ExGenet, Integrating Design of Experiments and Response Surface Methodology for Cancer Gene Detection in Gene Regulatory Networks.

Objective: Network analysis techniques often require tuning hyperparameters for optimal performance. For instance, the independent cascade model necessitates determining the probability of diffusion. Despite its importance, a consensus on effective parameter adjustment remains elusive.

BuB: a builder-booster model for link prediction on knowledge graphs.

Link prediction (LP) has many applications in various fields. Much research has been carried out on the LP field, and one of the most critical problems in LP models is handling one-to-many and many-to-many relationships. To the best of our knowledge, there is no research on discriminative fine-tuning (DFT).

A Social Network Analysis Approach to Evaluate the Relationship Between the Mobility Network Metrics and the COVID-19 Outbreak.

The emergence of the new coronavirus in late 2019 further highlighted the human need for solutions to explore various aspects of deadly pandemics. Providing these solutions will enable humans to be more prepared for dealing with possible future pandemics. In addition, it helps governments implement strategies to tackle and control infectious diseases similar to COVID-19 faster than ever before.

DGRanker: Cancer Driver Gene Detection in Human Transcriptional Regulatory Network.

Background: Cancer is a group of diseases that have received much attention in biological research because of its high mortality rate and the lack of accurate identification of its root causes. In such studies, researchers usually try to identify cancer driver genes (CDGs) that start cancer in a cell. The majority of the methods that have ever been proposed for the identification of CDGs are based on gene expression data and the concept of mutation in genomic data.

GenHITS: A network science approach to driver gene detection in human regulatory network using gene's influence evaluation.

Cancer is among the diseases causing death, in which, cells uncontrollably grow and reproduce beyond the cell regulatory mechanism. In this disease, some genes are initiators of abnormalities and then transmit them to other genes through protein interactions. Accordingly, these genes are known as cancer driver genes (CDGs).

KatzDriver: A network based method to cancer causal genes discovery in gene regulatory network.

One of the important problems in oncology is finding the genes that perturb the cell functionality and cause cancer. These genes, namely cancer driver genes (CDGs), when mutated, lead to the activation of the abnormal proteins. This abnormality is passed on to other genes by protein-protein interactions, which can cause cells to uncontrollably multiply and become cancerous.

A new application of community detection for identifying the real specialty of physicians.

Background: There is an increasing trend in using network science methods and algorithms, including community detection methods, in different areas of healthcare. These areas include protein networks, drug prescriptions, healthcare fraud detection, and drug abuse. Counterfeit drugs, off-label marketing issues, and finding the healthcare community structures in a network of hospitals, are examples of using community detection in healthcare.

Cancer driver gene discovery in transcriptional regulatory networks using influence maximization approach.

Cancer driver genes (CDGs) are the genes whose mutations cause tumor growth. Several computational methods have been previously developed for finding CDGs. Most of these methods are sequence-based, that is, they rely on finding key mutations in genomic data to predict CDGs.