This paper analyzes the application of surrogate models to improve the efficiency of Gene Regulatory Network (GRN) inference from time-series data. A Radial Basis Function (RBF) surrogate model was integrated with the Penalized mAximum LikeLihood and pArticle Swarms (PALLAS) using a Mixed Fish School Search (MFSS) algorithm to reduce the computational cost associated with evaluating the penalized log-likelihood (PLL) fitness function. Experimental results on the p53-MDM2 negative-feedback loop GRN dataset demonstrate that the surrogate-assisted approach significantly reduced fitness function calls by 50% and 89% while maintaining the quality of the PLL metric, with this showing the potential of surrogate models to accelerate GRN inference.
This study aims to analyze the genomic characteristics of a plant growth-promoting bacterium, focusing on genome assembly and annotation, as well as phylogenetic and metabolic analyses. The primary objective is to identify and validate genes related to potassium and phosphate solubilization, as well as other primary and secondary metabolic pathways. The research involves the selection of a strain, cultivation, DNA extraction, sequencing, genome assembly and annotation, followed by phylogenetic and comparative analyses. The analyzed Bacillus nitratireducensLABIM48 strain shows high genomic conservation, indicating a common origin and conserved evolution. Phylogenetic analyses confirmed that the strain belongs to the same mentioned species, with the potential to produce bioactive compounds of interest identified in silico. The ability to solubilize phosphate and potassium was validated through in vitro tests.
This paper investigates convolutional neural networks (CNN) for predicting cancer types by integrating protein-protein interaction (PPI) networks with omics data. While [Chuang et al. 2021] employed a single 3-layer CNN, we explore ten different architectures, including a custom model developed by our team (CNN2Layers), following their methodology. By evaluating the strengths and weaknesses of these models, we aim to identify the most effective CNN for accurately predicting various human cancers. Our proposed model achieved state-of-the-art performance using fewer layers. Interestingly, the simpler architectures achieved superior results, indicating their effectiveness in handling the specific characteristics of the dataset.
This study develops and evaluates pan-cancer (PC) models for cohort-specific (CS) predictions using neural networks (NNs). We adopt a dual approach, including a method inspired by few-shot learning, aiming at improving the models’ ability to distinguish between normal and tumorous tissues across diverse cohorts. The first approach trains a NN with comprehensive PC datasets containing 16 cancer types, comparing it against CS models on a target cohort, while the second analyzes whether PC models could generalize to smaller and unseen cohorts by training on 15 cohorts and evaluating on the excluded cohort. Our experiments show that PC models generally outperform CS models, even with limited sample sizes and class imbalances. Moreover, the few-shot approach successfully generalizes to other cancer types, highlighting its potential to advance personalized cancer diagnosis and treatment.
