Identification of feasible pathway information for c-di-GMP binding proteins in cellulose production

• URL: http://arxiv.org/abs/2204.12526v1
• Date: Tue, 26 Apr 2022 18:22:13 GMT
• Title: Identification of feasible pathway information for c-di-GMP binding proteins in cellulose production
• Abstract summary: We use a machine learning approach to identify the significant pathways for c-di-GMP signaling proteins. The dataset involves gene counts from 12 pathways and 5 essential c-di-GMP binding domains for 1024 bacterial genomes.
• Score: 0.3149883354098941
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we utilize a machine learning approach to identify the significant pathways for c-di-GMP signaling proteins. The dataset involves gene counts from 12 pathways and 5 essential c-di-GMP binding domains for 1024 bacterial genomes. Two novel approaches, Least absolute shrinkage and selection operator (Lasso) and Random forests, have been applied for analyzing and modeling the dataset. Both approaches show that bacterial chemotaxis is the most essential pathway for c-di-GMP encoding domains. Though popular for feature selection, the strong regularization of Lasso method fails to associate any pathway to MshE domain. Results from the analysis may help to understand and emphasize the supporting pathways involved in bacterial cellulose production. These findings demonstrate the need for a chassis to restrict the behavior or functionality by deactivating the selective pathways in cellulose production.

Related papers

• Machine Learning for analysis of Multiple Sclerosis cross-tissue bulk and single-cell transcriptomics data [30.40681235085877]
  We developed an end-to-end machine learning pipeline to analyze transcriptomic data from peripheral blood mononuclear cells and cerebrospinal fluid.<n>We identified immune activation, non-canonical immune checkpoints, ribosomal and translational programs, ubiquitin-proteasome regulation, lipid trafficking, and Epstein-Barr virus-related pathways.
  arXiv  Detail & Related papers  (2026-03-05T16:09:12Z)
• Biological Pathway Informed Models with Graph Attention Networks (GATs) [25.189604321546952]
  Biological pathways map gene-gene interactions that govern all human processes.<n>Most ML models treat genes as unstructured tokens, discarding known pathway structure.<n>We propose a Graph Attention Network (GAT) that models pathways at the gene level.
  arXiv  Detail & Related papers  (2025-08-30T15:14:30Z)
• CFM-GP: Unified Conditional Flow Matching to Learn Gene Perturbation Across Cell Types [7.197454335008732]
  We present CFM-GP, a method for cell type-agnostic gene perturbation prediction.<n>CFM-GP learns a continuous, time-dependent transformation between unperturbed and perturbed gene expression distributions.<n>We evaluate on five datasets: SARS-CoV-2 infection, IFN-beta stimulated PBMCs, treated with glioblastoma Panobinostat, lupus under IFN-beta stimulation, and Statefate progenitor fate mapping.
  arXiv  Detail & Related papers  (2025-08-09T00:00:17Z)
• PathSegDiff: Pathology Segmentation using Diffusion model representations [63.20694440934692]
  We propose PathSegDiff, a novel approach for histopathology image segmentation that leverages Latent Diffusion Models (LDMs) as pre-trained featured extractors. Our method utilizes a pathology-specific LDM, guided by a self-supervised encoder, to extract rich semantic information from H&E stained histopathology images. Our experiments demonstrate significant improvements over traditional methods on the BCSS and GlaS datasets.
  arXiv  Detail & Related papers  (2025-04-09T14:58:21Z)
• scGSDR: Harnessing Gene Semantics for Single-Cell Pharmacological Profiling [5.831554646284266]
  scGSDR is a model that integrates two computational pipelines grounded in the knowledge of cellular states and gene signaling pathways. scGSDR enhances predictive performance by incorporating gene semantics and employs an interpretability module. The model's application has extended from single-drug predictions to scenarios involving drug combinations.
  arXiv  Detail & Related papers  (2025-02-02T15:43:20Z)
• Cell as Point: One-Stage Framework for Efficient Cell Tracking [54.19259129722988]
  This paper proposes the novel end-to-end CAP framework to achieve efficient and stable cell tracking in one stage. CAP abandons detection or segmentation stages and simplifies the process by exploiting the correlation among the trajectories of cell points to track cells jointly. Cap demonstrates strong cell tracking performance while also being 10 to 55 times more efficient than existing methods.
  arXiv  Detail & Related papers  (2024-11-22T10:16:35Z)
• A Bioinformatic Approach Validated Utilizing Machine Learning Algorithms to Identify Relevant Biomarkers and Crucial Pathways in Gallbladder Cancer [2.3087284629747766]
  Gallbladder cancer (GBC) is the most frequent cause of disease among biliary tract neoplasms. Few recent studies have explored the roles of biomarkers in GBC. We used machine learning (ML) and bioinformatics techniques to identify biomarkers in GBC.
  arXiv  Detail & Related papers  (2024-10-18T12:51:19Z)
• Towards Multi-modality Fusion and Prototype-based Feature Refinement for Clinically Significant Prostate Cancer Classification in Transrectal Ultrasound [4.662744612095781]
  We propose a novel learning framework for clinically significant prostate cancer (csPCa) classification using multi-modality TRUS. The proposed framework employs two separate 3D ResNet-50 to extract distinctive features from B-mode and shear wave elastography (SWE) The performance of the framework is assessed on a large-scale dataset consisting of 512 TRUS videos with biopsy-proved prostate cancer.
  arXiv  Detail & Related papers  (2024-06-20T07:45:01Z)
• MMIL: A novel algorithm for disease associated cell type discovery [58.044870442206914]
  Single-cell datasets often lack individual cell labels, making it challenging to identify cells associated with disease. We introduce Mixture Modeling for Multiple Learning Instance (MMIL), an expectation method that enables the training and calibration of cell-level classifiers.
  arXiv  Detail & Related papers  (2024-06-12T15:22:56Z)
• ProLLM: Protein Chain-of-Thoughts Enhanced LLM for Protein-Protein Interaction Prediction [54.132290875513405]
  The prediction of protein-protein interactions (PPIs) is crucial for understanding biological functions and diseases. Previous machine learning approaches to PPI prediction mainly focus on direct physical interactions. We propose a novel framework ProLLM that employs an LLM tailored for PPI for the first time.
  arXiv  Detail & Related papers  (2024-03-30T05:32:42Z)
• 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)
• Functional Integrative Bayesian Analysis of High-dimensional Multiplatform Genomic Data [0.8029049649310213]
  We propose a framework called Functional Integrative Bayesian Analysis of High-dimensional Multiplatform Genomic Data (fiBAG) fiBAG allows simultaneous identification of upstream functional evidence of proteogenomic biomarkers. We demonstrate the profitability of fiBAG via a pan-cancer analysis of 14 cancer types.
  arXiv  Detail & Related papers  (2022-12-29T03:31:45Z)
• Designing Biological Sequences via Meta-Reinforcement Learning and Bayesian Optimization [68.28697120944116]
  We train an autoregressive generative model via Meta-Reinforcement Learning to propose promising sequences for selection. We pose this problem as that of finding an optimal policy over a distribution of MDPs induced by sampling subsets of the data. Our in-silico experiments show that meta-learning over such ensembles provides robustness against reward misspecification and achieves competitive results.
  arXiv  Detail & Related papers  (2022-09-13T18:37:27Z)
• Lung Cancer Lesion Detection in Histopathology Images Using Graph-Based Sparse PCA Network [93.22587316229954]
  We propose a graph-based sparse principal component analysis (GS-PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E) We evaluate the performance of the proposed algorithm on H&E slides obtained from an SVM K-rasG12D lung cancer mouse model using precision/recall rates, F-score, Tanimoto coefficient, and area under the curve (AUC) of the receiver operator characteristic (ROC)
  arXiv  Detail & Related papers  (2021-10-27T19:28:36Z)
• Primary Tumor and Inter-Organ Augmentations for Supervised Lymph Node Colon Adenocarcinoma Metastasis Detection [8.69535649683089]
  scarcity of labeled data is a major bottleneck for developing deep learning-based models for histopathology applications. This work explores alternatives on how to augment the training data for colon carcinoma metastasis detection when there is limited or no representation of the target domain.
  arXiv  Detail & Related papers  (2021-09-17T17:31:25Z)
• A general kernel boosting framework integrating pathways for predictive modeling based on genomic data [5.763710641111974]
  We propose a general framework, Pathway-based Kernel Boosting (PKB), which incorporates clinical information and prior knowledge about pathways for prediction of binary, continuous and survival outcomes. Our prediction algorithm incorporates pathway knowledge by constructing kernel function spaces from the pathways and use them as base learners in the boosting procedure.
  arXiv  Detail & Related papers  (2020-08-26T05:54:23Z)

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.