Network Analysis in Translational Research

A great majority of the morbidity and mortality worldwide can still be attributed to cardiovascular diseases, such as ischemic (coronary) heart disease, angina pectoris, and myocardial and cerebral infarction. Atherosclerosis, narrowing of the arteries because of arterial cholesterol deposition in macrophage foam cells, is the driving force behind the cardiovascular disease pathology. Water-soluble protein/lipid complexes called lipoproteins mediate the transport of cholesterol and other lipoid substances through the blood compartment. Relatively high levels of cholesterol associated with apolipoprotein B–containing low-density lipoprotein (LDL) particles predispose human subjects to the development of atherosclerosis and, thereby, increase the risk for cardiovascular disease.1,2 Apolipoprotein B–containing lipoproteins are, therefore, generally regarded as being proatherogenic factors. Cholesterol ester–rich high-density lipoprotein (HDL) particles use apolipoprotein A1 (apoA1) as their primary protein component. In sharp contrast to LDL, HDL is considered a potent anti-atherogenic agent. This notion is based on the fact that, in the general population, a strong inverse correlation exists between plasma levels of HDL cholesterol and the risk of cardiovascular disease.1 Of note, this inverse association seems to be independent of the level of cholesterol associated with proatherogenic LDL particles. As such, increasing plasma levels of HDL cholesterol has long been regarded a promising alternative therapy to supplement classical statin–based LDL cholesterol–lowering strategies that are able to reduce cardiovascular disease by only ≈30%.3 However, over the last decade, the enthusiasm for HDL as an interesting therapeutic target has been challenged by the HDL hypothesis critics because genetic association studies have excluded HDL cholesterol levels as determinants for cardiovascular disease risk.2,4 Furthermore, several therapeutic HDL-targeting approaches have proven insufficient to secure benefit for cardiovascular disease patients. Niacin is the most effective drug available in the clinic to raise plasma HDL cholesterol levels. Despite the fact that niacin is …

Protein function prediction plays an important role in the field of biology. With the wide application of deep learning in the field of bioinformatics, more and more natural language processing (NLP) technologies are applied to the downstream tasks in the field of bioinformatics, and it has also shown excellent performance in protein function prediction. Protein-protein interaction (PPI) networks and other biological attributes contain rich information critical for annotating protein functions. However, existing deep learning networks still suffer from overfitting and noise issues, resulting in low accuracy in protein function prediction. Consequently, developing efficient models for protein function prediction remains a popular and challenging topic in the application of NLP in bioinformatics. In this study, we propose a novel protein function prediction model based on attention mechanisms, termed IMAGO. This model employs the Transformer pre-training process, integrating multi-head attention mechanisms and regularization techniques, and optimizes the loss function to effectively reduce overfitting and noise issues during training. It generates more robust embeddings, ultimately improving the accuracy of protein function prediction. Experimental results on human and mouse datasets indicate that our model surpasses other protein function prediction models across multiple metrics. Thus, this efficient, stable, and accurate deep learning model holds significant promise for protein function prediction.

Applied Network Analysis is a reference book on the methodology of network analysis -- the study of the structure of relations between people, groups or formal organizations. Illustrations from real research show the problems that arise in network analysis -- and how to resolve or avoid them. Primarily written by Burt and Minor, the book has the cohesion of a text while still using work from other leading network analysts.

BackgroundDiabetic retinopathy (DR) is a common and serious microvascular complication of diabetes. Taohong Siwu decoction (THSWD), a famous traditional Chinese medicine (TCM) prescription, has been proved to have a good clinical effect on DR, whereas its molecular mechanism remains unclear. Our study aimed to uncover the core targets and signaling pathways of THSWD against DR.MethodsFirst, the active ingredients of THSWD were searched from Traditional Chinese Medicine Systems Pharmacology (TCMSP) Database. Second, the targets of active ingredients were identified from ChemMapper and PharmMapper databases. Third, DR associated targets were searched from DisGeNET, DrugBank and Therapeutic Target Database (TTD). Subsequently, the common targets of active ingredients and DR were found and analyzed in STRING database. DAVID database and ClueGo plug-in software were used to carry out the gene ontology (GO) and KEGG enrichment analysis. The core signaling pathway network of “herb-ingredient-target” was constructed by the Cytoscape software. Finally, the key genes of THSWD against DR were validated by quantitative real-time PCR (qRT-PCR).ResultsA total of 2340 targets of 61 active ingredients in THSWD were obtained. Simultaneously, a total of 263 DR-associated targets were also obtained. Then, 67 common targets were found by overlapping them, and 23 core targets were identified from protein-protein interaction (PPI) network. Response to hypoxia was found as the top GO term of biological process, and HIF-1 signaling pathway was found as the top KEGG pathway. Among the key genes in HIF-1 pathway, the mRNA expression levels of VEGFA, SERPINE1 and NOS2 were significantly down-regulated by THSWD (P < 0.05), and NOS3 and HMOX1 were significantly up-regulated (P < 0.05).ConclusionTHSWD had a protective effect on DR via regulating HIF-1 signaling pathway and other important pathways. This study might provide a theoretical basis for the application of THSWD and the development of new drugs for the treatment of DR.

In recent years, significant effort has been given to predicting protein functions from protein interaction data generated from high throughput techniques. However, predicting protein functions correctly and reliably still remains a challenge. Recently, many computational methods have been proposed for predicting protein functions. Among these methods, clustering based methods are the most promising. The existing methods, however, mainly focus on protein relationship modeling and the prediction algorithms that statically predict functions from the clusters that are related to the unannotated proteins. In fact, the clustering itself is a dynamic process and the function prediction should take this dynamic feature of clustering into consideration. Unfortunately, this dynamic feature of clustering is ignored in the existing prediction methods. In this paper, we propose an innovative progressive clustering based prediction method to trace the functions of relevant annotated proteins across all clusters that are generated through the progressive clustering of proteins. A set of prediction criteria is proposed to predict functions of unannotated proteins from all relevant clusters and traced functions. The method was evaluated on real protein interaction datasets and the results demonstrated the effectiveness of the proposed method compared with representative existing methods.

After reviewing the underlying framework required for computational function prediction in the previous chapter, we discuss two advanced sequence-based function prediction methods developed in our group, namely the Protein Function Prediction (PFP) method and the Extended Similarity Group (ESG) method. PFP extends the traditional homology search by incorporating functional associations between pairs of Gene Ontology terms based on the frequencies of co-occurrences in annotation of the same proteins in the database. PFP also considers very weakly similar sequences to the query, thereby increases its sensitivity and ability to predict low resolution functional terms. On the other hand, ESG recursively searches the sequence similarity space around the query to find consensus annotations in the neighborhood. The last part of the chapter discusses the network structure of gene functional space built by connecting proteins with functional similarity. Function annotation was enriched by predictions by PFP. Similarity to structures of protein-protein interaction networks and metabolic pathway networks is discussed.

Information on protein-protein interactions provides valuable insight into the underlying mechanism of protein functions. However, the accuracy of protein-protein interaction data obtained by high-throughput experimental methods is low, and thus requires a rigorous assessment of their reliability. This paper proposes a computational method for predicting the unknown function of a protein interacting with a protein with known function, and presents the experimental results of applying the method to the protein-protein interactions in yeast and human. This method can also be used to assess the reliability of the protein-protein interaction data.KeywordsAssociation RuleInteraction DataYeast ProteinUncharacterized ProteinProtein Interaction DataThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

COST B15: modelling in drug development

world population (Bieber et al, 2023). Among these the inflammatory diseases like rheumatoid arthritis, type 1 diabetes mellitus, systemic lupus erythematosus, multiple sclerosis, Crohn's disease, ulcerative colitis, psoriasis, and myasthenia gravis are prominent ones. The ever-increasing number of senior citizens (age >60 years) across the globe and the advent of newer and better diagnostic tools for immune-mediated diseases has produced an increase in the incidence of inflammatory diseases. While the general population grew from one billion in the year 1800 to eight billion at present, and it is estimated that by 2030 one in six people in world will be aged sixty or older (Bolkan et al., 2023;Pereira et al., 2023). In addition to this, studies suggest that there is an increased risk of developing immune-mediated diseases to the individuals who got SARS-CoV-2 infections (Kim et al, 2024). Despite the great advances in the treatment of these medical conditions in recent past, many patients suffer with serious conditions of the disease for a long time. This situation demands constant research, to identify new drugs, with more specific targets and fewer side effects to treat inflammatory diseases (Wang et al, 2015).In Brazil, despite the guarantee of free access to medicines through the Unified Health System for all citizens as a constitutional right, immune-mediated diseases represent a significant challenge. This is due to its complexity and the lack of adequate resources for early diagnosis and effective treatment. For example, one of the main obstacles to the accurate diagnosis of rheumatological diseases in Brazil is the shortage of specialized professionals. Recent data indicates that 53.3% of the all the rheumatologists in the country live in the capitals of state in the southeast region; 16.8% live in the very populous northeast region, while only 4.1% of medical professionals reside in the north region. This data represents the heterogeneity in the territorial distribution of professionals in Brazil, a developing country. Furthermore, the lack of adequate medical training for those who work in primary care can lead to diagnostic errors or delays in referral to specialists, which ends up worsening the health condition, with irreversible damage to patients (Scheffer et al, 2023;BRAZIL, 2021). The same kind of situation is prevalent in almost all the developing or underdeveloped countries where most of the advance medical facilities are concentrated to affluent and populous cities and rural areas are devoid of such facilities. Despite the existence of specific clinical protocols for treatment, there is a great difficulty in the diagnosis for immune disease due to complex definition of diagnostic criteria, leading to severe impact on the quality of life of affected individuals (Dal Lin et al, 2023). In this scenario, the discovery of new molecules capable of treating diseases mediated by the immune system remains fundamental.Identification of new natural products, synthetic molecules, drug repurposing, in silico molecular modelling studies associated with chemical synthesis represent important strategies for the development of new drugs (Wainwright et al, 2022;Spitschak et al, 2022). The global demand for new drugs and the associated scientific challenges were motivations for this special edition, with the aim to bring together scientific research works that addressed the development of new immunomodulatory drugs. As a result, we have very interesting articles, coming from different research aspects, which lead us to believe that they will be very useful in discovering new treatments for inflammatory diseases in near future.The study "A real-world disproportionality analysis of mepolizumab based on the FDA adverse event reporting system" investigated adverse events associated with mepolizumab in the treatment of severe asthma with an eosinophilic phenotype. Reports of expected adverse events were identified, but also unexpected ones, such as coughing, malaise and chest discomfort, recommending continued attention to safety in long-term use and in paediatrics. In conclusion, the field of anti-inflammatory drug development for immune mediated diseases is evolving rapidly and breath-taking research, supported by innovative techniques is giving transformative breakthroughs and cutting-edge solutions.However, despite the current research diversity with this edition and optimism for the future, inflammatory diseases are known for their complexity and will remain an intriguing challenge for researchers for future studies too.

BackgroundIn the current climate of high-throughput computational biology, the inference of a protein's function from related measurements, such as protein-protein interaction relations, has become a canonical task. Most existing technologies pursue this task as a classification problem, on a term-by-term basis, for each term in a database, such as the Gene Ontology (GO) database, a popular rigorous vocabulary for biological functions. However, ontology structures are essentially hierarchies, with certain top to bottom annotation rules which protein function predictions should in principle follow. Currently, the most common approach to imposing these hierarchical constraints on network-based classifiers is through the use of transitive closure to predictions.ResultsWe propose a probabilistic framework to integrate information in relational data, in the form of a protein-protein interaction network, and a hierarchically structured database of terms, in the form of the GO database, for the purpose of protein function prediction. At the heart of our framework is a factorization of local neighborhood information in the protein-protein interaction network across successive ancestral terms in the GO hierarchy. We introduce a classifier within this framework, with computationally efficient implementation, that produces GO-term predictions that naturally obey a hierarchical 'true-path' consistency from root to leaves, without the need for further post-processing.ConclusionA cross-validation study, using data from the yeast Saccharomyces cerevisiae, shows our method offers substantial improvements over both standard 'guilt-by-association' (i.e., Nearest-Neighbor) and more refined Markov random field methods, whether in their original form or when post-processed to artificially impose 'true-path' consistency. Further analysis of the results indicates that these improvements are associated with increased predictive capabilities (i.e., increased positive predictive value), and that this increase is consistent uniformly with GO-term depth. Additional in silico validation on a collection of new annotations recently added to GO confirms the advantages suggested by the cross-validation study. Taken as a whole, our results show that a hierarchical approach to network-based protein function prediction, that exploits the ontological structure of protein annotation databases in a principled manner, can offer substantial advantages over the successive application of 'flat' network-based methods.

Network science has become an invaluable tool for neuroimaging analysis and has advanced our understanding of the brain's complex structural and functional topology. Network science, an interdisciplinary offshoot of graph theory, conceives of the brain as a system of nodes, representing brain regions or voxels, and edges, representing the structural or functional connection between these regions or voxels. Such a model is considered appealing in neuroscience as it describes a system with various interacting regions that produces complex behaviors. This conception of the brain has revealed that the brain exhibits “small-world” properties, namely that brain networks are systems with regional specialization and efficient global information transfer. More importantly, studies in brain networks have furthered our understanding of diseases and disorders affecting the brain. Assessing group differences in representative samples of brain networks has become increasingly important, yet appropriate methodological developments have remained relatively sparse. Additionally, there is increased interest in understanding dynamic changes, or how a network changes over time. Currently, most network analysis tools are designed to analyze a static representation, thus changes that occur over time are often overlooked or ignored. As the field continues to grow, it is becoming increasingly important to develop methods for understanding the complexity of these data. This research topic features selected contributions that explores group analysis and dynamics in brain network studies including methodological and practical applications employing various imaging modalities. This snapshot of the field represents the growing interest to expand and validate new methods for use in the field of brain network neuroscience. “Defining nodes in complex brain networks” is a review article discussing the methodological choices for defining nodes, particularly how the choice of atlas-based or voxel-based methods affects the computed network (Stanley et al., 2013). “Routing in the brain” is a commentary on network routing focusing on how current methods and models may not be adequate for brain networks. Further exploration of the packet switching network model may be more realistic and appropriate for the brain (Graham, 2014). “A multimodal approach for determining brain networks by jointly modeling functional and structural connectivity” is a methods article introducing a Bayesian model for estimating functional networks, from functional magnetic resonance imaging (fMRI) data, leveraging complementary structural diffusion tensor imaging (DTI) data (Xue et al., 2015). “A permutation testing framework to compare groups of brain networks” is a methods article describing a permutation testing framework to statistically compare groups of functional brain networks while incorporating topological features inherent in each individual network (Simpson et al., 2013). “Assessing dynamics, spatial scale, and uncertainty in task-related brain network analyses” is a methods article describing a statistical approach for computing uncertainty in static and dynamic functional networks and aggregating network measures in task-related electrocorticography (ECoG) data (Stephen et al., 2014). “Hierarchical vector auto-regressive models and their applications to multi-subject effective connectivity” is a methods article introducing a generalization of the vector auto-regressive (VAR) model for comparing effective connectivity between experimental conditions while accounting for between-subject heterogeneity (Gorrostieta et al., 2013). “Statistical network analysis for functional MRI: summary networks and group comparisons” is a review article discussing the construction of summary networks and how to test for topological differences in groups of networks when these groups also exhibit significant differences in density (Ginestet et al., 2014). “Detecting functional connectivity change points for single-subject fMRI data” is a methods article that extends Dynamic Connectivity Regression (DCR), a data-driven technique which detects change points in functional connectivity between brain regions to determine state changes over the course of an experimental task, by introducing a novel algorithm aimed at increasing the estimation of networks for individual subject data (Cribben et al., 2013). “The Laplacian spectrum of neural networks” is an original research article examining the use of the Laplacian spectrum in anatomic networks of the macaque, cat and Caenorhabditis elegans. This method describes network structure at a systems level, assessing systemic infrastructural properties of the entire network as opposed to properties of specific nodes or connections (De Lange et al., 2014). “Quantifying network properties in multi-electrode recordings: spatiotemporal characterization and inter-trial variation of evoked gamma oscillations in mouse somatosensory cortex in vitro” is an original research article on network dynamics in multi-electrode array data focusing on changes in the functional connectivity of evoked gamma oscillations in cortical circuits (Carmeli et al., 2013).

Innovative Partnerships for Drug Discovery against Neglected Diseases

The high-throughput technologies have led to vast amounts of protein-protein interaction (PPI) data, and a number of approaches based on PPI networks have been proposed for protein function prediction. However, these approaches do not work well if there is not enough PPI information. To address this issue, we propose a novel collective classification based approach that combines protein sequence information and PPI information to improve the prediction performance. We first reconstruct a PPI network by adding a number of computed edges based on protein sequence similarity, and then apply a collective classification algorithm to predict protein function based on the new PPI network. Experiments over two real datasets demonstrate that our approach outperforms most of existing approaches across a series of label situations, especially in sparsely-labeled networks where the existing approaches fail because of PPI information inadequacy. Experimental results also validate the robustness of our approach to the number of labeled proteins in PPI networks.

Completely sequenced genomes and other types of genomics data provide us with new information to predict protein function. While classical, homology-based function prediction provides information about a proteins’ molecular function (what does the protein do at a molecular scale?), the analysis of the sequence in the context of its genome or in other types of genomics data provides information about its functional context (what are the proteins’ interaction partners, and in which biological process does it play a role?) Genomic context data are however inherently noisy. Only by combining different types of genomic(s) context data (vertical comparative genomics) or by combining the same type of genomics data from different species (horizontal comparative genomics) do they become sufficiently reliable to be used for protein function prediction. Homology-based function prediction and context-based function prediction provide complementary information about a protein’s function and can becombined to make predictions that are specific enough for experimental testing. Here we discuss the genomic coverage and reliability of combining genomics data for protein function prediction and survey predictions that have actually led to experimental confirmation. Using a number of examples we illustrate how combining the information from various types of genomics data can lead to specific protein function predictions. These include the prediction that the Ribonuclease L inhibitor (RLI) is involved in the maturation of ribosomal RNA.

The proper integration of multiple sources of data and the unbalance between annotated and unannotated proteins represent two of the main issues of the automated function prediction (AFP) problem. Most of supervised and semisupervised learning algorithms for AFP proposed in literature do not jointly consider these items, with a negative impact on both sensitivity and precision performances, due to the unbalance between annotated and unannotated proteins that characterize the majority of functional classes and to the specific and complementary information content embedded in each available source of data. We propose UNIPred (unbalance-aware network integration and prediction of protein functions), an algorithm that properly combines different biomolecular networks and predicts protein functions using parametric semisupervised neural models. The algorithm explicitly takes into account the unbalance between unannotated and annotated proteins both to construct the integrated network and to predict protein annotations for each functional class. Full-genome and ontology-wide experiments with three eukaryotic model organisms show that the proposed method compares favorably with state-of-the-art learning algorithms for AFP.