Journal of Bioinformatics and Computational Biology最新文献_第8页

RNA modification writers influence tumor microenvironment in gastric cancer and prospects of targeted drug therapy RNA修饰对胃癌肿瘤微环境的影响及靶向药物治疗前景

IF 1 4区生物学

Journal of Bioinformatics and Computational Biology Pub Date : 2022-03-14 DOI: 10.1142/S0219720022500044

P. Song, Sheng Zhou, Xiaoyang Qi, Y. Jiao, Y. Gong, Jie Zhao, Haojun Yang, Z. Qian, J. Qian, Liming Tang

{"title":"RNA modification writers influence tumor microenvironment in gastric cancer and prospects of targeted drug therapy","authors":"P. Song, Sheng Zhou, Xiaoyang Qi, Y. Jiao, Y. Gong, Jie Zhao, Haojun Yang, Z. Qian, J. Qian, Liming Tang","doi":"10.1142/S0219720022500044","DOIUrl":"https://doi.org/10.1142/S0219720022500044","url":null,"abstract":"Background: RNA adenosine modifications are crucial for regulating RNA levels. N6-methyladenosine (m6A), N1-methyladenosine (m1A), adenosine-to-inosine RNA editing, and alternative polyadenylation (APA) are four major RNA modification types. Methods: We evaluated the altered mRNA expression profiles of 27 RNA modification enzymes and compared the differences in tumor microenvironment (TME) and clinical prognosis between two RNA modification patterns using unsupervised clustering. Then, we constructed a scoring system, WM_score, and quantified the RNA modifications in patients of gastric cancer (GC), associating WM_score with TME, clinical outcomes, and effectiveness of targeted therapies. Results: RNA adenosine modifications strongly correlated with TME and could predict the degree of TME cell infiltration, genetic variation, and clinical prognosis. Two modification patterns were identified according to high and low WM_scores. Tumors in the WM_score-high subgroup were closely linked with survival advantage, CD4[Formula: see text] T-cell infiltration, high tumor mutation burden, and cell cycle signaling pathways, whereas those in the WM_score-low subgroup showed strong infiltration of inflammatory cells and poor survival. Regarding the immunotherapy response, a high WM_score showed a significant correlation with PD-L1 expression, predicting the effect of PD-L1 blockade therapy. Conclusion: The WM_scoring system could facilitate scoring and prediction of GC prognosis.","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":"1 1","pages":"2250004"},"PeriodicalIF":1.0,"publicationDate":"2022-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48168063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A sequence-based two-layer predictor for identifying enhancers and their strength through enhanced feature extraction 一种基于序列的两层预测器，用于通过增强特征提取识别增强子及其强度

IF 1 4区生物学

Journal of Bioinformatics and Computational Biology Pub Date : 2022-03-09 DOI: 10.1142/S0219720022500056

Santhosh Amilpur, Raju Bhukya

{"title":"A sequence-based two-layer predictor for identifying enhancers and their strength through enhanced feature extraction","authors":"Santhosh Amilpur, Raju Bhukya","doi":"10.1142/S0219720022500056","DOIUrl":"https://doi.org/10.1142/S0219720022500056","url":null,"abstract":"Enhancers are short regulatory DNA fragments that are bound with proteins called activators. They are free-bound and distant elements, which play a vital role in controlling gene expression. It is challenging to identify enhancers and their strength due to their dynamic nature. Although some machine learning methods exist to accelerate identification process, their prediction accuracy and efficiency will need more improvement. In this regard, we propose a two-layer prediction model with enhanced feature extraction strategy which does feature combination from improved position-specific amino acid propensity (PSTKNC) method along with Enhanced Nucleic Acid Composition (ENAC) and Composition of k-spaced Nucleic Acid Pairs (CKSNAP). The feature sets from all three feature extraction approaches were concatenated and then sent through a simple artificial neural network (ANN) to accurately identify enhancers in the first layer and their strength in the second layer. Experiments are conducted on benchmark chromatin nine cell lines dataset. A 10-fold cross validation method is employed to evaluate model's performance. The results show that the proposed model gives an outstanding performance with 94.50%, 0.8903 of accuracy and Matthew's correlation coefficient (MCC) in predicting enhancers and fairly does well with independent test also when compared with all other existing methods.","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":"1 1","pages":"2250005"},"PeriodicalIF":1.0,"publicationDate":"2022-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41464283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Identification of cancer-related module in protein-protein interaction network based on gene prioritization. 基于基因优先级的蛋白质相互作用网络中癌症相关模块的鉴定。

IF 1 4区生物学

Journal of Bioinformatics and Computational Biology Pub Date : 2022-02-01 Epub Date: 2021-12-03 DOI: 10.1142/S0219720021500311

Jingli Wu, Qi Zhang, Gaoshi Li

{"title":"Identification of cancer-related module in protein-protein interaction network based on gene prioritization.","authors":"Jingli Wu, Qi Zhang, Gaoshi Li","doi":"10.1142/S0219720021500311","DOIUrl":"https://doi.org/10.1142/S0219720021500311","url":null,"abstract":"With the rapid development of deep sequencing technologies, a large amount of high-throughput data has been available for studying the carcinogenic mechanism at the molecular level. It has been widely accepted that the development and progression of cancer are regulated by modules/pathways rather than individual genes. The investigation of identifying cancer-related active modules has received an extensive attention. In this paper, we put forward an identification method ModFinder by integrating both biological networks and gene expression profiles. More concretely, a gene scoring function is devised by using the regression model with [Formula: see text]-step random walk kernel, and the genes are ranked according to both of their active scores and degrees in the PPI network. Then a greedy algorithm NSEA is introduced to find an active module with high score and strong connectivity. Experiments were performed on both simulated data and real biological one, i.e. breast cancer and cervical cancer. Compared with the previous methods SigMod, LEAN and RegMod, ModFinder shows competitive performance. It can successfully identify a well-connected module that contains a large proportion of cancer-related genes, including some well-known oncogenes or tumor suppressors enriched in cancer-related pathways.","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":"20 1","pages":"2150031"},"PeriodicalIF":1.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39956506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A new Bayesian approach for QTL mapping of family data. 一种新的贝叶斯方法用于家族数据的QTL映射。

IF 1 4区生物学

Journal of Bioinformatics and Computational Biology Pub Date : 2022-02-01 Epub Date: 2021-11-19 DOI: 10.1142/S021972002150030X

Daiane Aparecida Zuanetti, Luis Aparecido Milan

引用次数: 0

Identifying duplications and lateral gene transfers simultaneously and rapidly. 识别复制和横向基因转移同时和快速。

IF 1 4区生物学

Journal of Bioinformatics and Computational Biology Pub Date : 2022-02-01 Epub Date: 2021-12-09 DOI: 10.1142/S0219720021500335

Zhi-Zhong Chen, Fei Deng, Lusheng Wang

引用次数: 0

Female reproduction-specific proteins, origins in marine species, and their evolution in the animal kingdom. 雌性生殖特异性蛋白，在海洋物种中的起源，以及它们在动物界的进化。

IF 1 4区生物学

Journal of Bioinformatics and Computational Biology Pub Date : 2022-02-01 Epub Date: 2022-01-12 DOI: 10.1142/S0219720022400017

Laura Rebeca Jimenez-Gutierrez

引用次数: 0

Mining sponge phenomena in RNA expression data. 挖掘RNA表达数据中的海绵现象。

IF 1 4区生物学

Journal of Bioinformatics and Computational Biology Pub Date : 2022-02-01 Epub Date: 2021-11-18 DOI: 10.1142/S0219720021500220

Fabrizio Angiulli, Teresa Colombo, Fabio Fassetti, Angelo Furfaro, Paola Paci

{"title":"Mining sponge phenomena in RNA expression data.","authors":"Fabrizio Angiulli, Teresa Colombo, Fabio Fassetti, Angelo Furfaro, Paola Paci","doi":"10.1142/S0219720021500220","DOIUrl":"https://doi.org/10.1142/S0219720021500220","url":null,"abstract":"In the last few years, the interactions among competing endogenous RNAs (ceRNAs) have been recognized as a key post-transcriptional regulatory mechanism in cell differentiation, tissue development, and disease. Notably, such sponge phenomena substracting active microRNAs from their silencing targets have been recognized as having a potential oncosuppressive, or oncogenic, role in several cancer types. Hence, the ability to predict sponges from the analysis of large expression data sets (e.g. from international cancer projects) has become an important data mining task in bioinformatics. We present a technique designed to mine sponge phenomena whose presence or absence may discriminate between healthy and unhealthy populations of samples in tumoral or normal expression data sets, thus providing lists of candidates potentially relevant in the pathology. With this aim, we search for pairs of elements acting as ceRNA for a given miRNA, namely, we aim at discovering miRNA-RNA pairs involved in phenomena which are clearly present in one population and almost absent in the other one. The results on tumoral expression data, concerning five different cancer types, confirmed the effectiveness of the approach in mining interesting knowledge. Indeed, 32 out of 33 miRNAs and 22 out of 25 protein-coding genes identified as top scoring in our analysis are corroborated by having been similarly associated with cancer processes in independent studies. In fact, the subset of miRNAs selected by the sponge analysis results in a significant enrichment of annotation for the KEGG32 pathway \"microRNAs in cancer\" when tested with the commonly used bioinformatic resource DAVID. Moreover, often the cancer datasets where our sponge analysis identified a miRNA as top scoring match the one reported already in the pertaining literature.","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":"20 1","pages":"2150022"},"PeriodicalIF":1.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39636898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

O-glycosylation site prediction for Homo sapiens by combining properties and sequence features with support vector machine. 基于属性与序列特征结合的智人o -糖基化位点预测。

IF 1 4区生物学

Journal of Bioinformatics and Computational Biology Pub Date : 2022-02-01 Epub Date: 2021-11-19 DOI: 10.1142/S0219720021500293

Yan Zhu, Shuwan Yin, Jia Zheng, Yixia Shi, Cangzhi Jia

{"title":"O-glycosylation site prediction for Homo sapiens by combining properties and sequence features with support vector machine.","authors":"Yan Zhu, Shuwan Yin, Jia Zheng, Yixia Shi, Cangzhi Jia","doi":"10.1142/S0219720021500293","DOIUrl":"https://doi.org/10.1142/S0219720021500293","url":null,"abstract":"O-glycosylation is a protein posttranslational modification important in regulating almost all cells. It is related to a large number of physiological and pathological phenomena. Recognizing O-glycosylation sites is the key to further investigating the molecular mechanism of protein posttranslational modification. This study aimed to collect a reliable dataset on Homo sapiens and develop an O-glycosylation predictor for Homo sapiens, named Captor, through multiple features. A random undersampling method and a synthetic minority oversampling technique were employed to deal with imbalanced data. In addition, the Kruskal-Wallis (K-W) test was adopted to optimize feature vectors and improve the performance of the model. A support vector machine, due to its optimal performance, was used to train and optimize the final prediction model after a comprehensive comparison of various classifiers in traditional machine learning methods and deep learning. On the independent test set, Captor outperformed the existing O-glycosylation tool, suggesting that Captor could provide more instructive guidance for further experimental research on O-glycosylation. The source code and datasets are available at https://github.com/YanZhu06/Captor/.","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":"20 1","pages":"2150029"},"PeriodicalIF":1.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39645905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Clarifying real receptor binding site between coronavirus HCoV-HKU1 and 9-O-Ac-Sia based on molecular docking. 基于分子对接的冠状病毒HCoV-HKU1与9-O-Ac-Sia真正受体结合位点的厘清

IF 1 4区生物学

Journal of Bioinformatics and Computational Biology Pub Date : 2022-02-01 Epub Date: 2022-01-20 DOI: 10.1142/S0219720021500347

Xiaoyu Liu, Jingying Zhao, Sicong Li, Cai Wei, Shihang Wang, Xuanyu Xu, Yin Zheng, Xiangyu Deng, Wenliang Yuan, Xiaomin Zeng, Sihua Peng

引用次数: 0

Amino acid environment affinity model based on graph attention network. 基于图关注网络的氨基酸环境亲和模型。

IF 1 4区生物学

Journal of Bioinformatics and Computational Biology Pub Date : 2022-02-01 Epub Date: 2021-11-13 DOI: 10.1142/S0219720021500323

Xueheng Tong, Shuqi Liu, Jiawei Gu, Chunguo Wu, Yanchun Liang, Xiaohu Shi

{"title":"Amino acid environment affinity model based on graph attention network.","authors":"Xueheng Tong, Shuqi Liu, Jiawei Gu, Chunguo Wu, Yanchun Liang, Xiaohu Shi","doi":"10.1142/S0219720021500323","DOIUrl":"https://doi.org/10.1142/S0219720021500323","url":null,"abstract":"Proteins are engines involved in almost all functions of life. They have specific spatial structures formed by twisting and folding of one or more polypeptide chains composed of amino acids. Protein sites are protein structure microenvironments that can be identified by three-dimensional locations and local neighborhoods in which the structure or function exists. Understanding the amino acid environment affinity is essential for additional protein structural or functional studies, such as mutation analysis and functional site detection. In this study, an amino acid environment affinity model based on the graph attention network was developed. Initially, we constructed a protein graph according to the distance between amino acid pairs. Then, we extracted a set of structural features for each node. Finally, the protein graph and the associated node feature set were set to input the graph attention network model and to obtain the amino acid affinities. Numerical results show that our proposed method significantly outperforms a recent 3DCNN-based method by almost 30%.","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":"20 1","pages":"2150032"},"PeriodicalIF":1.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39875262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0