Robust Gene Prioritization via Fast-mRMR Feature Selection in high-dimensional omics data

• URL: http://arxiv.org/abs/2511.21211v1
• Date: Wed, 26 Nov 2025 09:41:20 GMT
• Title: Robust Gene Prioritization via Fast-mRMR Feature Selection in high-dimensional omics data
• Authors: Rubén Fernández-Farelo, Jorge Paz-Ruza, Bertha Guijarro-Berdiñas, Amparo Alonso-Betanzos, Alex A. Freitas,
• Abstract summary: Existing methods struggle with the high dimensionality and incomplete labelling of biomedical data.<n>This work proposes a more robust and efficient pipeline that leverages Fast-mRMR feature selection.<n>Experiments on Dietary Restriction datasets show significant improvements over existing methods.
• Score: 3.5395196881025446
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Gene prioritization (identifying genes potentially associated with a biological process) is increasingly tackled with Artificial Intelligence. However, existing methods struggle with the high dimensionality and incomplete labelling of biomedical data. This work proposes a more robust and efficient pipeline that leverages Fast-mRMR feature selection to retain only relevant, non-redundant features for classifiers. This enables us to build simpler and more effective models, as well as to combine different biological feature sets. Experiments on Dietary Restriction datasets show significant improvements over existing methods, proving that feature selection can be critical for reliable gene prioritization.

Related papers

• Biological Pathway Guided Gene Selection Through Collaborative Reinforcement Learning [25.2831953927341]
  We propose a novel framework that integrates statistical selection with biological pathway knowledge using multi-agent reinforcement learning (MARL)<n>Our framework incorporates pathway knowledge through Graph Neural Network-based state representations, a reward mechanism combining prediction performance with gene centrality and pathway coverage, and collaborative learning strategies using shared memory and a centralized critic component.
  arXiv  Detail & Related papers  (2025-05-30T03:01:07Z)
• GRAPE: Heterogeneous Graph Representation Learning for Genetic Perturbation with Coding and Non-Coding Biotype [51.58774936662233]
  Building gene regulatory networks (GRN) is essential to understand and predict the effects of genetic perturbations.<n>In this work, we leverage pre-trained large language model and DNA sequence model to extract features from gene descriptions and DNA sequence data.<n>We introduce gene biotype information for the first time in genetic perturbation, simulating the distinct roles of genes with different biotypes in regulating cellular processes.
  arXiv  Detail & Related papers  (2025-05-06T03:35:24Z)
• BOLIMES: Boruta and LIME optiMized fEature Selection for Gene Expression Classification [0.0937465283958018]
  BOLIMES is a novel feature selection algorithm designed to enhance gene expression classification.<n>It combines exhaustive feature selection with interpretability-driven refinement, offering a powerful solution for high-dimensional gene expression analysis.
  arXiv  Detail & Related papers  (2025-02-18T17:33:41Z)
• Knowledge-Guided Biomarker Identification for Label-Free Single-Cell RNA-Seq Data: A Reinforcement Learning Perspective [30.927272289309048]
  We present an iterative gene panel selection strategy that harnesses ensemble knowledge from existing gene selection algorithms to establish preliminary boundaries or prior knowledge.<n>We incorporate reinforcement learning through a reward function shaped by expert behavior, enabling dynamic refinement and targeted selection of gene panels.<n>Our results underscore the potential of this approach to advance single-cell genomics data analysis.
  arXiv  Detail & Related papers  (2025-01-02T07:57:41Z)
• Weighted Diversified Sampling for Efficient Data-Driven Single-Cell Gene-Gene Interaction Discovery [56.622854875204645]
  We present an innovative approach utilizing data-driven computational tools, leveraging an advanced Transformer model, to unearth gene-gene interactions. A novel weighted diversified sampling algorithm computes the diversity score of each data sample in just two passes of the dataset.
  arXiv  Detail & Related papers  (2024-10-21T03:35:23Z)
• Multi-Modal and Multi-Attribute Generation of Single Cells with CFGen [76.02070962797794]
  This work introduces CellFlow for Generation (CFGen), a flow-based conditional generative model that preserves the inherent discreteness of single-cell data.<n>CFGen generates whole-genome multi-modal single-cell data reliably, improving the recovery of crucial biological data characteristics.
  arXiv  Detail & Related papers  (2024-07-16T14:05:03Z)
• Single-Cell Deep Clustering Method Assisted by Exogenous Gene Information: A Novel Approach to Identifying Cell Types [50.55583697209676]
  We develop an attention-enhanced graph autoencoder, which is designed to efficiently capture the topological features between cells. During the clustering process, we integrated both sets of information and reconstructed the features of both cells and genes to generate a discriminative representation. This research offers enhanced insights into the characteristics and distribution of cells, thereby laying the groundwork for early diagnosis and treatment of diseases.
  arXiv  Detail & Related papers  (2023-11-28T09:14:55Z)
• Genetic InfoMax: Exploring Mutual Information Maximization in High-Dimensional Imaging Genetics Studies [50.11449968854487]
  Genome-wide association studies (GWAS) are used to identify relationships between genetic variations and specific traits. Representation learning for imaging genetics is largely under-explored due to the unique challenges posed by GWAS. We introduce a trans-modal learning framework Genetic InfoMax (GIM) to address the specific challenges of GWAS.
  arXiv  Detail & Related papers  (2023-09-26T03:59:21Z)
• Genetic heterogeneity analysis using genetic algorithm and network science [2.6166087473624318]
  Genome-wide association studies (GWAS) can identify disease susceptible genetic variables. Genetic variables intertwined with genetic effects often exhibit lower effect-size. This paper introduces a novel feature selection mechanism for GWAS, named Feature Co-selection Network (FCSNet)
  arXiv  Detail & Related papers  (2023-08-12T01:28:26Z)
• Unsupervised ensemble-based phenotyping helps enhance the discoverability of genes related to heart morphology [57.25098075813054]
  We propose a new framework for gene discovery entitled Un Phenotype Ensembles. It builds a redundant yet highly expressive representation by pooling a set of phenotypes learned in an unsupervised manner. These phenotypes are then analyzed via (GWAS), retaining only highly confident and stable associations.
  arXiv  Detail & Related papers  (2023-01-07T18:36:44Z)
• Neural networks for Anatomical Therapeutic Chemical (ATC) [83.73971067918333]
  We propose combining multiple multi-label classifiers trained on distinct sets of features, including sets extracted from a Bidirectional Long Short-Term Memory Network (BiLSTM) Experiments demonstrate the power of this approach, which is shown to outperform the best methods reported in the literature.
  arXiv  Detail & Related papers  (2021-01-22T19:49:47Z)
• A New Gene Selection Algorithm using Fuzzy-Rough Set Theory for Tumor Classification [0.0]
  We present a new technique for gene selection using a discernibility matrix of fuzzy-rough sets. The proposed technique takes into account the similarity of those instances that have the same and different class labels to improve the gene selection results. Experimental results demonstrate that this technique provides better efficiency compared to the state-of-the-art approaches.
  arXiv  Detail & Related papers  (2020-03-26T13:43:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.