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Zebrafish and human genomes are highly homologous; however, despite this genomic similarity, adult zebrafish can achieve neuronal proliferation, regeneration and functional restoration within 6-8 weeks after spinal cord injury, whereas humans cannot. To analyze differentially expressed zebrafish genes between axon-regenerated neurons and axon-non-regenerated neurons after spinal cord injury, and to explore the key genes and pathways of axonal regeneration after spinal cord injury, microarray GSE56842 was analyzed using the online tool, GEO2R, in the Gene Expression Omnibus database. Gene ontology and protein-protein interaction networks were used to analyze the identified differentially expressed genes. Finally, we screened for genes and pathways that may play a role in spinal cord injury repair in zebrafish and mammals. A total of 636 differentially expressed genes were obtained, including 255 up-regulated and 381 down-regulated differentially expressed genes in axon-regenerated neurons. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment results were also obtained. A protein-protein interaction network contained 480 node genes and 1976 node connections. We also obtained the 10 hub genes with the highest correlation and the two modules with the highest score. The results showed that spectrin may promote axonal regeneration after spinal cord injury in zebrafish. Transforming growth factor beta signaling may inhibit repair after spinal cord injury in zebrafish. Focal adhesion or tight junctions may play an important role in the migration and proliferation of some cells, such as Schwann cells or neural progenitor cells, after spinal cord injury in zebrafish. Bioinformatic analysis identified key candidate genes and pathways in axonal regeneration after spinal cord injury in zebrafish, providing targets for treatment of spinal cord injury in mammals.

Constructing the structure of protein signaling networks by Bayesian network technology is a key issue in the field of bioinformatics. The primitive structure learning algorithms of the Bayesian network take no account of the causal relationships between variables, which is unfortunately important in the application of protein signaling networks. In addition, as a combinatorial optimization problem with a large searching space, the computational complexities of the structure learning algorithms are unsurprisingly high. Therefore, in this paper, the causal directions between any two variables are calculated first and stored in a graph matrix as one of the constraints of structure learning. A continuous optimization problem is constructed next by using the fitting losses of the corresponding structure equations as the target, and the directed acyclic prior is used as another constraint at the same time. Finally, a pruning procedure is developed to keep the result of the continuous optimization problem sparse. Experiments show that the proposed method improves the structure of the Bayesian network compared with the existing methods on both the artificial data and the real data, meanwhile, the computational burdens are also reduced significantly.

Background Lung adenocarcinoma shows distinct differences between males and females in incidence, prognosis, and treatment response, suggesting unique molecular mechanisms that remain underexplored. This study aims to identify sex-specific molecular signatures and therapeutic targets in lung adenocarcinoma using multi-omics approaches to inform personalized treatment strategies. Methods We conducted an integrative analysis of transcriptomic and proteomic data from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and The Cancer Genome Atlas (TCGA) datasets, comparing male and female lung adenocarcinoma profiles. Transcription factor activity was assessed using TIGER on gene expression data, while kinase activity was evaluated with PTM-SEA on proteomic data. These results were combined to build a kinase-transcription factor signaling network. Potential sex-specific drugs were identified using the PRISM drug screening database. Results The analysis revealed significant sex-based differences in transcription factor and kinase activity. Notably, NR3C1, AR, and AURKA exhibited sex-biased expression and activity. The constructed signaling network highlighted druggable pathways linked to cancer-related processes, with distinct profiles in males and females. PRISM screening identified glucocorticoid receptor agonists and aurora kinase inhibitors as promising sex-specific therapeutic candidates. Conclusions Our findings underscore the importance of considering sex differences in lung adenocarcinoma molecular profiles. The integration of transcriptomic and proteomic data reveals sex-specific pathways and potential therapies, paving the way for personalized treatment approaches tailored to male and female patients. Highlights Identified sex-specific molecular differences in lung adenocarcinoma using gene and protein data. Found women have stronger immune responses, linked to better survival outcomes. Discovered sixteen drugs with different effectiveness in men versus women, including aurora kinase inhibitors. Integrated multi-omics approach revealed new pathways for personalized, sex-specific treatments. Highlights the need to consider biological sex to improve cancer therapy outcomes. Plain English Summary Lung cancer, specifically lung adenocarcinoma, affects men and women differently in terms of how often it occurs, how it progresses, and how well treatments work. These differences likely come from a mix of genetics, hormones, and lifestyle factors, but the exact reasons are not fully understood. Our study used advanced tools to compare the molecular makeup of lung tumors in men and women, looking at both gene activity and protein function. We found important differences in how certain proteins and genes work together in men versus women, including differences that influence cancer growth and immune responses. For example, women showed stronger immune activity linked to better survival, while men and women responded differently to certain drugs. By identifying these differences, we discovered potential new treatments, such as drugs targeting specific proteins, that might work better for men or women. This research shows that considering sex differences is key to developing better, personalized treatments for lung cancer, which could improve outcomes for all patients.

Inferring causal protein signalling networks from human immune system cell data is a promising approach to unravel the underlying tissue signalling biology and dysfunction in diseased cells, which has attracted considerable attention within the bioinformatics field. Recently, Bayesian network (BN) techniques have gained significant popularity in inferring causal protein signalling networks from multiparameter single‐cell data. However, current BN methods may exhibit high computational complexity and ignore interactions among protein signalling molecules from different single cells. A novel BN method is presented for learning causal protein signalling networks based on parallel discrete artificial bee colony (PDABC), named PDABC. Specifically, PDABC is a score‐based BN method that utilises the parallel artificial bee colony to search for the global optimal causal protein signalling networks with the highest discrete K2 metric. The experimental results on several simulated datasets, as well as a previously published multi‐parameter fluorescence‐activated cell sorter dataset, indicate that PDABC surpasses the existing state‐of‐the‐art methods in terms of performance and computational efficiency.

A wealth of causal relationships exists in biological systems, both causal brain networks and causal protein signaling networks are very classical causal biological networks (CBNs). Learning CBNs from biological signal data reliably is a critical problem today. However, most of the existing methods are not excellent enough in terms of accuracy and time performance, and tend to fall into local optima because they do not take full advantage of global information. In this paper, we propose a parallel ant colony optimization algorithm to learn causal biological networks from biological signal data, called PACO. Specifically, PACO first maps the construction of CBNs to ants, then searches for CBNs in parallel by simulating multiple groups of ants foraging, and finally obtains the optimal CBN through pheromone fusion and CBNs fusion between different ant colonies. Extensive experimental results on simulation data sets as well as two real-world data sets, the fMRI signal data set and the Single-cell data set, show that PACO can accurately and efficiently learn CBNs from biological signal data.

When a posterior distribution has multiple modes, unconditional expectations, such as the posterior mean, may not offer informative summaries of the distribution. Motivated by this problem, we propose to decompose the sample space of a multimodal distribution into domains of attraction of local modes. Domain-based representations are defined to summarize the probability masses of and conditional expectations on domains of attraction, which are much more informative than the mean and other unconditional expectations. A computational method, the multi-domain sampler, is developed to construct domain-based representations for an arbitrary multimodal distribution. The multi-domain sampler is applied to structural learning of protein-signaling networks from high-throughput single-cell data, where a signaling network is modeled as a causal Bayesian network. Not only does our method provide a detailed landscape of the posterior distribution but also improves the accuracy and the predictive power of estimated networks. This article has supplementary material online.

In this paper, cell cycle in higher eukaryotes and their molecular networks signals both in G1/S and G2/M transitions are replicated in silico. Biochemical kinetics, converted into a set of differential equations, and system control theory are employed to design multi-nested digital layers to simulate protein-to-protein activation and inhibition for cell cycle dynamics in the presence of damaged genomes. Sequencing and controlling the digital process of four micro-scale species networks (p53/Mdm2/DNA damage, p21mRNA/cyclin-CDK complex, CDK/CDC25/weel/SKP2/APC/CKI and apoptosis target genes system) not only allows the comprehension of the mechanisms of these molecule interactions but paves the way for unraveling the participants and their by-products, until now quite unclear, which have the task of carrying out (or not) cell death. Whatever the running simulations (e.g., different species signals, mutant cells and different DNA damage levels), the results of the proposed cell digital multi-layers give reason to believe in the existence of a universal apoptotic mechanism. As a consequence, we identified and selected cell check points, sizers, timers and specific target genes dynamic both for influencing mitotic process and avoiding cancer proliferation as much as for leading the cancer cell(s) to collapse into a steady stable apoptosis phase.

After spinal cord injury, dysregulated miRNAs appear and can participate in inflammatory responses, as well as the inhibition of apoptosis and axon regeneration through multiple pathways. However, the functions of miRNAs in spinal cord ischemia-reperfusion injury progression remain unclear. miRCURY LNATM Arrays were used to analyze miRNA expression profiles of rats after 90 minutes of ischemia followed by reperfusion for 24 and 48 hours. Furthermore, subsequent construction of aberrantly expressed miRNA regulatory patterns involved cell survival, proliferation, and apoptosis. Remarkably, the mitogen-activated protein kinase (MAPK) signaling pathway was the most significantly enriched pathway among 24- and 48-hour groups. Bioinformatics analysis and quantitative reverse transcription polymerase chain reaction confirmed the persistent overexpression of miR-22-3p in both groups. These results suggest that the aberrant miRNA regulatory network is possibly regulated MAPK signaling and continuously affects the physiological and biochemical status of cells, thus participating in the regulation of spinal cord ischemia-reperfusion injury. As such, miR-22-3p may play sustained regulatory roles in spinal cord ischemia-reperfusion injury. All experimental procedures were approved by the Animal Ethics Committee of Jilin University, China [approval No. 2020 (Research) 01].

This chapter contains sections titled: Introduction to Text Mining A li B aba as a Tool for Mining Biological Facts from Literature Components and Usage of A li B aba A li B aba 's Approach to Text Mining Related Biomedical Text Mining Tools Conclusions and Future Perspectives Acknowledgments References

Molecular knowledge of biological processes is a cornerstone in omics data analysis. Applied to single‐cell data, such analyses provide mechanistic insights into individual cells and their interactions. However, knowledge of intercellular communication is scarce, scattered across resources, and not linked to intracellular processes. To address this gap, we combined over 100 resources covering interactions and roles of proteins in inter‐ and intracellular signaling, as well as transcriptional and post‐transcriptional regulation. We added protein complex information and annotations on function, localization, and role in diseases for each protein. The resource is available for human, and via homology translation for mouse and rat. The data are accessible via OmniPath ’s web service ( https://omnipathdb.org/ ), a Cytoscape plug‐in, and packages in R/Bioconductor and Python, providing access options for computational and experimental scientists. We created workflows with tutorials to facilitate the analysis of cell–cell interactions and affected downstream intracellular signaling processes. OmniPath provides a single access point to knowledge spanning intra‐ and intercellular processes for data analysis, as we demonstrate in applications studying SARS‐CoV‐2 infection and ulcerative colitis. SYNOPSIS Over 100 resources are integrated into OmniPath , a comprehensive knowledge base of intra‐ and inter‐cellular signaling. Workflows are provided and illustrated in case studies analyzing omics data in SARS‐CoV‐2 infection and ulcerative colitis. OmniPath includes 4,000,000 annotations for over 20,000 proteins. A new framework defining transmitter and receiver roles generalizes the concepts of ligand and receptor . Integrated analysis of intra‐ and intercellular signaling can be performed to study how cells affect each other in healthy and diseased conditions. Software tools and workflows in R and Python facilitate the analysis of bulk and single‐cell omics data using tools such as CellPhoneDB , NicheNet and CARNIVAL . Graphical Abstract Over 100 resources are integrated into OmniPath , a comprehensive knowledge base of intra‐ and inter‐cellular signaling. Workflows are provided and illustrated in case studies analyzing omics data in SARS‐CoV‐2 infection and ulcerative colitis.