IsoFrog:一个可逆跳跃马尔可夫链蒙特卡罗特征选择为基础的方法预测异构体函数。

IF 4.4 3区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Yiwei Liu, Changhuo Yang, Hong-Dong Li, Jianxin Wang
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引用次数: 0

摘要

动机:一个基因可以通过选择性剪接产生几个具有不同功能的同种异构体。不断的努力致力于开发机器学习方法来预测同形函数。然而,现有的方法没有考虑到每个特征与特定函数的相关性,忽略了不相关特征引起的噪声。在这种情况下,我们假设构建一个特征选择框架来提取与函数相关的特征可能有助于提高模型在同形函数预测中的准确性。结果:在本文中,我们提出了一种基于特征选择的IsoFrog方法来预测异构体函数。首先,IsoFrog采用基于可逆跳跃马尔可夫链蒙特卡罗(RJMCMC)的特征选择框架来评估特征对基因功能的重要性。其次,采用顺序特征选择程序选择与功能相关的特征子集。该策略为特定功能筛选相关特征,同时剔除不相关特征,提高输入特征的有效性。然后,将选择的特征输入到我们提出的改进域不变偏最小二乘方法中,该方法为每个正MIG优先考虑最可能的正异构体,并利用diPLS进行异构体函数预测。在三个数据集上的测试表明,我们的方法比六种最先进的方法取得了更好的性能,基于rjmcmc的特征选择框架优于三种经典的特征选择方法。我们期望这一方法将促进异构体功能的识别,并进一步激发新方法的发展。可用性和实现:IsoFrog可以在https://github.com/genemine/IsoFrog上免费获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

IsoFrog: a reversible jump Markov Chain Monte Carlo feature selection-based method for predicting isoform functions.

IsoFrog: a reversible jump Markov Chain Monte Carlo feature selection-based method for predicting isoform functions.

IsoFrog: a reversible jump Markov Chain Monte Carlo feature selection-based method for predicting isoform functions.

IsoFrog: a reversible jump Markov Chain Monte Carlo feature selection-based method for predicting isoform functions.

Motivation: A single gene may yield several isoforms with different functions through alternative splicing. Continuous efforts are devoted to developing machine-learning methods to predict isoform functions. However, existing methods do not consider the relevance of each feature to specific functions and ignore the noise caused by the irrelevant features. In this case, we hypothesize that constructing a feature selection framework to extract the function-relevant features might help improve the model accuracy in isoform function prediction.

Results: In this article, we present a feature selection-based approach named IsoFrog to predict isoform functions. First, IsoFrog adopts a reversible jump Markov Chain Monte Carlo (RJMCMC)-based feature selection framework to assess the feature importance to gene functions. Second, a sequential feature selection procedure is applied to select a subset of function-relevant features. This strategy screens the relevant features for the specific function while eliminating irrelevant ones, improving the effectiveness of the input features. Then, the selected features are input into our proposed method modified domain-invariant partial least squares, which prioritizes the most likely positive isoform for each positive MIG and utilizes diPLS for isoform function prediction. Tested on three datasets, our method achieves superior performance over six state-of-the-art methods, and the RJMCMC-based feature selection framework outperforms three classic feature selection methods. We expect this proposed methodology will promote the identification of isoform functions and further inspire the development of new methods.

Availability and implementation: IsoFrog is freely available at https://github.com/genemine/IsoFrog.

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来源期刊
Bioinformatics
Bioinformatics 生物-生化研究方法
CiteScore
11.20
自引率
5.20%
发文量
753
审稿时长
2.1 months
期刊介绍: The leading journal in its field, Bioinformatics publishes the highest quality scientific papers and review articles of interest to academic and industrial researchers. Its main focus is on new developments in genome bioinformatics and computational biology. Two distinct sections within the journal - Discovery Notes and Application Notes- focus on shorter papers; the former reporting biologically interesting discoveries using computational methods, the latter exploring the applications used for experiments.
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