Open Bioinformatics Journal最新文献_第7页

Bioinformatics Based Understanding of Effect of Mutations in the Human β Tubulin Outside Drug Binding Sites and its Significance in Drug Resistance 基于生物信息学的人β微管蛋白药物结合位点外突变效应及其在耐药性中的意义

Open Bioinformatics Journal Pub Date : 2018-03-13 DOI: 10.2174/1875036201811010029

Selvaa Kumar, D. Dasgupta, Nikhil Gadewal

引用次数: 0

Investigation of Drought and Salinity Tolerance Related Genes and their Regulatory Mechanisms in Arabidopsis (Arabidopsis thaliana) 拟南芥耐干旱耐盐碱相关基因及其调控机制的研究

Open Bioinformatics Journal Pub Date : 2018-03-07 DOI: 10.2174/1875036201811010012

Nikwan Shariatipour, B. Heidari

引用次数: 16

Prospect and Competence of Quantitative Methods via Real-time PCR in a Comparative Manner: An Experimental Review of Current Methods 实时PCR定量方法的比较前景和能力：对现有方法的实验综述

Open Bioinformatics Journal Pub Date : 2018-02-28 DOI: 10.2174/1875036201811010001

Hossein Mahboudi, N. Heidari, Zahra Irani Rashidabadi, Ali Houshmand Anbarestani, Soroush Karimi, K. Darestani

引用次数: 4

Data Mining Approach to Identify Disease Cohorts from Primary Care Electronic Medical Records: A Case of Diabetes Mellitus 从初级保健电子病历中识别疾病队列的数据挖掘方法：一例糖尿病

Open Bioinformatics Journal Pub Date : 2017-12-12 DOI: 10.2174/1875036201710010016

Ebenezer S. Owusu Adjah, O. Montvida, Julius Agbeve, S. Paul

{"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}

引用次数: 22

Data Mining Approach to Estimate the Duration of Drug Therapy from Longitudinal Electronic Medical Records 从纵向电子病历估计药物治疗持续时间的数据挖掘方法

Open Bioinformatics Journal Pub Date : 2017-07-31 DOI: 10.2174/1875036201709010001

O. Montvida, Ognjen Arandjelovic, E. Reiner, S. Paul

{"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}

引用次数: 13

Using Chou’s Pseudo Amino Acid Composition and Machine LearningMethod to Predict the Antiviral Peptides 利用Chou的伪氨基酸组成和机器学习方法预测抗病毒肽

Open Bioinformatics Journal Pub Date : 2015-03-31 DOI: 10.2174/1875036201509010013

M. Zare, H. Mohabatkar, Fatemeh Faramarzi, Majid Mohammad Beigi, M. Behbahani

引用次数: 21

Protein-Protein Interaction Prediction using PCA and SVR-PHCS 基于PCA和SVR-PHCS的蛋白质相互作用预测

Open Bioinformatics Journal Pub Date : 2015-01-23 DOI: 10.2174/1875036201509010001

S. Mahmoudian, Abdulaziz Yousef, Nasrollah Moghadam Charkari

{"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}

引用次数: 0

Predicting Neutropenia Risk in Breast Cancer Patients from Pre- Chemotherapy Characteristics 从化疗前特征预测乳腺癌患者中性粒细胞减少的风险

Open Bioinformatics Journal Pub Date : 2015-01-13 DOI: 10.2174/1875036201408010016

S. Lawal, M. Korenberg, Natalia M. Pittman, M. Mates

{"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}

引用次数: 0

Identification of the Factors Responsible for the Interaction of Hsp90α and its Client Proteins Hsp90α与其客户蛋白相互作用的相关因子鉴定

Open Bioinformatics Journal Pub Date : 2014-12-31 DOI: 10.2174/1875036201408010006

Ashutosh Shukla, S. Paul

{"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}

引用次数: 0

Performances of Bioinformatics Pipelines for the Identification of Pathogensin Clinical Samples with the De Novo Assembly Approaches: Focuson 2009 Pandemic Influenza A (H1N1) 基于De Novo组装方法的生物信息学管道在临床样本病原体鉴定中的应用——以2009年甲型H1N1流感为例

Open Bioinformatics Journal Pub Date : 2014-12-31 DOI: 10.2174/1875036201408010001

T. Biagini, B. Bartolini, E. Giombini, M. Capobianchi, F. Ferrè, G. Chillemi, A. Desideri

{"title":"Performances of Bioinformatics Pipelines for the Identification of Pathogensin Clinical Samples with the De Novo Assembly Approaches: Focuson 2009 Pandemic Influenza A (H1N1)","authors":"T. Biagini, B. Bartolini, E. Giombini, M. Capobianchi, F. Ferrè, G. Chillemi, A. Desideri","doi":"10.2174/1875036201408010001","DOIUrl":"https://doi.org/10.2174/1875036201408010001","url":null,"abstract":"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.","PeriodicalId":38956,"journal":{"name":"Open Bioinformatics Journal","volume":"8 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2014-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68107448","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}

引用次数: 1