麻豆淫院

Researchers at Ben-Gurion University of the Negev have developed a machine-learning algorithm that could enhance our understanding of human biology and disease. The new method, Weighted Graph Anomalous Node Detection (WGAND), takes inspiration from social network analysis and is designed to identify proteins with significant roles in various human tissues.

Proteins are essential molecules in our bodies, and they interact with each other in complex networks, known as protein-protein interaction (PPI) networks. Studying these networks helps scientists understand how proteins function and how they contribute to health and disease.

Prof. Esti Yeger-Lotem, Dr. Michael Fire, Dr. Jubran Juman, and Dr. Dima Kagan developed the algorithm to analyze these PPI networks to detect "anomalous" proteins鈥攖hose that stand out due to their unique pattern of weighted interactions. This implies that the amount of the protein and its protein interactors is greater in that particular network, allowing them to carry out more functions and drive more processes. This also indicates the great importance that these proteins have in a particular network, because the body will not waste energy on their production for no reason.

The research combined the expertise in protein networks of Prof. Yeger-Lotem with the network analysis expertise of Dr. Fire derived from his study of social networks. In cybersecurity-related analyses of social networks, identifying atypical patterns can uncover fraudulent transactions or suspicious user behavior.

The innovative insight of the new GigaScience is that the same algorithms that uncover anomalies in social networks can be applied to the networks of proteins inside individual cells. By focusing on these anomalies, the algorithm can identify proteins that play crucial roles in specific tissues, such as the brain, heart, and liver.

WGAND successfully identified proteins associated with tissue-specific diseases, such as those involved in brain disorders and heart conditions. The algorithm also pinpointed proteins involved in critical biological processes, like neuron signaling in the brain and muscle contraction in the heart. Moreover, WGAND outperformed other existing methods in terms of accuracy and precision.

"This innovative algorithm has the potential to pinpoint which proteins are important in specific contexts, helping scientists to develop more targeted and effective treatments for various conditions," says Prof. Yeger-Lotem.

"It's exciting to see how bringing together expertise from bioinformatics and cybersecurity can lead to breakthroughs in understanding human biology. By applying network analysis and machine learning, we have developed a tool that helps uncover key proteins in different tissues鈥攑aving the way for new insights into human health and disease," says Dr. Michael Fire.

In accordance with Dr. Fire's principles as the head of the Fire AI Lab, the is open source, allowing researchers worldwide to utilize and build upon it. The Yeger-Lotem lab also maintains web tools enabling easy access to researchers with no computational background.