Dataset Information

Identification of Cancerlectins Using Support Vector Machines With Fusion of G-Gap Dipeptide.

ABSTRACT: The cancerlectin plays an important role in the initiation, survival, growth, metastasis, and spread of cancer. Therefore, to study the function of cancerlectin is greatly significant because it can help to identify tumor markers and tumor prevention, treatment, and prognosis. However, plenty of studies have generated a large amount of protein data. Traditional prediction methods have been unable to meet the needs of analysis. Developing powerful computational models based on these data to discriminate cancerlectins and non-cancerlectins on a large scale has been treated as one of the most important topics. In this study, we developed a feature extraction method to identify cancerlectins based on fusion of g-gap dipeptides. The analysis of variance was used to select the optimal feature set and a support vector machine was used to classify the data. The rigorous nested 10-fold cross-validation results, demonstrated that our method obtained the prediction accuracy of 83.91% and sensitivity of 83.15%. At the same time, in order to evaluate the performance of the classification model constructed in this work, we constructed a new data set. The prediction accuracy of the new data set reaches 83.3%. Experimental results show that the performance of our method is better than the state-of-the-art methods.

SUBMITTER: Qian L

PROVIDER: S-EPMC7147460 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

Identification of Cancerlectins Using Support Vector Machines With Fusion of G-Gap Dipeptide.

Qian Lili L Wen Yaping Y Han Guosheng G

Frontiers in genetics 20200403

The cancerlectin plays an important role in the initiation, survival, growth, metastasis, and spread of cancer. Therefore, to study the function of cancerlectin is greatly significant because it can help to identify tumor markers and tumor prevention, treatment, and prognosis. However, plenty of studies have generated a large amount of protein data. Traditional prediction methods have been unable to meet the needs of analysis. Developing powerful computational models based on these data to discr ...[more]

PMID: 32318092

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Project description:BACKGROUND:While the genomes of hundreds of organisms have been sequenced and good approaches exist for finding protein encoding genes, an important remaining challenge is predicting the functions of the large fraction of genes for which there is no annotation. Large gene expression datasets from microarray experiments already exist and many of these can be used to help assign potential functions to these genes. We have applied Support Vector Machines (SVM), a sigmoid fitting function and a stratified cross-validation approach to analyze a large microarray experiment dataset from Drosophila melanogaster in order to predict possible functions for previously un-annotated genes. A total of approximately 5043 different genes, or about one-third of the predicted genes in the D. melanogaster genome, are represented in the dataset and 1854 (or 37%) of these genes are un-annotated. RESULTS:39 Gene Ontology Biological Process (GO-BP) categories were found with precision value equal or larger than 0.75, when recall was fixed at the 0.4 level. For two of those categories, we have provided additional support for assigning given genes to the category by showing that the majority of transcripts for the genes belonging in a given category have a similar localization pattern during embryogenesis. Additionally, by assessing the predictions using a confidence score, we have been able to provide a putative GO-BP term for 1422 previously un-annotated genes or about 77% of the un-annotated genes represented on the microarray and about 19% of all of the un-annotated genes in the D. melanogaster genome. CONCLUSIONS:Our study successfully employs a number of SVM classifiers, accompanied by detailed calibration and validation techniques, to generate a number of predictions for new annotations for D. melanogaster genes. The applied probabilistic analysis to SVM output improves the interpretability of the prediction results and the objectivity of the validation procedure.

Project description:Recognizing transcription start sites is key to gene identification. Several approaches have been employed in related problems such as detecting translation initiation sites or promoters, many of the most recent ones based on machine learning. Deep learning methods have been proven to be exceptionally effective for this task, but their use in transcription start site identification has not yet been explored in depth. Also, the very few existing works do not compare their methods to support vector machines (SVMs), the most established technique in this area of study, nor provide the curated dataset used in the study. The reduced amount of published papers in this specific problem could be explained by this lack of datasets. Given that both support vector machines and deep neural networks have been applied in related problems with remarkable results, we compared their performance in transcription start site predictions, concluding that SVMs are computationally much slower, and deep learning methods, specially long short-term memory neural networks (LSTMs), are best suited to work with sequences than SVMs. For such a purpose, we used the reference human genome GRCh38. Additionally, we studied two different aspects related to data processing: the proper way to generate training examples and the imbalanced nature of the data. Furthermore, the generalization performance of the models studied was also tested using the mouse genome, where the LSTM neural network stood out from the rest of the algorithms. To sum up, this article provides an analysis of the best architecture choices in transcription start site identification, as well as a method to generate transcription start site datasets including negative instances on any species available in Ensembl. We found that deep learning methods are better suited than SVMs to solve this problem, being more efficient and better adapted to long sequences and large amounts of data. We also create a transcription start site (TSS) dataset large enough to be used in deep learning experiments.

Dataset Information

Identification of Cancerlectins Using Support Vector Machines With Fusion of G-Gap Dipeptide.

Publications

Identification of Cancerlectins Using Support Vector Machines With Fusion of G-Gap Dipeptide.

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