ABSTRACT Purpose Breast cancer is a significant threat to women's health. Precise prognosis prediction for breast cancer can help doctors implement more rational treatment strategies. Artificial intelligence can assist doctors in decision‐making and enhance prediction accuracy. Experimental Design In this paper, a deep learning model ECMHA‐PP (Energy Constrained Multi‐Head Self‐Attention based Prognosis Prediction) is proposed to predict the prognosis of breast cancer. ECMHA‐PP utilizes patients' clinical data and extracts features through a cross‐position mix and a channel mix multi‐layer perceptron. Then, it incorporates an energy‐constrained multi‐head self‐attention layer to improve feature extraction capability. The source code of ECMHA‐PP has been hosted on GitHub and is available at https://github.com/xiaoliu166370/ECMHA‐PP . Results To evaluate our proposed method, prognostic prediction experiments were performed on the METABRIC dataset, yielding outstanding results with an average accuracy of 93.0% and an average area under the curve of 0.974. To further validate the model's performance, we conducted tests on another independent dataset, BRCA, achieving an accuracy of 87.6%. Conclusions and Clinical Relevance In comparison with other widely used advanced methods, ECMHA‐PP demonstrated higher comprehensive performance, making it a reliable prognostic prediction model for breast cancer. Given its robust feature extraction and prediction capabilities.

Fan Zhang

Chaoyang Liu

Xinhong Zhang

PROTEOMICS - CLINICAL APPLICATIONS

蛋白质组学 - 临床应用已发展成为将蛋白质组学应用于人类疾病研究和临床转化领域的重要信息来源。该杂志每年出版 12 期，将在所有相关领域发表论文，包括：-旨在进一步了解人类疾病功能障碍的分子机制的基础蛋白质组学研究-致力于发现和验证诊断和预后疾病的蛋白质组学研究结果生物标志物——蛋白质组学用于发现新的药物靶点——蛋白质组学在药物开发过程中的应用——蛋白质组学作为临床试验的组成部分的应用。

Proteomics - Clinical Applications has become an important source of information for applying proteomics to human disease research and clinical translation. The journal publishes 12 issues annually, covering all relevant fields of research, including: - Basic proteomics studies aimed at further understanding the molecular mechanisms of human disease; - Proteomics research dedicated to discovering and validating biomarkers for diagnostic and prognostic diseases; - The application of proteomics in discovering new drug targets; - The role of proteomics in the drug development process; - The application of proteomics as part of clinical trials.

《Proteomics - Clinical Applications》已发展成为将蛋白质组学应用于人类疾病研究和临床转化领域的重要信息来源。该杂志每年出版12期，涵盖所有相关领域的研究，包括：- 基础蛋白质组学研究，旨在深入了解人类疾病的分子机制；- 发现和验证用于诊断和预后疾病的生物标志物的蛋白质组学研究；- 蛋白质组学在新药靶点发现中的应用；- 蛋白质组学在药物开发过程中的作用；- 蛋白质组学作为临床试验组成部分的应用。

Category	Quartile
生物学	4
生物学, 生化研究方法	4

ECMHA‐PP: A Breast Cancer Prognosis Prediction Model Based on Energy‐Constrained Multi‐Head Self‐Attention

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Category	Quartile
BIOCHEMICAL RESEARCH METHODS	3