28 papers · ranked by Valyu relevance
Pengpeng Luo, Ming Yang, Chong Peng, Qianqian Wang + 1 more
Multi-view subspace clustering has progressed significantly by using deep neural networks to handle nonlinear data representations. A recent advancement, the Multi-view Self-Expressive Subspace Clustering (MSESC) network, achieves markedly higher computational efficiency by substituting the traditional self-expression…
Ryosuke Kasai, Hideki Otsuka, Xianlin Song
Emission tomography, including single-photon emission computed tomography (SPECT), requires image reconstruction from noisy and incomplete projection data. The maximum-likelihood expectation maximization (MLEM) algorithm is widely used due to its statistical foundation and non-negativity preservation, but it is highly…
Zhiyi Zhang, Mingyi Yang, Cheng Xie, Zhigang Xu + 2 more
To address the nonlinear dynamics and strong multivariate coupling inherent in complex industrial data, while overcoming the high computational costs and deployment challenges of deep learning, this paper proposes a Channel-Independent Anchor Graph-Regularized Broad Learning System (CI-GBLS). First, a Channel…
Mohammad Forouhesh
Recovering a latent potential from observed flow on a directed graph (a discrete Poisson problem with Dirichlet boundaries) is ill-posed, and the standard fix backfires: ridge regularization shrinks toward a gauge-meaningless origin, collapsing and reversing the recovered ordering ($+0.81\to-0.42$ rank correlation…
Antonio Briola, Marwin Schmidt, Fabio Caccioli, Carlos Ros Perez + 3 more
High-dimensional data often exhibit dependencies among variables that violate the isotropic-noise assumption under which principal component analysis (PCA) is optimal. For cases where the noise is not independent and identically distributed across features (i.e., the covariance is not spherical) we introduce Graph…
Saghar Bagheri, Gene Cheung, Tim Eadie, Antonio Ortega
A crucial assumption in graph signal processing (GSP) is the existence of an underlying graph that captures the pairwise similarities between nodes, allowing filters to be designed based on this graph for tasks such as denoising. For spatial-temporal data in which node-to-node similarities evolve over time, a static…
Chuansen Peng, Xiaojing Shen
Inferring time-varying graph structures from high-dimensional nodal observations is a fundamental problem arising in neuroscience, finance, climatology, and beyond. Two intrinsic challenges govern this problem: maintaining the \emph{temporal coherence} of the latent graph across successive observation windows, and…
Chenyue Zhang, Shangyuan Liu, Hoi-To Wai, Anthony Man-Cho So
Learning graph topology of complex networks is challenging due to limited data availability and imprecise data models. Different from prior works that focus on structural priors with explicit control on macroscopic properties such as sparsity, this paper proposes a novel functional prior approach for graph topology…
Jingtao Hu, Yi Zhang, Chengzhang Zhu, Changsheng Hou + 1 more
Attributed graphs have recently emerged as a powerful tool for representing diverse data in numerous real-world sensors. Among various applications, unsupervised graph anomaly detection (UGAD) aims to identify abnormal data that significantly deviate from the majority of normal nodes without label annotations. Hence…
Yuyao Wang, Yu-Hung Cheng, Debarghya Mukherjee, Huimin Cheng
Graphon models provide a flexible nonparametric framework for estimating latent connectivity probabilities in networks, enabling a range of downstream applications such as link prediction and data augmentation. However, accurate graphon estimation typically requires a large graph, whereas in practice, one often only…
Omar Melikechi, David B. Dunson, Noureddine Melikechi, Jeffrey W. Miller
Many datasets include a small set of variables, such as biomarkers or clinical outcomes, whose relationships to the broader system are of primary scientific interest. Estimating the full network of inter-variable relationships in such settings often obscures local structures around these targets, limiting…
Maria Boulougouri, Mohan Vamsi Nallapareddy, Pierre Vandergheynst
Gene interactions form complex networks underlying disease susceptibility and therapeutic response. While bulk transcriptomic datasets offer rich resources for studying these interactions, applying Graph Neural Networks (GNNs) to such data remains limited by a lack of methodological guidance, especially for…
Chimdi Walter Ndubuisi
Discrete Ricci curvature is an appealing descriptor for single-cell trajectory graphs, but its practical value depends on task validity, graph-topology controls, and whether the biological target is a local transition region or a broader fate decision. We present a controlled empirical study of when curvature features…
Yuhua Fan, Ilkka Launonen, Mikko J Sillanpää, Patrik Waldmann
High-dimensional genomic datasets contain complex patterns shaped by substantial biological noise, which pose major challenges for predictive modeling in genetics and breeding. Residual neural networks (ResNets) provide a powerful framework for capturing nonlinear genomic effects, but often overfit in settings where…
Marco Stock, Florin Ratajczak, Paul Bertin, Eva Hoermanseder + 6 more
Accurate reconstruction of gene regulatory networks (GRNs) from single-cell transcriptomic data remains a major methodological challenge. Recent machine learning approaches, particularly graph neural networks and graph autoencoders, have reported improved performance, yet these gains do not consistently translate to…
Sumathi Subbarayan, G. Hannah Grace
Introduction Clustering high-dimensional and noisy data remains challenging for conventional expectation-maximization (EM) methods as overlapping clusters, sparse features, and outliers can lead to covariance degeneracy and unstable parameter estimates. This research aims to improve clustering performance in…
Authors not listed
Pharmacophores are widely used to describe protein-ligand interactions, and the Grids of Pharmacophore Interaction Fields (GRAIL) method extends this concept by representing binding pockets as interpretable sets of interaction type-specific pharmacophoric maps. In this work, we propose a hybrid framework for binding…
Andrei Buciulea, Bishwadeep Das, Elvin Isufi, Antonio G. Marques
Graph learning aims to infer a network structure directly from observed data, enabling the analysis of complex dependencies in irregular domains. Traditional methods focus on scalar signals at each node, ignoring dependencies along additional dimensions such as time, configurations of the observation device, or…
Authors not listed
Computational methods for predictive modeling have been increasingly utilized in the early stages of drug discovery to supplement high-throughput screening. The advent of highly efficient and complex machine learning architectures necessitates new methods of collating the plethora of topological, geometrical, and…
Rongmei Tang, JianPing Liu, Pengfei Zhang, Xujun Liang
Gene regulatory networks are formed by complex regulatory relationships between transcription factors and their target genes. A systematic understanding of these regulatory relationships is crucial for deciphering the molecular mechanisms that underlie cell state transitions under physiological and pathological…
Authors not listed
We present a new method for fingerprint- ing atomic configurations relevant to ML-IAM training and application, utilizing the ChIMES descriptor. These fingerprints enable rigor- ous analysis of statistical distinguishability be- tween configurations. Sample applications in- clude assessing diversity within ML-IAP…
Authors not listed
We present graphRC, a graph-based method for rapid transition state (TS) mode analysis that provides chemical insight along normal mode displacements and reaction coordinate trajectories by translating Cartesian displacements into meaningful internal coordinate changes. Internal coordinates are constructed using…
Teddy Lazebnik, Alex Liberzon
Symbolic Regression (SR) is a powerful technique for discovering analytical mathematical expressions that describe observed numerical data. Traditionally, SR models work on data in tabular form, imposing a purely functional mapping without considering the underlying spatio-temporal dependencies or the governing…
Authors not listed
Machine learning models are increasingly applied to heterogeneous materials datasets spanning different synthesis routes, measurement protocols, and structural classes. Although multi-task and representation-learning approaches are commonly used to improve predictive performance, the latent representations learned by…
Daniel M. Gonçalves, André Patrício, Rafael S. Costa, Rui Henriques
The growing availability and complexity of omics data have driven the development of specialized algorithms for modeling molecular systems. Although graph-based learning methods effectively represent biological interactions, they often neglect the statistical information embedded in node and edge annotations. To…
Authors not listed
The Polytope Formalism provides a rigorous and unifying mathematical framework for representing all possible molecular configurations and their interrelationships. Extending its application from stereoisomerism to molecular constitution reveals that both arise from a common structural foundation linking discrete and…
Setareh Rahimi, Stephen Bonner, Avid Afzal, Marta Milo + 2 more
Predicting gene essentiality across cellular contexts is a central challenge in computational biology, with implications for identifying cancer vulnerabilities. Graph neural networks (GNNs) integrate molecular interaction networks with gene-level features, but it remains unclear whether their performance gains arise…
Authors not listed
Early prediction of drug-induced organ toxicity remains a major bottleneck in drug discovery and clinical pharmacotherapy. Most data-driven toxicity models behave as endpoint predictors: they output a label but provide limited transparency about why a compound is risky or which evidence channel dominated the decision.…