Morphological Segmentation Analysis and Texture-based Support Vector Machines Classification on Mice Liver Fibrosis Microscopic Images

Hepatocellular carcinoma (HCC) is a serious cancer which ranked the fourth in cancerrelated death worldwide. Hence, more accurate diagnostic models are urgently needed to aid the early HCC diagnosis under clinical scenarios and thus improve HCC treatment and survival. Several conventional methods have been used for discriminating HCC from cirrhosis tissues in patients without HCC (CwoHCC). However, the recognition successful rates are still far from satisfactory. In this study, we applied a computational approach that based on machine learning method to a set of microarray data generated from 1091 HCC samples and 242 CwoHCC samples. The within-sample relative expression orderings (REOs) method was used to extract numerical descriptors from gene expression profiles datasets. After removing the unrelated features by using maximum redundancy minimum relevance (mRMR) with incremental feature selection, we achieved "11-gene-pair" which could produce outstanding results. We further investigated the discriminate capability of the "11-gene-pair" for HCC recognition on several independent datasets. The wonderful results were obtained, demonstrating that the selected gene pairs can be signature for HCC. The proposed computational model can discriminate HCC and adjacent non-cancerous tissues from CwoHCC even for minimum biopsy specimens and inaccurately sampled specimens, which can be practical and effective for aiding the early HCC diagnosis at individual level.

Section: Classification Through Svmmentioning

confidence: 99%

Early Diagnosis of Hepatocellular Carcinoma Using Machine Learning Method

Zhang

Tan

Wang

et al. 2020

“…In addition, the SVM is also one of the common kernel learning methods for non-linear classification (Yang et al, 2019a). In recent years, SVMs have been successfully applied in bioinformatics fields (Xiong et al, 2012(Xiong et al, , 2019Zhang et al, 2015;Zhang J. et al, 2019;Ding et al, 2016a,b;Wei et al, 2016;Zeng et al, 2017;Zhao et al, 2017;Bu et al, 2018;Xu et al, 2018c;Hu et al, 2019;Liu and Li, 2019;Liu et al, 2019b;Wang et al, 2019;Dou et al, 2020). The LIBSVM is a widely used SVM tool.…”

Section: Support Vector Machinementioning

confidence: 99%

PSBP-SVM: A Machine Learning-Based Computational Identifier for Predicting Polystyrene Binding Peptides

Meng

Zhang

et al. 2020

Polystyrene binding peptides (PSBPs) play a key role in the immobilization process. The correct identification of PSBPs is the first step of all related works. In this paper, we proposed a novel support vector machine-based bioinformatic identification model. This model contains four machine learning steps, including feature extraction, feature selection, model training and optimization. In a five-fold cross validation test, this model achieves 90.38, 84.62, 87.50, and 0.90% SN, SP, ACC, and AUC, respectively. The performance of this model outperforms the state-of-the-art identifier in terms of the SN and ACC with a smaller feature set. Furthermore, we constructed a web server that includes the proposed model, which is freely accessible at http://server.malab.cn/ PSBP-SVM/index.jsp.

“…Support vector machine is a popular classifier which has solved several bioinformatics problems (Li et al, 2016;Chen et al, 2017;Bu et al, 2018;Zhang et al, 2018;Chao et al, 2019a,b;Sun et al, 2019;Wang et al, 2019). The "caret" R package was used to train models and tune the model hyperparameters based on SVM (Kuhn, 2008).…”

Section: Model Training and Evaluationmentioning

confidence: 99%

Identifying Antifreeze Proteins Based on Key Evolutionary Information

Sun

Ding

Wang

et al. 2020

Antifreeze proteins are important antifreeze materials that have been widely used in industry, including in cryopreservation, de-icing, and food storage applications. However, the quantity of some commercially produced antifreeze proteins is insufficient for large-scale industrial applications. Further, many antifreeze proteins have properties such as cytotoxicity, severely hindering their applications. Understanding the mechanisms underlying the protein-ice interactions and identifying novel antifreeze proteins are, therefore, urgently needed. In this study, to uncover the mechanisms underlying protein-ice interactions and provide an efficient and accurate tool for identifying antifreeze proteins, we assessed various evolutionary features based on position-specific scoring matrices (PSSMs) and evaluated their importance for discriminating of antifreeze and non-antifreeze proteins. We then parsimoniously selected seven key features with the highest importance. We found that the selected features showed opposite tendencies (regarding the conservation of certain amino acids) between antifreeze and non-antifreeze proteins. Five out of the seven features had relatively high contributions to the discrimination of antifreeze and non-antifreeze proteins, as revealed by a principal component analysis, i.e., the conservation of the replacement of Cys, Trp, and Gly in antifreeze proteins by Ala, Met, and Ala, respectively, in the related proteins, and the conservation of the replacement of Arg in non-antifreeze proteins by Ser and Arg in the related proteins. Based on the seven parsimoniously selected key features, we established a classifier using support vector machine, which outperformed the state-of-the-art tools. These results suggest that understanding evolutionary information is crucial to designing accurate automated methods for discriminating antifreeze and non-antifreeze proteins. Our classifier, therefore, is an efficient tool for annotating new proteins with antifreeze functions based on sequence information and can facilitate their application in industry.