Babak Najafpour
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Babak Najafpour has a total of 8 co-authors. Here are the top co-authors by number of shared publications:
- Alexia Fytsili
Department of Biochemistry and Biotechnology, University of Thessaly, Biopolis, 41500 Larissa, Greece
1 shared publication - Alice Printzi
Biology Department, University of Crete, 70013 Heraklion, Greece
1 shared publication - Andreas Tsipourlianos
Laboratory of Genetics, Comparative and Evolutionary Biology, Department of Biochemistry and Biotechnology, University of Thessaly, 41500 Larissa, Greece
1 shared publication - Deborah Mary Power
Centro de Ciências do Mar (CCMAR), Universidade do Algarve, Faro, Portugal
1 shared publication - George Koumoundouros
Biology Department, University of Crete, 70013 Heraklion, Greece
1 shared publication - Katerina A. Moutou
Laboratory of Genetics, Comparative and Evolutionary Biology, Department of Biochemistry and Biotechnology, University of Thessaly, 41500 Larissa, Greece
1 shared publication
Latest Publications
- Open Access
Gene Expression-Based Classification of European Seabass Larval Batches According to Saddleback Syndrome Incidence Using Machine Learning
Skeletal deformities remain a major challenge in marine fish hatcheries, affecting larval quality, animal welfare, production efficiency, and market value. In European seabass (Dicentrarchus labrax), saddleback syndrome (SBS) is a relevant skeletal abnormality that develops during larval ontogeny and has been associated with defects of the primordial marginal finfold around the flexion stage. This study investigated whether gene expression markers, combined with machine learning, could provide a stage-specific molecular approach for assessing SBS-associated larval batch quality. Larval populations from commercial hatcheries were classified as GOOD or POOR according to SBS incidence at mid-metamorphosis. Gene expression was analyzed at first feeding, flexion, post-flexion, and mid-metamorphosis, targeting genes involved in osteogenesis, myogenesis, metabolism, oxidative phosphorylation, and stress response. Stage-specific random forest models were used to classify gene expression profiles derived from larval populations with contrasting SBS incidence and to identify candidate informative genes. The models showed cross-validated area under the receiver operating characteristic curve (ROC AUC) values ranging from 0.83 to 0.962, with the highest performance at flexion. Reduced models based on the three most informative genes retained comparable internal cross-validation performance. Key candidate genes were mainly related to mitochondrial energy production, iron metabolism, stress response, muscle development, and extracellular matrix formation. These findings suggest that gene expression profiling combined with machine learning may support stage-aware discrimination of larval populations with contrasting SBS incidence, although validation in larger independent datasets is required before hatchery application.
Open AccessGene Expression-Based Classification of European Seabass Larval Batches According to Saddleback Syndrome Incidence Using Machine Learning
Skeletal deformities remain a major challenge in marine fish hatcheries, affecting larval quality, animal welfare, production efficiency, and market value. In European seabass (Dicentrarchus labrax), saddleback syndrome (SBS) is a relevant skeletal abnormality that develops during larval ontogeny and has been associated with defects of the primordial marginal finfold around the flexion stage. This study investigated whether gene expression markers, combined with machine learning, could provide a stage-specific molecular approach for assessing SBS-associated larval batch quality. Larval populations from commercial hatcheries were classified as GOOD or POOR according to SBS incidence at mid-metamorphosis. Gene expression was analyzed at first feeding, flexion, post-flexion, and mid-metamorphosis, targeting genes involved in osteogenesis, myogenesis, metabolism, oxidative phosphorylation, and stress response. Stage-specific random forest models were used to classify gene expression profiles derived from larval populations with contrasting SBS incidence and to identify candidate informative genes. The models showed cross-validated area under the receiver operating characteristic curve (ROC AUC) values ranging from 0.83 to 0.962, with the highest performance at flexion. Reduced models based on the three most informative genes retained comparable internal cross-validation performance. Key candidate genes were mainly related to mitochondrial energy production, iron metabolism, stress response, muscle development, and extracellular matrix formation. These findings suggest that gene expression profiling combined with machine learning may support stage-aware discrimination of larval populations with contrasting SBS incidence, although validation in larger independent datasets is required before hatchery application.