ECFS-DEA: an ensemble classifier-based feature selection for differential expression analysis on expression profiles

Zhao, Xudong; Jiao, Qing; Li, Hangyu; Wu, Yiming; Wang, Hanxu; Huang, Shan; Wang, Guohua

doi:10.1186/s12859-020-3388-y

Cited by 68 publications

(42 citation statements)

References 30 publications

(14 reference statements)

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“…Feature extraction is very important for constructing a predictor [ 28 – 37 ]. We use the CTDC method in iLearn toolkit to extract features of protein sequences.…”

Section: Methodsmentioning

confidence: 99%

A Method for Identifying Vesicle Transport Proteins Based on LibSVM and MRMD

Tao

Teng

et al. 2020

Computational and Mathematical Methods in Medicine

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With the development of computer technology, many machine learning algorithms have been applied to the field of biology, forming the discipline of bioinformatics. Protein function prediction is a classic research topic in this subject area. Though many scholars have made achievements in identifying protein by different algorithms, they often extract a large number of feature types and use very complex classification methods to obtain little improvement in the classification effect, and this process is very time-consuming. In this research, we attempt to utilize as few features as possible to classify vesicular transportation proteins and to simultaneously obtain a comparative satisfactory classification result. We adopt CTDC which is a submethod of the method of composition, transition, and distribution (CTD) to extract only 39 features from each sequence, and LibSVM is used as the classification method. We use the SMOTE method to deal with the problem of dataset imbalance. There are 11619 protein sequences in our dataset. We selected 4428 sequences to train our classification model and selected other 1832 sequences from our dataset to test the classification effect and finally achieved an accuracy of 71.77%. After dimension reduction by MRMD, the accuracy is 72.16%.

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“…Feature extraction is very important for constructing a predictor [ 28 – 37 ]. We use the CTDC method in iLearn toolkit to extract features of protein sequences.…”

Section: Methodsmentioning

confidence: 99%

A Method for Identifying Vesicle Transport Proteins Based on LibSVM and MRMD

Tao

Teng

et al. 2020

Computational and Mathematical Methods in Medicine

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“…Due to the limited sample size, resampling is only an approximation to the population. In our previous work, it has been pointed out that different base classifiers should be considered according to various sample distributions [50]. However, the base classifier was interactively appointed in [50].…”

Section: Purpose Of Using Base Classifier Selectionmentioning

confidence: 99%

“…3 is a semi-automatic way for variable selection. Here, it goes against the interactive way that uses a manual selection within a table or on a 2-D scatter plot [50]. Also, it abandons the automatic way of automatic clustering [51] on accumulated scores.…”

Section: Purpose Of Using a Line Chart For Variable Selectionmentioning

confidence: 99%

Variable Selection from a Feature Representing Protein Sequences: A Case of Classification on Bacterial Type IV Secreted Effectors

Zhang

et al. 2020

Preprint

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Background: Classification of certain proteins with specific functions is momentous for biological research. Encoding approaches of protein sequences for feature extraction play an important role in protein classification. Many computational methods (namely classifiers) are used for classification on protein sequences according to various encoding approaches. Commonly, protein sequences keep certain labels corresponding to different categories of biological functions (e.g., bacterial type IV secreted effectors or not), which makes protein prediction a fantasy. As to protein prediction, a kernel set of protein sequences keeping certain labels certified by biological experiments should be existent in advance. However, it has been hardly ever seen in prevailing researches. Therefore, unsupervised learning rather than supervised learning (e.g. classification) should be considered. As to protein classification, various classifiers may help to evaluate the effectiveness of different encoding approaches. Besides, variable selection from an encoded feature representing protein sequences is an important issue that also needs to be considered.Results: Focusing on the latter problem, we propose a new method for variable selection from an encoded feature representing protein sequences. Taking a benchmark dataset as a case, experiments are made to identify bacterial type IV secreted effectors from protein sequences, which indicates the effectiveness of our method. Conclusions: Certain variables other than an encoded feature they belong to do work for discrimination between different types of proteins. In addition, ensemble classifiers with an automatic assignment of different base classifiers do achieve a better classification result.

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“…It is often necessary to identify individual cells and follow them over time to gain biological insights from time-lapse microscopy recordings of cell behavior. Microscopic target tracking can provide technical support for the analysis of other features in biological and medical research (Cheng, 2019;Zhao et al, 2020). Therefore, it is of great significance to find an automatic and reliable way to track multiple cells.…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning for Data Association in Cell Tracking

Wang

Zhao

et al. 2020

Front. Bioeng. Biotechnol.

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Accurate target detection and association are vital for the development of reliable target tracking, especially for cell tracking based on microscopy images due to the similarity of cells. We propose a deep reinforcement learning method to associate the detected targets between frames. According to the dynamic model of each target, the cost matrix is produced by conjointly considering various features of targets and then used as the input of a neural network. The proposed neural network is trained using reinforcement learning to predict a distribution over the association solution. Furthermore, we design a residual convolutional neural network that results in more efficient learning. We validate our method on two applications: the multiple target tracking simulation and the ISBI cell tracking. The results demonstrate that our approach based on reinforcement learning techniques could effectively track targets following different motion patterns and show competitive results.

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ECFS-DEA: an ensemble classifier-based feature selection for differential expression analysis on expression profiles

Cited by 68 publications

References 30 publications

A Method for Identifying Vesicle Transport Proteins Based on LibSVM and MRMD

A Method for Identifying Vesicle Transport Proteins Based on LibSVM and MRMD

Variable Selection from a Feature Representing Protein Sequences: A Case of Classification on Bacterial Type IV Secreted Effectors

Deep Reinforcement Learning for Data Association in Cell Tracking

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