必需基因对任何生物体的生长和生存至关重要。机器学习方法补充了实验方法,以最大程度地减少本质分析所需的资源。以前的研究表明,需要发现对必需基因进行显著分类的相关特征,提高预测模型跨生物体的泛化性,并构建一个稳健的黄金标准作为列车数据的类标签,以增强预测能力。研究结果还表明,机器学习方法的一个显著限制是预测有条件的必需基因。基因的本质状态可以由于生物体的特定条件而改变。这篇综述探讨了应用于基本基因预测任务的各种方法,他们的长处,限制和负责有效计算预测必需基因的因素。我们讨论了特征的类别以及它们如何对重要性预测模型的分类性能做出贡献。五类功能,即,基因序列,蛋白质序列,网络拓扑,同源性和基于基因本体的特征,为秀丽隐杆线虫生成,以对其必要性预测能力进行比较分析。基于基因本体论的特征类别主要由于其与基因的生物学功能高度相关而优于其他特征类别。然而,拓扑特征类别提供了最高的判别能力,使其更适合于本质预测。机器学习预测必需基因条件的主要限制因素是无法获得可以训练分类器的感兴趣条件的标记数据。因此,合作机器学习可以进一步利用在条件本质预测中表现良好的模型。
必要基因的鉴定是必要的,因为它提供了对核心结构和功能的理解,加速药物目标的发现,在其他功能中。最近的研究已经应用机器学习来补充必需基因的实验鉴定。然而,有几个因素限制了机器学习方法的性能。这篇综述旨在提供预测生物体必需基因的标准程序和资源。并强调了导致当前使用机器学习进行条件基因重要性预测的局限性的因素。特征的选择和ML技术被确定为有效预测必需基因的重要因素。
Essential genes are critical for the growth and survival of any organism. The machine learning approach complements the experimental methods to minimize the resources required for essentiality assays. Previous studies revealed the need to discover relevant features that significantly classify essential genes, improve on the generalizability of prediction models across organisms, and construct a robust gold standard as the class label for the train data to enhance prediction. Findings also show that a significant limitation of the machine learning approach is predicting conditionally essential genes. The essentiality status of a gene can change due to a specific condition of the organism. This
review examines various methods applied to essential gene prediction task, their strengths, limitations and the factors responsible for effective computational prediction of essential genes. We discussed categories of features and how they contribute to the classification performance of essentiality prediction models. Five categories of features, namely, gene sequence, protein sequence, network topology, homology and gene ontology-based features, were generated for Caenorhabditis elegans to perform a comparative analysis of their essentiality prediction capacity. Gene ontology-based feature category outperformed other categories of features majorly due to its high correlation with the genes\' biological functions. However, the topology feature category provided the highest discriminatory power making it more suitable for essentiality prediction. The major limiting factor of machine learning to predict essential genes conditionality is the unavailability of labeled data for interest conditions that can train a classifier. Therefore, cooperative machine learning could further exploit models that can perform well in conditional essentiality predictions.
Identification of essential genes is imperative because it provides an understanding of the core structure and function, accelerating drug targets\' discovery, among other functions. Recent studies have applied machine learning to complement the experimental identification of essential genes. However, several factors are limiting the performance of machine learning approaches. This
review aims to present the standard procedure and resources available for predicting essential genes in organisms, and also highlight the factors responsible for the current limitation in using machine learning for conditional gene essentiality prediction. The choice of features and ML technique was identified as an important factor to predict essential genes effectively.