Pub Date : 2018-03-13DOI: 10.2174/1875036201811010029
Selvaa Kumar, D. Dasgupta, Nikhil Gadewal
RESEARCH ARTICLE Bioinformatics Based Understanding of Effect of Mutations in the Human β Tubulin Outside Drug Binding Sites and its Significance in Drug Resistance Selvaa Kumar C, Debjani Dasgupta and Nikhil Gadewal School of Biotechnology and Bioinformatics, DY Patil University, CBD Belapur, Navi Mumbai 400614, India Advanced Centre for Treatment, Research and Education in Cancer, Kharghar, Navi Mumbai 410210, India
{"title":"Bioinformatics Based Understanding of Effect of Mutations in the Human β Tubulin Outside Drug Binding Sites and its Significance in Drug Resistance","authors":"Selvaa Kumar, D. Dasgupta, Nikhil Gadewal","doi":"10.2174/1875036201811010029","DOIUrl":"https://doi.org/10.2174/1875036201811010029","url":null,"abstract":"RESEARCH ARTICLE Bioinformatics Based Understanding of Effect of Mutations in the Human β Tubulin Outside Drug Binding Sites and its Significance in Drug Resistance Selvaa Kumar C, Debjani Dasgupta and Nikhil Gadewal School of Biotechnology and Bioinformatics, DY Patil University, CBD Belapur, Navi Mumbai 400614, India Advanced Centre for Treatment, Research and Education in Cancer, Kharghar, Navi Mumbai 410210, India","PeriodicalId":38956,"journal":{"name":"Open Bioinformatics Journal","volume":"11 1","pages":"29-37"},"PeriodicalIF":0.0,"publicationDate":"2018-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43040487","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 : 2018-03-07DOI: 10.2174/1875036201811010012
Nikwan Shariatipour, B. Heidari
Results: Under drought stress, 2558 gene accessions in root and 3691 in shoot tissues had significantly differential expression with respect to control condition. Likewise, under salinity stress 9078 gene accessions in root and 5785 in shoot tissues were discriminated between stressed and non-stressed conditions. Furthermore, the transcription regulatory activity of differentially expressed genes was mainly due to hormone, light, circadian and stress responsive cis-acting regulatory elements among which ABRE, ERE, P-box, TATC-box, CGTCA-motif, GARE-motif, TGACG-motif, GAG-motif, GA-motif, GATAmotif, TCT-motif, GT1-motif, Box 4, G-Box, I-box, LAMP-element, Sp1, MBS, TC-rich repeats, TCA-element and HSE were the most important elements in the identified up-regulated genes.
{"title":"Investigation of Drought and Salinity Tolerance Related Genes and their Regulatory Mechanisms in Arabidopsis (Arabidopsis thaliana)","authors":"Nikwan Shariatipour, B. Heidari","doi":"10.2174/1875036201811010012","DOIUrl":"https://doi.org/10.2174/1875036201811010012","url":null,"abstract":"Results: Under drought stress, 2558 gene accessions in root and 3691 in shoot tissues had significantly differential expression with respect to control condition. Likewise, under salinity stress 9078 gene accessions in root and 5785 in shoot tissues were discriminated between stressed and non-stressed conditions. Furthermore, the transcription regulatory activity of differentially expressed genes was mainly due to hormone, light, circadian and stress responsive cis-acting regulatory elements among which ABRE, ERE, P-box, TATC-box, CGTCA-motif, GARE-motif, TGACG-motif, GAG-motif, GA-motif, GATAmotif, TCT-motif, GT1-motif, Box 4, G-Box, I-box, LAMP-element, Sp1, MBS, TC-rich repeats, TCA-element and HSE were the most important elements in the identified up-regulated genes.","PeriodicalId":38956,"journal":{"name":"Open Bioinformatics Journal","volume":"11 1","pages":"12-28"},"PeriodicalIF":0.0,"publicationDate":"2018-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45041774","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 : 2018-02-28DOI: 10.2174/1875036201811010001
Hossein Mahboudi, N. Heidari, Zahra Irani Rashidabadi, Ali Houshmand Anbarestani, Soroush Karimi, K. Darestani
RESEARCH ARTICLE Prospect and Competence of Quantitative Methods via Real-time PCR in a Comparative Manner: An Experimental Review of Current Methods Hossein Mahboudi, Negin Mohammadizadeh Heidari, Zahra Irani Rashidabadi, Ali Houshmand Anbarestani, Soroush Karimi and Kaveh Darabi Darestani Department of medical biotechnology, School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran Department of Agronomy and Plant breeding, Agricultural Faculty, Zanjan University, Zanjan, Iran Department of Genetics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran Nano Drug Delivery Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran Biology Department, School of advanced sciences regenerative medicine, Tehran Medical Branch Islamic Azad University, Tehran, Iran
{"title":"Prospect and Competence of Quantitative Methods via Real-time PCR in a Comparative Manner: An Experimental Review of Current Methods","authors":"Hossein Mahboudi, N. Heidari, Zahra Irani Rashidabadi, Ali Houshmand Anbarestani, Soroush Karimi, K. Darestani","doi":"10.2174/1875036201811010001","DOIUrl":"https://doi.org/10.2174/1875036201811010001","url":null,"abstract":"RESEARCH ARTICLE Prospect and Competence of Quantitative Methods via Real-time PCR in a Comparative Manner: An Experimental Review of Current Methods Hossein Mahboudi, Negin Mohammadizadeh Heidari, Zahra Irani Rashidabadi, Ali Houshmand Anbarestani, Soroush Karimi and Kaveh Darabi Darestani Department of medical biotechnology, School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran Department of Agronomy and Plant breeding, Agricultural Faculty, Zanjan University, Zanjan, Iran Department of Genetics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran Nano Drug Delivery Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran Biology Department, School of advanced sciences regenerative medicine, Tehran Medical Branch Islamic Azad University, Tehran, Iran","PeriodicalId":38956,"journal":{"name":"Open Bioinformatics Journal","volume":"11 1","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2018-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48757328","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 : 2017-12-12DOI: 10.2174/1875036201710010016
Ebenezer S. Owusu Adjah, O. Montvida, Julius Agbeve, S. Paul
Background: Identification of diseased patients from primary care based electronic medical records (EMRs) has methodological challenges that may impact epidemiologic inferences. Objective: To compare deterministic clinically guided selection algorithms with probabilistic machine learning (ML) methodologies for their ability to identify patients with type 2 diabetes mellitus (T2DM) from large population based EMRs from nationally representative primary care database. Methods: Four cohorts of patients with T2DM were defined by deterministic approach based on disease codes. The database was mined for a set of best predictors of T2DM and the performance of six ML algorithms were compared based on cross-validated true positive rate, true negative rate, and area under receiver operating characteristic curve. Results: In the database of 11,018,025 research suitable individuals, 379 657 (3.4%) were coded to have T2DM. Logistic Regression classifier was selected as best ML algorithm and resulted in a cohort of 383,330 patients with potential T2DM. Eighty-three percent (83%) of this cohort had a T2DM code, and 16% of the patients with T2DM code were not included in this ML cohort. Of those in the ML cohort without disease code, 52% had at least one measure of elevated glucose level and 22% had received at least one prescription for antidiabetic medication. Conclusion: Deterministic cohort selection based on disease coding potentially introduces significant mis-classification problem. ML techniques allow testing for potential disease predictors, and under meaningful data input, are able to identify diseased cohorts in a holistic way.
{"title":"Data Mining Approach to Identify Disease Cohorts from Primary Care Electronic Medical Records: A Case of Diabetes Mellitus","authors":"Ebenezer S. Owusu Adjah, O. Montvida, Julius Agbeve, S. Paul","doi":"10.2174/1875036201710010016","DOIUrl":"https://doi.org/10.2174/1875036201710010016","url":null,"abstract":"Background: Identification of diseased patients from primary care based electronic medical records (EMRs) has methodological challenges that may impact epidemiologic inferences. Objective: To compare deterministic clinically guided selection algorithms with probabilistic machine learning (ML) methodologies for their ability to identify patients with type 2 diabetes mellitus (T2DM) from large population based EMRs from nationally representative primary care database. Methods: Four cohorts of patients with T2DM were defined by deterministic approach based on disease codes. The database was mined for a set of best predictors of T2DM and the performance of six ML algorithms were compared based on cross-validated true positive rate, true negative rate, and area under receiver operating characteristic curve. Results: In the database of 11,018,025 research suitable individuals, 379 657 (3.4%) were coded to have T2DM. Logistic Regression classifier was selected as best ML algorithm and resulted in a cohort of 383,330 patients with potential T2DM. Eighty-three percent (83%) of this cohort had a T2DM code, and 16% of the patients with T2DM code were not included in this ML cohort. Of those in the ML cohort without disease code, 52% had at least one measure of elevated glucose level and 22% had received at least one prescription for antidiabetic medication. Conclusion: Deterministic cohort selection based on disease coding potentially introduces significant mis-classification problem. ML techniques allow testing for potential disease predictors, and under meaningful data input, are able to identify diseased cohorts in a holistic way.","PeriodicalId":38956,"journal":{"name":"Open Bioinformatics Journal","volume":"10 1","pages":"16-27"},"PeriodicalIF":0.0,"publicationDate":"2017-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46745948","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 : 2017-07-31DOI: 10.2174/1875036201709010001
O. Montvida, Ognjen Arandjelovic, E. Reiner, S. Paul
Electronic Medical Records (EMRs) from primary/ ambulatory care systems present a new and promising source of information for conducting clinical and translational research. To address the methodological and computational challenges in order to extract reliable medication information from raw data which is often complex, incomplete and erroneous. To assess whether the use of specific chaining fields of medication information may additionally improve the data quality. Guided by a range of challenges associated with missing and internally inconsistent data, we introduce two methods for the robust extraction of patient-level medication data. First method relies on chaining fields to estimate duration of treatment (“chaining”), while second disregards chaining fields and relies on the chronology of records (“continuous”). Centricity EMR database was used to estimate treatment duration with both methods for two widely prescribed drugs among type 2 diabetes patients: insulin and glucagon-like peptide-1 receptor agonists. At individual patient level the “chaining” approach could identify the treatment alterations longitudinally and produced more robust estimates of treatment duration for individual drugs, while the “continuous” method was unable to capture that dynamics. At population level, both methods produced similar estimates of average treatment duration, however, notable differences were observed at individual-patient level. The proposed algorithms explicitly identify and handle longitudinal erroneous or missing entries and estimate treatment duration with specific drug(s) of interest, which makes them a valuable tool for future EMR based clinical and pharmaco-epidemiological studies. To improve accuracy of real-world based studies, implementing chaining fields of medication information is recommended.
{"title":"Data Mining Approach to Estimate the Duration of Drug Therapy from Longitudinal Electronic Medical Records","authors":"O. Montvida, Ognjen Arandjelovic, E. Reiner, S. Paul","doi":"10.2174/1875036201709010001","DOIUrl":"https://doi.org/10.2174/1875036201709010001","url":null,"abstract":"\u0000 \u0000 Electronic Medical Records (EMRs) from primary/ ambulatory care systems present a new and promising source of information for conducting clinical and translational research.\u0000 \u0000 \u0000 \u0000 To address the methodological and computational challenges in order to extract reliable medication information from raw data which is often complex, incomplete and erroneous. To assess whether the use of specific chaining fields of medication information may additionally improve the data quality.\u0000 \u0000 \u0000 \u0000 Guided by a range of challenges associated with missing and internally inconsistent data, we introduce two methods for the robust extraction of patient-level medication data. First method relies on chaining fields to estimate duration of treatment (“chaining”), while second disregards chaining fields and relies on the chronology of records (“continuous”). Centricity EMR database was used to estimate treatment duration with both methods for two widely prescribed drugs among type 2 diabetes patients: insulin and glucagon-like peptide-1 receptor agonists.\u0000 \u0000 \u0000 \u0000 At individual patient level the “chaining” approach could identify the treatment alterations longitudinally and produced more robust estimates of treatment duration for individual drugs, while the “continuous” method was unable to capture that dynamics. At population level, both methods produced similar estimates of average treatment duration, however, notable differences were observed at individual-patient level.\u0000 \u0000 \u0000 \u0000 The proposed algorithms explicitly identify and handle longitudinal erroneous or missing entries and estimate treatment duration with specific drug(s) of interest, which makes them a valuable tool for future EMR based clinical and pharmaco-epidemiological studies. To improve accuracy of real-world based studies, implementing chaining fields of medication information is recommended.\u0000","PeriodicalId":38956,"journal":{"name":"Open Bioinformatics Journal","volume":"10 1","pages":"1-15"},"PeriodicalIF":0.0,"publicationDate":"2017-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49153056","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 : 2015-03-31DOI: 10.2174/1875036201509010013
M. Zare, H. Mohabatkar, Fatemeh Faramarzi, Majid Mohammad Beigi, M. Behbahani
Traditional antiviral therapies are expensive, limitedly available, and cause several side effects. Currently, de- signing antiviral peptides is very important, because these peptides interfere with the key stage of virus life cycle. Most of the antiviral peptides are derived from viral proteins for example peptide derived from HIV-1 capsid protein. Because of the importance of these peptides, in this study the concept of pseudo-amino acid composition (PseAAC) and machine learning methods are used to classify or identify antiviral peptides.
{"title":"Using Chou’s Pseudo Amino Acid Composition and Machine LearningMethod to Predict the Antiviral Peptides","authors":"M. Zare, H. Mohabatkar, Fatemeh Faramarzi, Majid Mohammad Beigi, M. Behbahani","doi":"10.2174/1875036201509010013","DOIUrl":"https://doi.org/10.2174/1875036201509010013","url":null,"abstract":"Traditional antiviral therapies are expensive, limitedly available, and cause several side effects. Currently, de- signing antiviral peptides is very important, because these peptides interfere with the key stage of virus life cycle. Most of the antiviral peptides are derived from viral proteins for example peptide derived from HIV-1 capsid protein. Because of the importance of these peptides, in this study the concept of pseudo-amino acid composition (PseAAC) and machine learning methods are used to classify or identify antiviral peptides.","PeriodicalId":38956,"journal":{"name":"Open Bioinformatics Journal","volume":"9 1","pages":"13-19"},"PeriodicalIF":0.0,"publicationDate":"2015-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68107581","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 : 2015-01-23DOI: 10.2174/1875036201509010001
S. Mahmoudian, Abdulaziz Yousef, Nasrollah Moghadam Charkari
Protein-Protein Interactions (PPIs) play a key role in many biological systems. Thus, identifying PPIs is critical for understanding cellular processes. Many experimental techniques were applied to predict PPIs. The data extracted using these techniques are incomplete and noisy. In this regard, a number of computational methods include machine learning classification techniques have been developed to reduce the noise data and predict new PPIs. Since, using regression methods to solve classification problems has good results in other applications. Therefore, in this paper, a regression view is applied to the PPI prediction classification problem, so a new approach is proposed using Principal Component Analysis (PCA) and Support Vector Regression (SVR) which has been improved by a new Parallel Hierarchical Cube Search (PHCS) method. Firstly, PCA algorithm is implemented to select an optimal subset of features which leads to reduce processing time and to lessen the effect of noise. Then, the PPIs would be predicted, by using SVR. To get a better performance of SVR, a new PHCS method has been applied to select the appropriate values of SVR parameters. The obtained classification accuracy of the proposed method is 74.505% on KUPS (The University of Kansas Proteomics Service) dataset which outperforms the other methods.
蛋白质-蛋白质相互作用(PPIs)在许多生物系统中起着关键作用。因此，识别ppi对于理解细胞过程至关重要。许多实验技术被应用于预测ppi。使用这些技术提取的数据是不完整和有噪声的。在这方面，已经开发了许多计算方法，包括机器学习分类技术，以减少噪声数据并预测新的ppi。因此，使用回归方法来解决分类问题在其他应用中也有很好的效果。为此，本文将回归的观点应用于PPI预测分类问题，提出了一种基于主成分分析(PCA)和支持向量回归(SVR)的PPI预测分类方法，并在此基础上改进了一种新的并行分层立方搜索(PHCS)方法。首先，采用主成分分析算法选择最优的特征子集，减少处理时间和噪声的影响;然后，利用SVR对ppi进行预测。为了获得更好的SVR性能，采用一种新的PHCS方法来选择合适的SVR参数值。在美国堪萨斯大学蛋白质组学服务(University of Kansas Proteomics Service)数据集上，该方法的分类准确率为74.505%，优于其他方法。
{"title":"Protein-Protein Interaction Prediction using PCA and SVR-PHCS","authors":"S. Mahmoudian, Abdulaziz Yousef, Nasrollah Moghadam Charkari","doi":"10.2174/1875036201509010001","DOIUrl":"https://doi.org/10.2174/1875036201509010001","url":null,"abstract":"Protein-Protein Interactions (PPIs) play a key role in many biological systems. Thus, identifying PPIs is critical for understanding cellular processes. Many experimental techniques were applied to predict PPIs. The data extracted using these techniques are incomplete and noisy. In this regard, a number of computational methods include machine learning classification techniques have been developed to reduce the noise data and predict new PPIs. Since, using regression methods to solve classification problems has good results in other applications. Therefore, in this paper, a regression view is applied to the PPI prediction classification problem, so a new approach is proposed using Principal Component Analysis (PCA) and Support Vector Regression (SVR) which has been improved by a new Parallel Hierarchical Cube Search (PHCS) method. Firstly, PCA algorithm is implemented to select an optimal subset of features which leads to reduce processing time and to lessen the effect of noise. Then, the PPIs would be predicted, by using SVR. To get a better performance of SVR, a new PHCS method has been applied to select the appropriate values of SVR parameters. The obtained classification accuracy of the proposed method is 74.505% on KUPS (The University of Kansas Proteomics Service) dataset which outperforms the other methods.","PeriodicalId":38956,"journal":{"name":"Open Bioinformatics Journal","volume":"41 1","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2015-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68107512","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 : 2015-01-13DOI: 10.2174/1875036201408010016
S. Lawal, M. Korenberg, Natalia M. Pittman, M. Mates
A previous study (Pittman, Hopman, Mates) of breast cancer patients undergoing curative chemotherapy (CT) found that the third most common reason for emergency department (ER) visits and hospital admission (HA) was febrile neutropenia. Factors associated with ER visits and HA included (1) stage of the cancer, (2) size of tumor, (3) adjuvant versus neo-adjuvant CT ("adjuvance"), and (4) number of CT cycles. We hypothesized that a statistically-significant pre- dictor of neutropenia could be built based on some of these factors, so that risk of neutropenia predicted for a patient feel- ing unwell during CT could be used in weighing need to visit the ER. The number of CT cycles was not used as a factor so that the predictor could calculate the neutropenia risk for a patient before the first CT cycle. Different models were built corresponding to different pre-chemotherapy factors or combinations of factors. The single factor yielding the best classification accuracy was tumor size (Mathews' correlation coefficient � = +0.18, Fisher's exact two-tailed probability P < 0.0374). The odds ratio of developing febrile neutropenia for the predicted high-risk group compared to the predicted low-risk group was 5.1875. Combining tumor size with adjuvance yielded a slightly more accurate predictor (Mathews' correlation coefficient � = +0.19, Fisher's exact two-tailed probability P < 0.0331, odds ratio = 5.5093). Based on the ob- served odds ratios, we conclude that a simple predictor of neutropenia may have value in deciding whether to recommend an ER visit. The predictor is sufficiently fast that it can run conveniently as an Applet on a mobile computing device.
{"title":"Predicting Neutropenia Risk in Breast Cancer Patients from Pre- Chemotherapy Characteristics","authors":"S. Lawal, M. Korenberg, Natalia M. Pittman, M. Mates","doi":"10.2174/1875036201408010016","DOIUrl":"https://doi.org/10.2174/1875036201408010016","url":null,"abstract":"A previous study (Pittman, Hopman, Mates) of breast cancer patients undergoing curative chemotherapy (CT) found that the third most common reason for emergency department (ER) visits and hospital admission (HA) was febrile neutropenia. Factors associated with ER visits and HA included (1) stage of the cancer, (2) size of tumor, (3) adjuvant versus neo-adjuvant CT (\"adjuvance\"), and (4) number of CT cycles. We hypothesized that a statistically-significant pre- dictor of neutropenia could be built based on some of these factors, so that risk of neutropenia predicted for a patient feel- ing unwell during CT could be used in weighing need to visit the ER. The number of CT cycles was not used as a factor so that the predictor could calculate the neutropenia risk for a patient before the first CT cycle. Different models were built corresponding to different pre-chemotherapy factors or combinations of factors. The single factor yielding the best classification accuracy was tumor size (Mathews' correlation coefficient � = +0.18, Fisher's exact two-tailed probability P < 0.0374). The odds ratio of developing febrile neutropenia for the predicted high-risk group compared to the predicted low-risk group was 5.1875. Combining tumor size with adjuvance yielded a slightly more accurate predictor (Mathews' correlation coefficient � = +0.19, Fisher's exact two-tailed probability P < 0.0331, odds ratio = 5.5093). Based on the ob- served odds ratios, we conclude that a simple predictor of neutropenia may have value in deciding whether to recommend an ER visit. The predictor is sufficiently fast that it can run conveniently as an Applet on a mobile computing device.","PeriodicalId":38956,"journal":{"name":"Open Bioinformatics Journal","volume":"29 1","pages":"16-21"},"PeriodicalIF":0.0,"publicationDate":"2015-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68107500","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 : 2014-12-31DOI: 10.2174/1875036201408010006
Ashutosh Shukla, S. Paul
Hsp90 is a stress protein that acts as a molecular chaperone and is known to assist in the maturation, folding and stabilization of various cellular proteins known as ‘client proteins’. However, the factors that drive the interaction between Hsp90 and its client proteins are not well understood. In the present investigation, we predicted the basis of the different interaction of Hsp90 with both wild and mutant p53 and other client proteins. We have predicted that the presence of hydrophobic patches having substantial value of hydropathy index and a minimum percent similarity of hydrophobic patches between Hsp90 and its client proteins of 40 % is a necessary condition for client proteins to be recognized by Hsp90 . We also predicted that the overall percentage hydrophobicity of client proteins more than 20 is a required condition for them to bind with Hsp90 . The docking energy of p53 with Hsp90 and with multi-chaperone complex was also separately reported. We have reported from docking result that mutant p53 has a stronger interaction with Hsp90 when associated with multi-chaperone complex than wild type p53 and this might be one of the causes of breast cancer pathogenesis.
{"title":"Identification of the Factors Responsible for the Interaction of Hsp90α and its Client Proteins","authors":"Ashutosh Shukla, S. Paul","doi":"10.2174/1875036201408010006","DOIUrl":"https://doi.org/10.2174/1875036201408010006","url":null,"abstract":"Hsp90 is a stress protein that acts as a molecular chaperone and is known to assist in the maturation, folding and stabilization of various cellular proteins known as ‘client proteins’. However, the factors that drive the interaction between Hsp90 and its client proteins are not well understood. In the present investigation, we predicted the basis of the different interaction of Hsp90 with both wild and mutant p53 and other client proteins. We have predicted that the presence of hydrophobic patches having substantial value of hydropathy index and a minimum percent similarity of hydrophobic patches between Hsp90 and its client proteins of 40 % is a necessary condition for client proteins to be recognized by Hsp90 . We also predicted that the overall percentage hydrophobicity of client proteins more than 20 is a required condition for them to bind with Hsp90 . The docking energy of p53 with Hsp90 and with multi-chaperone complex was also separately reported. We have reported from docking result that mutant p53 has a stronger interaction with Hsp90 when associated with multi-chaperone complex than wild type p53 and this might be one of the causes of breast cancer pathogenesis.","PeriodicalId":38956,"journal":{"name":"Open Bioinformatics Journal","volume":"8 1","pages":"6-15"},"PeriodicalIF":0.0,"publicationDate":"2014-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68107456","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 : 2014-12-31DOI: 10.2174/1875036201408010001
T. Biagini, B. Bartolini, E. Giombini, M. Capobianchi, F. Ferrè, G. Chillemi, A. Desideri
Diagnostic assays for pathogen detection are critical components of public-health monitoring efforts. In view of the limitations of methods that target specific agents, new approaches are required for the identification of novel, modi- fied or 'unsuspected' pathogens in public-health monitoring schemes. Metagenomic approach is an attractive possibility for rapid identification of these pathogens. The analysis of metagenomic libraries requires fast computation and appropri- ate algorithms to characterize sequences. In this paper, we compared the computational efficiency of different bioinfor- matic pipelines ad hoc established, based on de novo assembly of pathogen genomes, using a data set generated with a 454 genome sequencer from respiratory samples of patients with diagnosis of 2009 pandemic influenza A (H1N1). The results indicate high computational efficiency of the different bioinformatic pipelines, reducing the number of alignments respect to the identification based on the alignment of individual reads. The resulting computational time, added to the processing/sequencing time, is well compatible with diagnostic needs. The pipelines here described are useful in the unbi- ased analysis of clinical samples from patients with infectious diseases that may be relevant not only for the rapid identifi- cation but also for the extensive genetic characterization of viral pathogens without the need of culture amplification.
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