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: Not applicable.

Preoperative Prediction of Metastatic Lymph Nodes Posterior to the Right Recurrent Laryngeal Nerve in cN0 Papillary Thyroid Carcinoma.

Background: The advantages of the dissecting the metastatic lymph nodes posterior to the right recurrent laryngeal nerve (LN-prRLN) remain a great deal of controversies in papillary thyroid carcinoma (PTC) patients without clinical evidence. The purpose of our retrospective research was to investigate the predictive factors of the LN-prRLN in cN0 PTC patients. Methods and Materials: Altogether 251 consecutive cN0 PTC participants accepted unilateral or bilateral thyroidectomy accompanied with LN-prRLN dissection between June 2020 and May 2023 were included in the research. Then, univariate and multivariate logical regression analysis were conducted to analyze the relationship between the LN-prRLN and these predictive factors, and a predictive model was also developed. Surgical complications of LN-prRLN dissection were also presented. Results: The rate of LN-prRLN was 17.9% (45/251) in cN0 PTC patients after the analysis of postoperative histology. The age <55 years, multifocality, microcalcification, and BRAFV600E mutation were identified to be predictive factors of LN-prRLN in cN0 PTC patients. The risk score for LN-prRLN was calculated: risk score = 1.192 × (if age <55 years) + 0.808 × (if multifocality) + 1.196 × (if microcalcification in nodule) + 0.918 × (if BRAFV600E mutation in nodule). The rates of the transient hypoparathyroidism and hoarseness were 1.2% (3/251) and 2.0% (5/251), respectively. Conclusion: The age <55 years, multifocality, microcalcification, and BRAFV600E mutation are independent predictors of the LN-prRLN in cN0 PTC patients. An effective predictive model was established for predicting the LN-prRLN in cN0 PTC patients, with the aim to better guide the surgical treatment of PTC. A thorough inspection of the lateral compartment is recommended in PTC patients with risk factors. The multicenter research with long-term follow-up should be carried out to ascertain the optimal surgical approach for patients with PTC.