions between their constituent residues, but most quantitative models of how molecular phenotype depends on genotype must approximate this by simple additive effects. While recent models have relaxed this constraint to also account for pairwise in…
Parameterizing the approximate posterior of a generative model with neural networks has become a common theme in recent machine learning research. While providing appealing flexibility, this approach makes it difficult to impose or assess structural constraints such as conditional independence. We propose a framework f…
We introduce a tensor-based clustering method to extract sparse, low-dimensional structure from high-dimensional, multi-indexed datasets. This framework is designed to enable detection of clusters of data in the presence of structural requirements which we encode as algebraic constraints in a linear program. Our cluste…
In many domains, scientists build complex simulators of natural phenomena that encode their hypotheses about the underlying processes. These simulators can be deterministic or stochastic, fast or slow, constrained or unconstrained, and so on. Optimizing the simulators with respect to a set of parameter values is common…
Biological networks are a very convenient modelling and visualisation tool to discover knowledge from modern high-throughput genomics and postgenomics data sets. Indeed, biological entities are not isolated, but are components of complex multi-level systems. We go one step further and advocate for the consideration of …
Scalable GPLVM reduces complexity in scRNA-seq data, accounting for technical and biological confounders.
DeepSIBA predicts biological effects of chemical structures using graph neural networks.
This technical note considers the problems of blind sparse learning and inference of electrogram (EGM) signals under atrial fibrillation (AF) conditions. First of all we introduce a mathematical model for the observed signals that takes into account the multiple foci typically appearing inside the heart during AF. Then…
New methods improve uncertainty quantification in dynamic biological systems.
Proposes a novel network-based neighborhood regression for biological systems.
We study the problem of instance segmentation in biological images with crowded and compact cells. We formulate this task as an integer program where variables correspond to cells and constraints enforce that cells do not overlap. To solve this integer program, we propose a column generation formulation where the prici…
We analyzed the performance of a biologically inspired algorithm called the Corrected Projections Algorithm (CPA) when a sparseness constraint is required to unambiguously reconstruct an observed signal using atoms from an overcomplete dictionary. By changing the geometry of the estimation problem, CPA gives an analyti…
AR algorithm simplifies backpropagation with improved scalability and biological plausibility.
Physics-informed methods infer spatial dynamics from static snapshots, but limits exist.
We solve a lifecycle model in which the consumer's chronological age does not move in lockstep with calendar time. Instead, biological age increases at a stochastic non-linear rate in time like a broken clock that might occasionally move backwards. In other words, biological age could actually decline. Our paper is ins…
ASCEND discovers causal relationships in multi-omics data by leveraging known hierarchical structure.
New method discovers causal relationships in confounded systems.
Algorithm optimizes biological sequences using bootstrapped training with a score-conditioned generator.
FL-Sailer enables federated learning for scATAC-seq data, reducing dimensionality and noise.
Backpropagation is the workhorse of deep learning, however, several other biologically-motivated learning rules have been introduced, such as random feedback alignment and difference target propagation. None of these methods have produced a competitive performance against backpropagation. In this paper, we show that bi…
Fault-tolerant neural networks inspired by biological error correction codes.
Probabilistic graphical models (PGMs) have become a popular tool for computational analysis of biological data in a variety of domains. But, what exactly are they and how do they work? How can we use PGMs to discover patterns that are biologically relevant? And to what extent can PGMs help us formulate new hypotheses t…
Recent technological advancements in data acquisition tools allowed life scientists to acquire multimodal data from different biological application domains. Broadly categorized in three types (i.e., sequences, images, and signals), these data are huge in amount and complex in nature. Mining such an enormous amount of …
New learning rules for wide neural networks without backpropagation.
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 introduce bio-inspired artificial neural networks consisting of neurons that are additionally characterized by spatial positions. To simulate properties of biological systems we add the costs penalizing long connections and the proximity of neurons in a two-dimensional space. Our experiments show that in the case wh…
Graph auto-encoder predicts unobserved node features from biological networks and omics data.