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。各种各样的疾病,包括癌症,心脏问题,大脑问题,遗传问题,等。可以通过基因组序列的有效分析来抢占。研究人员提出了不同配置的机器学习模型来处理基因组序列,这些模型中的每一个在性能和适用性特征方面都有所不同。使用生物启发优化的模型通常较慢,但具有优越的增量性能,而使用一次性学习的模型可以获得更高的瞬时精度,但不能针对更大的疾病集进行缩放。由于这种变化,基因组系统设计人员很难为其特定于应用程序和特定于性能的用例确定最佳模型。为了克服这个问题,根据功能细微差别对不同基因组处理模型进行了详细调查,特定于应用的优势,特定于部署的限制,本文讨论了上下文未来范围。基于这一讨论,研究人员将能够为他们的功能用例确定最佳模型。本文还比较了所审查的模型的定量参数集,其中包括,分类的准确性,对大长度序列进行分类所需的延迟,精密水平,可扩展性级别,和部署成本,这将帮助读者为其上下文临床场景选择特定于部署的模型。本文还评估了每个模型的新基因组加工效率等级(GPER),这将使读者能够在实时场景下识别具有更高性能和低开销的模型。
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 incremental-performance, 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.