Identification of cyclin protein using gradient boost decision tree algorithm

Zulfiqar, Hasan; Yuan, Shi-Shi; Huang, Qin-Lai; Sun, Zi-Jie; Yu, Xiaolong; Lin, Hao

doi:10.1016/j.csbj.2021.07.013

Cited by 49 publications

(25 citation statements)

References 113 publications

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“…Protein prediction is usually described as a binary classification problem ( Zhai et al, 2020 ; Zhang et al, 2021 ; Zulfiqar et al, 2021 ). We selected the random forest learning method for prediction modelling in the present study.…”

Section: Methodsmentioning

confidence: 99%

KK-DBP: A Multi-Feature Fusion Method for DNA-Binding Protein Identification Based on Random Forest

2021

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DNA-binding protein (DBP) is a protein with a special DNA binding domain that is associated with many important molecular biological mechanisms. Rapid development of computational methods has made it possible to predict DBP on a large scale; however, existing methods do not fully integrate DBP-related features, resulting in rough prediction results. In this article, we develop a DNA-binding protein identification method called KK-DBP. To improve prediction accuracy, we propose a feature extraction method that fuses multiple PSSM features. The experimental results show a prediction accuracy on the independent test dataset PDB186 of 81.22%, which is the highest of all existing methods.

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Section: Methodsmentioning

confidence: 99%

KK-DBP: A Multi-Feature Fusion Method for DNA-Binding Protein Identification Based on Random Forest

2021

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“…In this project, HIV-1 pol serves as the research object, and model inference is achieved through JRip of the RWeka software package of the R system, which makes fast rule inference on the aforementioned three sets of data by adopting the RIPPER algorithm. Then, reliability verification of the model inference (leave-one-out classification verification) is conducted to obtain the statistical evaluation of the rule inference results ( Zeng et al, 2018 ; Zeng et al, 2019 ; Dao et al, 2020 ; Fu et al, 2020 ; Zulfiqar et al, 2021 ). Finally, the sequence characteristics of recombinant HIV-1, recombinant drug-related patterns, and recombinant characteristic drug resistance patterns will be obtained.…”

Section: Methods Of Research On Drug Sensitivitymentioning

confidence: 99%

Bioinformatics Research on Drug Sensitivity Prediction

Chen

Juan

Lv³

et al. 2021

Front. Pharmacol.

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Modeling-based anti-cancer drug sensitivity prediction has been extensively studied in recent years. While most drug sensitivity prediction models only use gene expression data, the remarkable impacts of gene mutation, methylation, and copy number variation on drug sensitivity are neglected. Drug sensitivity prediction can both help protect patients from some adverse drug reactions and improve the efficacy of treatment. Genomics data are extremely useful for drug sensitivity prediction task. This article reviews the role of drug sensitivity prediction, describes a variety of methods for predicting drug sensitivity. Moreover, the research significance of drug sensitivity prediction, as well as existing problems are well discussed.

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“…At the same time, research show many proteins perform specific biological functions only after they participate in the formation of protein complexes and interact with other proteins in the complex, suggesting that protein interactions are related to protein complexes. Therefore, this part takes the key proteins as the research object and uses the methods based on the subcellular protein interaction network and subcellular importance to identify the key proteins ( Cheng et al, 2021a ; Zulfiqar et al, 2021 ).…”

Section: Key Protein Identification Methodsmentioning

confidence: 99%

Research on the Computational Prediction of Essential Genes

Guo

Dong

et al. 2021

Front. Cell Dev. Biol.

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Genes, the nucleotide sequences that encode a polypeptide chain or functional RNA, are the basic genetic unit controlling biological traits. They are the guarantee of the basic structures and functions in organisms, and they store information related to biological factors and processes such as blood type, gestation, growth, and apoptosis. The environment and genetics jointly affect important physiological processes such as reproduction, cell division, and protein synthesis. Genes are related to a wide range of phenomena including growth, decline, illness, aging, and death. During the evolution of organisms, there is a class of genes that exist in a conserved form in multiple species. These genes are often located on the dominant strand of DNA and tend to have higher expression levels. The protein encoded by it usually either performs very important functions or is responsible for maintaining and repairing these essential functions. Such genes are called persistent genes. Among them, the irreplaceable part of the body’s life activities is the essential gene. For example, when starch is the only source of energy, the genes related to starch digestion are essential genes. Without them, the organism will die because it cannot obtain enough energy to maintain basic functions. The function of the proteins encoded by these genes is thought to be fundamental to life. Nowadays, DNA can be extracted from blood, saliva, or tissue cells for genetic testing, and detailed genetic information can be obtained using the most advanced scientific instruments and technologies. The information gained from genetic testing is useful to assess the potential risks of disease, and to help determine the prognosis and development of diseases. Such information is also useful for developing personalized medication and providing targeted health guidance to improve the quality of life. Therefore, it is of great theoretical and practical significance to identify important and essential genes. In this paper, the research status of essential genes and the essential genome database of bacteria are reviewed, the computational prediction method of essential genes based on communication coding theory is expounded, and the significance and practical application value of essential genes are discussed.

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Identification of cyclin protein using gradient boost decision tree algorithm

Abstract: Graphical abstract

Cited by 49 publications

References 113 publications

KK-DBP: A Multi-Feature Fusion Method for DNA-Binding Protein Identification Based on Random Forest

KK-DBP: A Multi-Feature Fusion Method for DNA-Binding Protein Identification Based on Random Forest

Bioinformatics Research on Drug Sensitivity Prediction

Research on the Computational Prediction of Essential Genes

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