Gene selection plays a pivotal role in oncology research for improving
outcome prediction accuracy and facilitating cost-effective genomic profiling
for cancer patients. This paper introduces two gene selection strategies for
deep learning-based survival prediction models. The first strategy uses a
sparsity-inducing method while the second one uses importance based gene
selection for identifying relevant genes. Our overall approach leverages the
power of deep learning to model complex biological data structures, while
sparsity-inducing methods ensure the selection process focuses on the most
informative genes, minimizing noise and redundancy. Through comprehensive
experimentation on diverse genomic and survival datasets, we demonstrate that
our strategy not only identifies gene signatures with high predictive power for
survival outcomes but can also streamlines the process for low-cost genomic
profiling. The implications of this research are profound as it offers a
scalable and effective tool for advancing personalized medicine and targeted
cancer therapies. By pushing the boundaries of gene selection methodologies,
our work contributes significantly to the ongoing efforts in cancer genomics,
promising improved diagnostic and prognostic capabilities in clinical settings.

基于深度学习的生存预测模型中的两种基因选择策略，一种利用稀疏性诱导方法，另一种利用重要性基因选择来识别相关基因，通过广泛的实验验证表明，该策略不仅可以识别具有高预测能力的基因特征用于生存结局，还可以简化低成本基因组筛查过程，为个体化医学和靶向癌症治疗的推进提供了一个可伸缩且有效的工具。

推进肿瘤学基因选择：深度学习与稀疏性的精确基因选择融合

Advancing Gene Selection in Oncology: A Fusion of Deep Learning and  Sparsity for Precision Gene Selection

Cancer remains a global challenge due to its growing clinical and economic
burden. Its uniquely personal manifestation, which makes treatment difficult,
has fuelled the quest for personalized treatment strategies. Thus, genomic
profiling is increasingly becoming part of clinical diagnostic panels.
Effective use of such panels requires accurate drug response prediction (DRP)
models, which are challenging to build due to limited labelled patient data.
Previous methods to address this problem have used various forms of transfer
learning. However, they do not explicitly model the variable length sequential
structure of the list of mutations in such diagnostic panels. Further, they do
not utilize auxiliary information (like patient survival) for model training.
We address these limitations through a novel transformer based method, which
surpasses the performance of state-of-the-art DRP models on benchmark data. We
also present the design of a treatment recommendation system (TRS), which is
currently deployed at the National University Hospital, Singapore and is being
evaluated in a clinical trial.

癌症个性化治疗是全球面临的一个挑战，在临床和经济上都具有不断增长的负担。为了有效利用临床诊断面板中的基因组分析，需要准确的药物反应预测模型，本研究提出了一种基于转换器的创新方法，超越了基准数据上最先进的药物反应预测模型的性能，同时还设计了一种治疗建议系统，并正在新加坡国立大学医院进行临床试验评估。