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A recent study conducted at Guangxi University in Nanning, China, has developed a new quantum algorithm called BG-VQE that accurately simulates chemical molecule properties. The algorithm reduces the complexity of theoretical simulation for drug molecule synthesis, making it easier to find precise solutions. The researchers used bridge-inspired quantum circuits and a gradient filter module to design the algorithm, which demonstrated its effectiveness and superiority in calculating ground state energies for various molecules. The research was supported by the National Natural Science Foundation of China and won the championship in the Quantum Biochemical Engineering track of the 2nd CCF Origin Pilot Cup Quantum Computing Challenge. [Extracted from the article]

A new report from Northeastern University in Shenyang, China discusses the importance of selecting critical features from microarray data as biomarkers for disease diagnosis and drug development. The report proposes a hybrid feature-selection algorithm that combines the Max Relevance and Min Redundancy (mRMR) method with differential evolution. This algorithm improves the quantization functions of mRMR to accommodate the continuous nature of microarray data attributes and utilizes an enhanced differential evolution algorithm to further filter the features. The research concludes that this hybrid algorithm improves the performance and efficiency of feature selection for microarray data analysis. [Extracted from the article]

A study conducted by researchers at Sun Yat-sen University in Guangdong, China, has developed a novel classification model to predict the recurrence of early-stage nasopharyngeal carcinoma after radiotherapy. The model combines unsupervised diagnostic feature learning with Adaboost ensemble learning, resulting in an accuracy of 80.33%, sensitivity of 79.55%, specificity of 80.43%, and GMean of 79.99%. The researchers believe that this model can be a valuable tool for medical professionals in predicting the likelihood of recurrence in the cervical lymph nodes following radiotherapy for early-stage nasopharyngeal carcinoma. [Extracted from the article]

A report from the University of Technology in Johor, Malaysia discusses the shift from standard healthcare to personalized medicine, which is based on genetics and other biological information. The researchers highlight the use of machine learning and data mining in classifying patient cohorts and subtyping diabetes. They found that diabetes can be differentiated into subtypes based on risk complications, genetics, clinical features, and treatment selection. The study emphasizes the importance of understanding the specific objectives and methods of diabetes subtyping using clustering algorithms to improve diabetes management. This research has been peer-reviewed and published in the Artificial Intelligence Review journal. [Extracted from the article]

A new report from Zhengzhou University in China discusses the need for large-scale constrained multi-objective optimization in personalized medicine. The researchers propose a new benchmark for testing algorithms in this field, taking into account realistic features such as mixed linkages between variables and varying numbers of constraint functions. They also introduce a bidirectional sampling strategy to improve algorithm performance in large-scale search spaces with constraints. The proposed algorithm is shown to be effective in solving personalized drug target recognition problems with over 2000 decision variables. [Extracted from the article]

A report from Southwest Jiaotong University in Chengdu, China, discusses a novel drug distribution system for epidemic situations. The study integrates an improved spread model and a multi-period vehicle routing optimization model to address the challenges of virus transmission and fluctuating drug demand. The researchers propose an epsilon-global optimization method and a hybrid tabu search algorithm to solve the problem. The study includes extensive test experiments and a case study in Chongqing city, China, demonstrating the effectiveness of the proposed methods. The research provides management implications for drug distribution optimization during sudden epidemic situations. [Extracted from the article]