: Human gene sequences are considered a primary source of comprehensive information about different body conditions. A wide variety of diseases including cancer, heart issues, brain issues, genetic issues, etc. can be pre-empted via efficient analysis of genomic sequences. Researchers have proposed different configurations of machine learning models for processing genomic sequences, and each of these models varies in terms of their performance & applicability characteristics. Models that use bioinspired optimizations are generally slower, but have superior incrementalperformance, while models that use one-shot learning achieve higher instantaneous accuracy but cannot be scaled for larger disease-sets. Due to such variations, it is difficult for genomic system designers to identify optimum models for their application-specific & performance-specific use cases. To overcome this issue, a detailed survey of different genomic processing models in terms of their functional nuances, application-specific advantages, deployment-specific limitations, and contextual future scopes is discussed in this text. Based on this discussion, researchers will be able to identify optimal models for their functional use cases. This text also compares the reviewed models in terms of their quantitative parameter sets, which include, the accuracy of classification, delay needed to classify large-length sequences, precision levels, scalability levels, and deployment cost, which will assist readers in selecting deployment-specific models for their contextual clinical scenarios. This text also evaluates a novel Genome Processing Efficiency Rank (GPER) for each of these models, which will allow readers to identify models with higher performance and low overheads under real-time scenarios.

中文翻译：

---

使用基因组序列处理模型进行疾病识别的研究和分析：实证综述

：人类基因序列被认为是有关不同身体状况的综合信息的主要来源。通过对基因组序列的有效分析，可以预防多种疾病，包括癌症、心脏问题、大脑问题、遗传问题等。研究人员提出了用于处理基因组序列的机器学习模型的不同配置，并且每个模型的性能和适用性特征各不相同。使用仿生优化的模型通常速度较慢，但​​具有卓越的增量性能，而使用一次性学习的模型可以实现更高的瞬时精度，但无法扩展到更大的疾病集。由于这些变化，基因组系统设计者很难为其特定应用和特定性能用例确定最佳模型。为了克服这个问题，本文讨论了不同基因组处理模型的功能细微差别、特定于应用程序的优势、特定于部署的限制和未来背景范围的详细调查。基于此讨论，研究人员将能够为其功能用例确定最佳模型。本文还比较了所审查模型的定量参数集，包括分类的准确性、分类大长度序列所需的延迟、精度水平、可扩展性水平和部署成本，这将有助于读者选择部署。针对其临床场景的特定模型。本文还评估了每个模型的新颖的基因组处理效率排名（GPER），这将使读者能够识别在实时场景下具有更高性能和低开销的模型。