Shrinkage estimation of gene interaction networks in single-cell RNA sequencing data
BMC Bioinformatics
by Duong H. T. Vo and Thomas Thorne
3d ago
Gene interaction networks are graphs in which nodes represent genes and edges represent functional interactions between them. These interactions can be at multiple levels, for instance, gene regulation, protei ..read more
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KiNext: a portable and scalable workflow for the identification and classification of protein kinases
BMC Bioinformatics
by Elisabeth Hellec, Flavia Nunes, Charlotte Corporeau and Alexandre Cormier
3d ago
Protein kinases are a diverse superfamily of proteins common to organisms across the tree of life that are typically involved in signal transduction, allowing organisms to sense and respond to biotic or abioti ..read more
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DNA-protein quasi-mapping for rapid differential gene expression analysis in non-model organisms
BMC Bioinformatics
by Kyle Christian L. Santiago and Anish M. S. Shrestha
3d ago
Conventional differential gene expression analysis pipelines for non-model organisms require computationally expensive transcriptome assembly. We recently proposed an alternative strategy of directly aligning ..read more
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CMAGN: circRNA–miRNA association prediction based on graph attention auto-encoder and network consistency projection
BMC Bioinformatics
by Anhui Yin, Lei Chen, Bo Zhou and Yu-Dong Cai
3d ago
Background: As noncoding RNAs, circular RNAs (circRNAs) can act as microRNA (miRNA) sponges due to their abundant miRNA binding sites, allowing them to regulate gene expression and influence disease developmen ..read more
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Data-driven discovery of chemotactic migration of bacteria via coordinate-invariant machine learning
BMC Bioinformatics
by Yorgos M. Psarellis, Seungjoon Lee, Tapomoy Bhattacharjee, Sujit S. Datta, Juan M. Bello-Rivas and Ioannis G. Kevrekidis
3d ago
E. coli chemotactic motion in the presence of a chemonutrient field can be studied using wet laboratory experiments or macroscale-level partial differential equations (PDEs) (among others). Bridging experimental ..read more
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Translation regulation by RNA stem-loops can reduce gene expression noise
BMC Bioinformatics
by Candan Çelik, Pavol Bokes and Abhyudai Singh
1w ago
Stochastic modelling plays a crucial role in comprehending the dynamics of intracellular events in various biochemical systems, including gene-expression models. Cell-to-cell variability arises from the stocha ..read more
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Mulea: An R package for enrichment analysis using multiple ontologies and empirical false discovery rate
BMC Bioinformatics
by Cezary Turek, Márton Ölbei, Tamás Stirling, Gergely Fekete, Ervin Tasnádi, Leila Gul, Balázs Bohár, Balázs Papp, Wiktor Jurkowski and Eszter Ari
1w ago
Traditional gene set enrichment analyses are typically limited to a few ontologies and do not account for the interdependence of gene sets or terms, resulting in overcorrected p-values. To address these challenge ..read more
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Biomedical relation extraction method based on ensemble learning and attention mechanism
BMC Bioinformatics
by Yaxun Jia, Haoyang Wang, Zhu Yuan, Lian Zhu and Zuo-lin Xiang
1w ago
Relation extraction (RE) plays a crucial role in biomedical research as it is essential for uncovering complex semantic relationships between entities in textual data. Given the significance of RE in biomedica ..read more
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Predicting stroke occurrences: a stacked machine learning approach with feature selection and data preprocessing
BMC Bioinformatics
by Pritam Chakraborty, Anjan Bandyopadhyay, Preeti Padma Sahu, Aniket Burman, Saurav Mallik, Najah Alsubaie, Mohamed Abbas, Mohammed S. Alqahtani and Ben Othman Soufiene
2w ago
Stroke prediction remains a critical area of research in healthcare, aiming to enhance early intervention and patient care strategies. This study investigates the efficacy of machine learning techniques, parti ..read more
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RepDilPCR: a tool for automated analysis of qPCR assays by the dilution-replicate method
BMC Bioinformatics
by Deyan Yordanov Yosifov, Michaela Reichenzeller, Stephan Stilgenbauer and Daniel Mertens
2w ago
The dilution-replicate experimental design for qPCR assays is especially efficient. It is based on multiple linear regression of multiple 3-point standard curves that are derived from the experimental samples ..read more
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