ORCID Profile
0000-0001-7293-8805
Current Organisations
Brown University
,
Medical University of Białystok
,
BRAC University
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Publisher: Informa UK Limited
Date: 13-07-2020
Publisher: Elsevier BV
Date: 12-2022
Publisher: Oxford University Press (OUP)
Date: 21-08-2022
DOI: 10.1093/BIB/BBAC343
Abstract: Antimicrobial peptides (AMPs) are a heterogeneous group of short polypeptides that target not only microorganisms but also viruses and cancer cells. Due to their lower selection for resistance compared with traditional antibiotics, AMPs have been attracting the ever-growing attention from researchers, including bioinformaticians. Machine learning represents the most cost-effective method for novel AMP discovery and consequently many computational tools for AMP prediction have been recently developed. In this article, we investigate the impact of negative data s ling on model performance and benchmarking. We generated 660 predictive models using 12 machine learning architectures, a single positive data set and 11 negative data s ling methods the architectures and methods were defined on the basis of published AMP prediction software. Our results clearly indicate that similar training and benchmark data set, i.e. produced by the same or a similar negative data s ling method, positively affect model performance. Consequently, all the benchmark analyses that have been performed for AMP prediction models are significantly biased and, moreover, we do not know which model is the most accurate. To provide researchers with reliable information about the performance of AMP predictors, we also created a web server AMPBenchmark for fair model benchmarking. AMPBenchmark is available at BioGenies.info/AMPBenchmark.
Publisher: Public Library of Science (PLoS)
Date: 29-08-2019
Publisher: Public Library of Science (PLoS)
Date: 18-12-2017
Publisher: Public Library of Science (PLoS)
Date: 06-07-2018
Publisher: Springer Science and Business Media LLC
Date: 12-2017
Publisher: Elsevier BV
Date: 03-2021
Publisher: Public Library of Science (PLoS)
Date: 10-02-2020
Publisher: Public Library of Science (PLoS)
Date: 31-10-2016
Publisher: Springer Science and Business Media LLC
Date: 31-01-2015
Publisher: Cambridge University Press (CUP)
Date: 16-01-2015
DOI: 10.1017/S0950268814003781
Abstract: There is limited information on percent expenditure of household income due to childhood diarrhoea especially in rural Bangladesh. A total of 4205 children aged years with acute diarrhoea were studied. Percent expenditure was calculated as total expenditure for the diarrhoeal episode ided by monthly family income, multiplied by 100. Overall median percent expenditure was 3·04 (range 0·01–94·35). For Vibrio cholerae it was 6·42 (range 0·52–82·85), for enterotoxigenic Escherichia coli 3·10 (range 0·22–91·87), for Shigella 3·17 (range 0·06–77·80), and for rotavirus 3·08 (range 0·06–48·00). In a multinomial logistic regression model, for the upper tertile of percent expenditure, significant higher odds were found for male sex, travelling a longer distance to reach hospital (⩾median of 4 miles), seeking care elsewhere before attending hospital, vomiting, higher frequency of purging (⩾10 times/day), some or severe dehydration and stunting. V. cholerae was the highest and rotavirus was the least responsible pathogen for percent expenditure of household income due to childhood diarrhoea.
Publisher: Wiley
Date: 22-11-2018
DOI: 10.1111/TMI.13171
Abstract: To assess tuberculosis mortality in Bangladesh through a population-based survey using a Verbal Autopsy tool. Nationwide mortality survey employing the WHO-recommended Verbal Autopsy (VA) tool, and using InsilicoVA, a data-driven method, to assign the cause of death. Using a three-stage cluster s ling method, 3997 VA interviews were conducted in both urban and rural areas of Bangladesh. Cause-specific mortality fractions (CSMF) were estimated using Bayesian probabilistic models. 6.8% of total deaths in the population were due to TB [95% CI: (5.1, 8.9)], comprising 12.0% [95% CI: (11.1, 12.8)] and 6.42% [95% CI: (5.4, 7.3)] of total male and female deaths, respectively. This proportion was highest among adults age 15-49 years [12.2%, 95% CI: (9.4, 14.6)]. The urban population is more likely to die from TB, and urban males have highest CSMF [13.6%, 95% CI: (9.1, 16.9)]. Our survey results show that TB is the fifth major cause of death in the general population and that sex and place of residence (urban/rural) have a significant effect on TB mortality in Bangladesh. The underlying causes of higher rates of TB-related deaths in urban areas and particularly among urban males, who have better knowledge and higher enrollment in the DOTS Program, need to be explored.
Publisher: Springer Science and Business Media LLC
Date: 12-09-2018
Publisher: Springer Science and Business Media LLC
Date: 08-2017
Publisher: Springer Science and Business Media LLC
Date: 14-01-2017
Publisher: Informa UK Limited
Date: 04-05-2018
DOI: 10.1080/17446651.2018.1471354
Abstract: The worldwide prevalence of Gestational Diabetes Mellitus (GDM) is increasing day by day. However, there is a knowledge gap regarding the effect of ethnic and geographical distribution on the risk of developing Diabetes Mellitus (DM) in women with history of GDM. This review was conducted to find out the role of ethnic and geographical distribution on the risk of developing DM is women with GDM. In this review we conducted a comprehensive search of published studies through different electronic databases (PubMed, Google Scholar, CINAHL, CINAHL plus and EMBASE) published between 1990 and 2017. The studies which were published in English investigated the risk of development of DM in women with previous history of GDM, reported outcome according to ethnicity with specific criteria of reporting DM and GDM, reported development of diabetes after 6 month of delivery in women with GDM during pregnancy were included. Initially, 350 articles were identified, among which 16 articles were included in the final review. Studies showed the increased risk of developing subsequent DM is associated with precedent GDM. Around 7-84% women developed diabetes after GDM in five years follow up, where some studies reported the risk continues to increase with increasing age. Risk of DM was found higher for some specific ethnicities, irrespective of the location of the study conducted. East Indian women showed the highest risk of postpartum DM after GDM and the crude prevalence remained almost similar in all form of study worldwide. Public health programme should focus more on women belonging to high-risk ethnicity of GDM for the prevention of postpartum DM.
Publisher: Wiley
Date: 07-11-2019
DOI: 10.1002/HSR2.141
Publisher: Cold Spring Harbor Laboratory
Date: 30-05-2022
DOI: 10.1101/2022.05.30.493946
Abstract: Antimicrobial peptides (AMPs) are a heterogeneous group of short polypeptides that target microorganisms but also viruses and cancer cells. Due to their lower selection for resistance compared to traditional antibiotics, AMPs have been attracting the ever-growing attention from researchers, including bioinformaticians. Machine learning represents the most cost-effective method for novel AMP discovery and consequently many computational tools for AMP prediction have been recently developed. In this article, we investigate the impact of negative data s ling on model performance and benchmarking. We generated 660 predictive models using 12 machine learning architectures, a single positive data set and 11 negative data s ling methods the architectures and methods were defined on the basis of published AMP prediction software. Our results clearly indicate that similar training and benchmark data set, i.e. produced by the same or a similar negative data s ling method, positively affect model performance. Consequently, all the benchmark analyses that have been performed for AMP prediction models are significantly biased and, moreover, we do not know which model is the most accurate. To provide researchers with reliable information about the performance of AMP predictors, we also created a web server AMPBenchmark for fair model benchmarking. AMPBenchmark is available at BioGenies.info/AMPBenchmark .
Publisher: Ubiquity Press, Ltd.
Date: 2023
DOI: 10.5334/AOGH.3948
Location: Poland
No related grants have been discovered for Malabika Sarker.