Heuristics and metaheuristics for biological network alignment: A review

Current and future directions in network biology

Summary

Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology, focusing on molecular/cellular networks but also on other biological network types such as biomedical knowledge graphs, patient similarity networks, brain networks, and social/contact networks relevant to disease spread. In more detail, we highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on future directions of network biology. Additionally, we discuss scientific communities, educational initiatives, and the importance of fostering diversity within the field. This article establishes a roadmap for an immediate and long-term vision for network biology.

Availability and implementation

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Severe acute respiratory syndrome Coronavirus-2 GenoAnalyzer and mutagenic anomaly detector using FCMFI and NSCE

In order to deepen our understanding of the virus and help guide the creation of efficient therapies, this study uses artificial intelligence tools to thoroughly explore the genetic sequences of the SARS-CoV-2 virus. The process starts by using the Fuzzy Closure Miner for Frequent Itemsets (FCMFI) on a large corpus of SARS-CoV-2 genomic sequences to reveal hidden patterns, including nucleotides base sequences, repeating motifs, and corresponding interchanges. Then, using the Nucleotide Sequence Comprehension Engine (NSCE) technique, we were able to precisely define the genomic areas for mutation analysis. Structured and unstructured proteins are both strongly impacted by virus mutations, with spike proteins that are linked to the severity of COVID-19 pneumonia being particularly affected. Notably, the Mutagenic Anomaly Detector shows a 65 % efficiency boost in computing genome mutation rates compared to conventional point mutation analysis, while GenoAnalyzer offers a remarkable 93.33 % improvement over existing approaches in recognizing common genomic sequence patterns. These results highlight the potential of FCMFI to reveal complex genomic patterns and significant insights in COVID-19 genetic sequences when combined with mutation analysis. The Mutagenic Anomaly Detector and GenoAnalyzer show promise for revealing hidden genomic patterns and precisely estimating the SARS-CoV-2 mutation rate.

Encoding edge type information in graphlets

Graph embedding approaches have been attracting increasing attention in recent years mainly due to their universal applicability. They convert network data into a vector space in which the graph structural information and properties are maximumly preserved. Most existing approaches, however, ignore the rich information about interactions between nodes, i.e., edge attribute or edge type. Moreover, the learned embeddings suffer from a lack of explainability, and cannot be used to study the effects of typed structures in edge-attributed networks. In this paper, we introduce a framework to embed edge type information in graphlets and generate a Typed-Edge Graphlets Degree Vector (TyE-GDV). Additionally, we extend two combinatorial approaches, i.e., the colored graphlets and heterogeneous graphlets approaches to edge-attributed networks. Through applying the proposed method to a case study of chronic pain patients, we find that not only the network structure of a patient could indicate his/her perceived pain grade, but also certain social ties, such as those with friends, colleagues, and healthcare professionals, are more crucial in understanding the impact of chronic pain. Further, we demonstrate that in a node classification task, the edge-type encoded graphlets approaches outperform the traditional graphlet degree vector approach by a significant margin, and that TyE-GDV could achieve a competitive performance of the combinatorial approaches while being far more efficient in space requirements.