Pub Date : 2022-07-01DOI: 10.17706/ijbbb.2022.12.3.43-52
Shuhei Yao, Kaito Uemura, S. Seno, H. Matsuda
: Trajectory inference has been used to model cellular dynamic processes by using single-cell RNA sequence data. The inference often computes pseudotime representing the progression through the process along the trajectory. Several methods to infer gene regulatory networks have been proposed using the gene expression profiles of the cells ordered with the pseudotime to elucidate the regulatory relationships between genes in a dynamic process. In this paper, we propose a novel method for the inference of such gene regulatory networks. To predict highly accurate gene regulatory relationships in the network, we introduce an edge-scoring scheme with bootstrap sampling. We demonstrate the accuracy of the proposed methods by comparing the results with those of existing methods using synthetic and real single-cell RNA-seq data. a type in which the regulatory relationships of genes are connected in a long straight line [6]. Each network was generated with five different patterns of cell numbers: 100, 200, 500, 2000, and 5000. In each condition, 10 networks were generated and evaluated as described in [6]. For the actual data, we extracted the expression data of 10 genes from the network of transcription factors experimentally confirmed in the report of [11] from the expression data of cells in the differentiation lineage from pluripotent progenitor cells to monocytes as described above. 315 cells in which at least 3 of the 10 genes were expressed. The same time point was defined as the number truncated after the decimal point of the pseudotime. Using these data as input, network inference was performed using the DBN approach of SiGN-BN, and edge gain was calculated using the approach described above. We performed 100 bootstrap sampling for the synthetic data and 1000 bootstrap sampling for the real data. For each edge of the obtained network, the bootstrap probability, which means the probability that a regulated edge appears in the network inferred from multiple data sets generated by the bootstrap method, was calculated separately from the proposed method. The results obtained by the bootstrap probabilities and SINCERITIES were compared with the proposed method. The AUROC (area under the receiver operating characteristic curve) and AUPRC for each method were computed with the PRROC package [12], and all graphs were plotted in R. For the network visualization, Cytoscape was used.
{"title":"A Novel Method for Gene Regulatory Network Inference with Pseudotime Data Using Information Criterion","authors":"Shuhei Yao, Kaito Uemura, S. Seno, H. Matsuda","doi":"10.17706/ijbbb.2022.12.3.43-52","DOIUrl":"https://doi.org/10.17706/ijbbb.2022.12.3.43-52","url":null,"abstract":": Trajectory inference has been used to model cellular dynamic processes by using single-cell RNA sequence data. The inference often computes pseudotime representing the progression through the process along the trajectory. Several methods to infer gene regulatory networks have been proposed using the gene expression profiles of the cells ordered with the pseudotime to elucidate the regulatory relationships between genes in a dynamic process. In this paper, we propose a novel method for the inference of such gene regulatory networks. To predict highly accurate gene regulatory relationships in the network, we introduce an edge-scoring scheme with bootstrap sampling. We demonstrate the accuracy of the proposed methods by comparing the results with those of existing methods using synthetic and real single-cell RNA-seq data. a type in which the regulatory relationships of genes are connected in a long straight line [6]. Each network was generated with five different patterns of cell numbers: 100, 200, 500, 2000, and 5000. In each condition, 10 networks were generated and evaluated as described in [6]. For the actual data, we extracted the expression data of 10 genes from the network of transcription factors experimentally confirmed in the report of [11] from the expression data of cells in the differentiation lineage from pluripotent progenitor cells to monocytes as described above. 315 cells in which at least 3 of the 10 genes were expressed. The same time point was defined as the number truncated after the decimal point of the pseudotime. Using these data as input, network inference was performed using the DBN approach of SiGN-BN, and edge gain was calculated using the approach described above. We performed 100 bootstrap sampling for the synthetic data and 1000 bootstrap sampling for the real data. For each edge of the obtained network, the bootstrap probability, which means the probability that a regulated edge appears in the network inferred from multiple data sets generated by the bootstrap method, was calculated separately from the proposed method. The results obtained by the bootstrap probabilities and SINCERITIES were compared with the proposed method. The AUROC (area under the receiver operating characteristic curve) and AUPRC for each method were computed with the PRROC package [12], and all graphs were plotted in R. For the network visualization, Cytoscape was used.","PeriodicalId":13816,"journal":{"name":"International Journal of Bioscience, Biochemistry and Bioinformatics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84190571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-01DOI: 10.17706/ijbbb.2022.12.3.53-62
Olympia Giannou
: A model-driven approach suitable for classifying microbiome-related morbidities such as ulcerative colitis on smart mobile devices is investigated in this manuscript. A novel scheme is proposed, which consists of a pre-trained image classifier on ImageNet and is deployed into the presented Android mobile application for this purpose. Endoscopic images of mouse colitis were used as input datasets for our experiments. The proposed approach offers an efficient classifier, based on the average of all its performance metrics: confusion matrix, accuracy, recall, precision, cross entropy, f1-score. The results are compared with these of the most representative image classifiers for the kind of classification we target, in terms of performance, as well as the size of the retrained frozen graph on our dataset. Such a classification could serve as a valuable tool in clinical medicine offering an automated, diagnostic tool for microbiome-related morbidities, thus allowing accurate early diagnosis and the design of personalized and targeted therapeutic approaches.
{"title":"A Personalized Diagnostic Tool for Microbiome-Related Morbidities","authors":"Olympia Giannou","doi":"10.17706/ijbbb.2022.12.3.53-62","DOIUrl":"https://doi.org/10.17706/ijbbb.2022.12.3.53-62","url":null,"abstract":": A model-driven approach suitable for classifying microbiome-related morbidities such as ulcerative colitis on smart mobile devices is investigated in this manuscript. A novel scheme is proposed, which consists of a pre-trained image classifier on ImageNet and is deployed into the presented Android mobile application for this purpose. Endoscopic images of mouse colitis were used as input datasets for our experiments. The proposed approach offers an efficient classifier, based on the average of all its performance metrics: confusion matrix, accuracy, recall, precision, cross entropy, f1-score. The results are compared with these of the most representative image classifiers for the kind of classification we target, in terms of performance, as well as the size of the retrained frozen graph on our dataset. Such a classification could serve as a valuable tool in clinical medicine offering an automated, diagnostic tool for microbiome-related morbidities, thus allowing accurate early diagnosis and the design of personalized and targeted therapeutic approaches.","PeriodicalId":13816,"journal":{"name":"International Journal of Bioscience, Biochemistry and Bioinformatics","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82922038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.33545/26646536.2022.v4.i1a.31
Darge Lulu, Dr. Mohammed Hussen
{"title":"Review on microbial genetics and virulence factors of mycobacterium bovis","authors":"Darge Lulu, Dr. Mohammed Hussen","doi":"10.33545/26646536.2022.v4.i1a.31","DOIUrl":"https://doi.org/10.33545/26646536.2022.v4.i1a.31","url":null,"abstract":"","PeriodicalId":13816,"journal":{"name":"International Journal of Bioscience, Biochemistry and Bioinformatics","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83167826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.17706/ijbbb.2022.12.4.71-84
A. Huang
{"title":"Repurposing of Known Drugs as Potential Therapeutics for Cancer Immunotherapy for Patients with Solid Tumors: Modeling Small Molecule Interactions with PD-1 Binding Sites","authors":"A. Huang","doi":"10.17706/ijbbb.2022.12.4.71-84","DOIUrl":"https://doi.org/10.17706/ijbbb.2022.12.4.71-84","url":null,"abstract":"","PeriodicalId":13816,"journal":{"name":"International Journal of Bioscience, Biochemistry and Bioinformatics","volume":"67 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77142656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.17706/ijbbb.2022.12.4.63-70
A. Nomura, H. Matsuda
{"title":"Identification of Lineage Markers for T Cell Immune Dysregulation in Sarcoidosis Using Single-Cell RNA-seq","authors":"A. Nomura, H. Matsuda","doi":"10.17706/ijbbb.2022.12.4.63-70","DOIUrl":"https://doi.org/10.17706/ijbbb.2022.12.4.63-70","url":null,"abstract":"","PeriodicalId":13816,"journal":{"name":"International Journal of Bioscience, Biochemistry and Bioinformatics","volume":"231 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75569008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.17706/ijbbb.2022.12.4.85-92
Yifei Wang
{"title":"Pirin, a Multifunction Protein with Quercetinase Activity and Involvement in Transcription Regulation","authors":"Yifei Wang","doi":"10.17706/ijbbb.2022.12.4.85-92","DOIUrl":"https://doi.org/10.17706/ijbbb.2022.12.4.85-92","url":null,"abstract":"","PeriodicalId":13816,"journal":{"name":"International Journal of Bioscience, Biochemistry and Bioinformatics","volume":"81 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84307772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.17706/ijbbb.2022.12.2.30-38
Jiahuan He
: Next-generation sequencing (NGS), with Illumina sequencing being the most well-known and most widely used NGS platform, is a high-throughput DNA (or RNA) sequencing technology that allows massive parallel sequencing. There are numerous applications of NGS, with some of them being related to the pandemics or epidemics caused by viral infection, and an example of that is the coronavirus disease 2019 (COVID-19), which is a pandemic that began in December 2019 caused by a novel coronavirus—the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This review then focuses on the discussion of how NGS technology has been applied to the study of the COVID-19 pandemic. Specifically, the review describes the applications of NGS in whole-genome sequencing of SARS-CoV-2, identification of novel COVID-19 viral mutations, tracking the variant viral lineages, and providing insights into the origin and transmission pattern of the current pandemic.
{"title":"Next-Generation Sequencing on COVID-19 Pandemic","authors":"Jiahuan He","doi":"10.17706/ijbbb.2022.12.2.30-38","DOIUrl":"https://doi.org/10.17706/ijbbb.2022.12.2.30-38","url":null,"abstract":": Next-generation sequencing (NGS), with Illumina sequencing being the most well-known and most widely used NGS platform, is a high-throughput DNA (or RNA) sequencing technology that allows massive parallel sequencing. There are numerous applications of NGS, with some of them being related to the pandemics or epidemics caused by viral infection, and an example of that is the coronavirus disease 2019 (COVID-19), which is a pandemic that began in December 2019 caused by a novel coronavirus—the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This review then focuses on the discussion of how NGS technology has been applied to the study of the COVID-19 pandemic. Specifically, the review describes the applications of NGS in whole-genome sequencing of SARS-CoV-2, identification of novel COVID-19 viral mutations, tracking the variant viral lineages, and providing insights into the origin and transmission pattern of the current pandemic.","PeriodicalId":13816,"journal":{"name":"International Journal of Bioscience, Biochemistry and Bioinformatics","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83863706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.17706/ijbbb.2022.12.2.22-29
Ayat Fayez, S. Hammad, D. Ghareeb, M. A. Demellawy, Ahmed M Osman
: Colorectal cancer (CRC) ranked the third and the second in the onset and mortality rates, respectively, among all types of cancer (GLOBOCAN 2020). In this study, we targeted epidermal growth factor receptor (EGFR) using two synthesized thiazolyl pyrazoline analogs in presence and absence of berberine as an adjuvant remedy. Preliminary results showed that these compounds exert anti-neoplastic and tyrosine kinase inhibitory effects against lung cancer cell lines. We assessed the anticancer activities of these compounds via quantitative determination of transcripts levels (qRT-PCR) of some apoptotic and proliferative markers in Caco-2 cell line. Our data showed a synergistic augmented effect between the tested compounds and berberine on the pro-apoptotic markers (BAX and p53). Furthermore, the oncogenes EGFR and c-MYC, as well as the anti-apoptotic gene ARC, exhibited downregulation trend. In conclusion, the novel compounds show promising results as potential TKI that inhibit malignant cell growth and restore cancer cells' apoptotic capacity. strategy to hinder the signaling pathways associated with EGFR by binding to its ATP binding sites. We aimed to investigate the potential of novel thiazolyl pyrazolines to inhibit EGFR expression and apoptosis induction when added with the natural isoquinoline berberine. Our results showed a good synergism where P53 was upregulated with its downstream transcription targets BAX and Fas, indicating apoptosis activation. Furthermore, EGFR and TGF-b were downregulated post-treatment suggesting a decline in cell proliferation and migration. Overall, we recommend combining berberine and thiazolyl pyrazoline derivatives to enhance colorectal cancer treatment.
{"title":"Novel Thiazolyl-Pyrazoline Analogs: Potential Role for Tyrosine Kinase Inhibition in Colorectal Cancer","authors":"Ayat Fayez, S. Hammad, D. Ghareeb, M. A. Demellawy, Ahmed M Osman","doi":"10.17706/ijbbb.2022.12.2.22-29","DOIUrl":"https://doi.org/10.17706/ijbbb.2022.12.2.22-29","url":null,"abstract":": Colorectal cancer (CRC) ranked the third and the second in the onset and mortality rates, respectively, among all types of cancer (GLOBOCAN 2020). In this study, we targeted epidermal growth factor receptor (EGFR) using two synthesized thiazolyl pyrazoline analogs in presence and absence of berberine as an adjuvant remedy. Preliminary results showed that these compounds exert anti-neoplastic and tyrosine kinase inhibitory effects against lung cancer cell lines. We assessed the anticancer activities of these compounds via quantitative determination of transcripts levels (qRT-PCR) of some apoptotic and proliferative markers in Caco-2 cell line. Our data showed a synergistic augmented effect between the tested compounds and berberine on the pro-apoptotic markers (BAX and p53). Furthermore, the oncogenes EGFR and c-MYC, as well as the anti-apoptotic gene ARC, exhibited downregulation trend. In conclusion, the novel compounds show promising results as potential TKI that inhibit malignant cell growth and restore cancer cells' apoptotic capacity. strategy to hinder the signaling pathways associated with EGFR by binding to its ATP binding sites. We aimed to investigate the potential of novel thiazolyl pyrazolines to inhibit EGFR expression and apoptosis induction when added with the natural isoquinoline berberine. Our results showed a good synergism where P53 was upregulated with its downstream transcription targets BAX and Fas, indicating apoptosis activation. Furthermore, EGFR and TGF-b were downregulated post-treatment suggesting a decline in cell proliferation and migration. Overall, we recommend combining berberine and thiazolyl pyrazoline derivatives to enhance colorectal cancer treatment.","PeriodicalId":13816,"journal":{"name":"International Journal of Bioscience, Biochemistry and Bioinformatics","volume":"141 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80072035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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