Prostate cancer (PCa) brings huge public health burden in men. A growing number of conventional o... more Prostate cancer (PCa) brings huge public health burden in men. A growing number of conventional observational studies report associations of multiple circulating proteins with PCa risk. However, the existing findings may be subject to incoherent biases of conventional epidemiologic studies. To better characterize their associations, herein, we evaluated associations of genetically predicted concentrations of plasma proteins with PCa risk. We developed comprehensive genetic prediction models for protein levels in plasma. After testing 1308 proteins in 79 194 cases and 61 112 controls of European ancestry included in the consortia of BPC3, CAPS, CRUK, PEGASUS, and PRACTICAL, 24 proteins showed significant associations with PCa risk, including 16 previously reported proteins and eight novel proteins. Of them, 14 proteins showed negative associations and 10 showed positive associations with PCa risk. For 18 of the identified proteins, potential functional somatic changes of encoding gen...
: MicroRNAs (miRNAs) are central players that regulate the post-transcriptional processes of gene... more : MicroRNAs (miRNAs) are central players that regulate the post-transcriptional processes of gene expression. Binding of miRNAs to target mRNAs can repress their translation by inducing the degradation or by inhibiting the translation of the target mRNAs. High-throughput experimental approaches for miRNA target identification are costly and time-consuming, depending on various factors. It is vitally important to develop bioinformatics methods for accurately predicting miRNA targets. With the increase of RNA sequences in the post-genomic era, bioinformatics methods are being developed for miRNA studies especially for miRNA target prediction. This review summarizes the current development of state-of-the-art bioinformatics tools for miRNA target prediction, points out the progress and limitations of the available miRNA databases, and their working principles. Finally, we discuss the caveat and perspectives of the next-generation algorithms for the prediction of miRNA targets.
Introduction: Systematic variation is a common issue in metabolomics data analysis. Therefore, di... more Introduction: Systematic variation is a common issue in metabolomics data analysis. Therefore, different scaling and normalization techniques are used to preprocess the data for metabolomics data analysis. Although several scaling methods are available in the literature, however, choice of scaling, transformation and/or normalization technique influence the further statistical analysis. It is challenged to choose the appropriate scaling technique for downstream analysis to get accurate results or to the make proper decision. Moreover, the existing scaling techniques are sensitive to outliers or extreme values. Objectives: To fill the gap, our objective is to introduce a robust scaling approach that is not influenced by outliers as well as provides more accurate results for downstream analysis. Methods: Here, we introduced a new weighted scaling approach that is robust against outliers however, where no additional outlier detection/treatment step is needed in data preprocessing and a...
In this study, we tested the interaction effect of multimodal datasets using a novel method calle... more In this study, we tested the interaction effect of multimodal datasets using a novel method called the kernel method for detecting higher order interactions among biologically relevant mulit-view data. Using a semiparametric method on a reproducing kernel Hilbert space (RKHS), we used a standard mixed-effects linear model and derived a score-based variance component statistic that tests for higher order interactions between multi-view data. The proposed method offers an intangible framework for the identification of higher order interaction effects (e.g., three way interaction) between genetics, brain imaging, and epigenetic data. Extensive numerical simulation studies were first conducted to evaluate the performance of this method. Finally, this method was evaluated using data from the Mind Clinical Imaging Consortium (MCIC) including single nucleotide polymorphism (SNP) data, functional magnetic resonance imaging (fMRI) scans, and deoxyribonucleic acid (DNA) methylation data, resp...
2008 11th International Conference on Computer and Information Technology, 2008
... [12] M. Huda. Bootstrapping and Robust Estimation in Factor Analysis: Application in Stock Pr... more ... [12] M. Huda. Bootstrapping and Robust Estimation in Factor Analysis: Application in Stock Price Data, M.Sc. thesis, Department of statistics, University of Rajshahi, Bangladesh, 2005. [13] M. A, Alam, M. Nasser, and AHMR Imon. ...
2013 12th International Conference on Machine Learning and Applications, 2013
Kernel canonical correlation analysis (kernel CCA) is sensitive to the choice of appropriate kern... more Kernel canonical correlation analysis (kernel CCA) is sensitive to the choice of appropriate kernels and associated parameters. To the best of our knowledge there is no general well-founded approach for choosing them. As we demonstrate with Gaussian kernels, the kernel CCA tends to show perfect correlation as the bandwidth parameter of the Gaussian kernel decreases, while it provides inappropriate features with all the data concentrated in a few points. This is caused by the ill-posed ness of the kernel CCA with the 4th order moment of canonical variates becomes large. To overcome this problem, we propose to use constraints on the 4th order moments of canonical variates in addition to the variances. Experiments on synthesized and real world datasets demonstrate that the proposed kernel CCA provides well-posed and robust solution in reasonable ranges of all the hyper parameters.
The objectives of the study were to assess the variability of growth contributing characters of 5... more The objectives of the study were to assess the variability of growth contributing characters of 50 okra (Abelmoschus esculentus L.) accessions and their interrelation effects on the yield of green pods. The experiment was undertaken at the Horticulture Farm of Bangladesh Agricultural University, Mymensingh during the period from February to May, 2002. There was a wide range of variation for spread of plant (43.73 cm), height of plant (80.90 cm) and length of petiole (12.31 cm). Moderate variation for number of nodes per plant (14.58), number of leaves per plant (24.51 at 80 DAS), length of leaf (12.20 cm), breadth of leaf (13.05 cm); and lesser variation for number of primary branches per plant (1.57) was observed. The yield of green pod varied significantly and ranged from 4.39 t/ha (accession 19) to 12.77 t/ha (accession 69) with the average value of 7.86 t/ha. Number of primary branches per plant, which showed a lesser range of variation, recorded the highest genotypic co-efficie...
Prostate cancer (PCa) brings huge public health burden in men. A growing number of conventional o... more Prostate cancer (PCa) brings huge public health burden in men. A growing number of conventional observational studies report associations of multiple circulating proteins with PCa risk. However, the existing findings may be subject to incoherent biases of conventional epidemiologic studies. To better characterize their associations, herein, we evaluated associations of genetically predicted concentrations of plasma proteins with PCa risk. We developed comprehensive genetic prediction models for protein levels in plasma. After testing 1308 proteins in 79 194 cases and 61 112 controls of European ancestry included in the consortia of BPC3, CAPS, CRUK, PEGASUS, and PRACTICAL, 24 proteins showed significant associations with PCa risk, including 16 previously reported proteins and eight novel proteins. Of them, 14 proteins showed negative associations and 10 showed positive associations with PCa risk. For 18 of the identified proteins, potential functional somatic changes of encoding gen...
: MicroRNAs (miRNAs) are central players that regulate the post-transcriptional processes of gene... more : MicroRNAs (miRNAs) are central players that regulate the post-transcriptional processes of gene expression. Binding of miRNAs to target mRNAs can repress their translation by inducing the degradation or by inhibiting the translation of the target mRNAs. High-throughput experimental approaches for miRNA target identification are costly and time-consuming, depending on various factors. It is vitally important to develop bioinformatics methods for accurately predicting miRNA targets. With the increase of RNA sequences in the post-genomic era, bioinformatics methods are being developed for miRNA studies especially for miRNA target prediction. This review summarizes the current development of state-of-the-art bioinformatics tools for miRNA target prediction, points out the progress and limitations of the available miRNA databases, and their working principles. Finally, we discuss the caveat and perspectives of the next-generation algorithms for the prediction of miRNA targets.
Introduction: Systematic variation is a common issue in metabolomics data analysis. Therefore, di... more Introduction: Systematic variation is a common issue in metabolomics data analysis. Therefore, different scaling and normalization techniques are used to preprocess the data for metabolomics data analysis. Although several scaling methods are available in the literature, however, choice of scaling, transformation and/or normalization technique influence the further statistical analysis. It is challenged to choose the appropriate scaling technique for downstream analysis to get accurate results or to the make proper decision. Moreover, the existing scaling techniques are sensitive to outliers or extreme values. Objectives: To fill the gap, our objective is to introduce a robust scaling approach that is not influenced by outliers as well as provides more accurate results for downstream analysis. Methods: Here, we introduced a new weighted scaling approach that is robust against outliers however, where no additional outlier detection/treatment step is needed in data preprocessing and a...
In this study, we tested the interaction effect of multimodal datasets using a novel method calle... more In this study, we tested the interaction effect of multimodal datasets using a novel method called the kernel method for detecting higher order interactions among biologically relevant mulit-view data. Using a semiparametric method on a reproducing kernel Hilbert space (RKHS), we used a standard mixed-effects linear model and derived a score-based variance component statistic that tests for higher order interactions between multi-view data. The proposed method offers an intangible framework for the identification of higher order interaction effects (e.g., three way interaction) between genetics, brain imaging, and epigenetic data. Extensive numerical simulation studies were first conducted to evaluate the performance of this method. Finally, this method was evaluated using data from the Mind Clinical Imaging Consortium (MCIC) including single nucleotide polymorphism (SNP) data, functional magnetic resonance imaging (fMRI) scans, and deoxyribonucleic acid (DNA) methylation data, resp...
2008 11th International Conference on Computer and Information Technology, 2008
... [12] M. Huda. Bootstrapping and Robust Estimation in Factor Analysis: Application in Stock Pr... more ... [12] M. Huda. Bootstrapping and Robust Estimation in Factor Analysis: Application in Stock Price Data, M.Sc. thesis, Department of statistics, University of Rajshahi, Bangladesh, 2005. [13] M. A, Alam, M. Nasser, and AHMR Imon. ...
2013 12th International Conference on Machine Learning and Applications, 2013
Kernel canonical correlation analysis (kernel CCA) is sensitive to the choice of appropriate kern... more Kernel canonical correlation analysis (kernel CCA) is sensitive to the choice of appropriate kernels and associated parameters. To the best of our knowledge there is no general well-founded approach for choosing them. As we demonstrate with Gaussian kernels, the kernel CCA tends to show perfect correlation as the bandwidth parameter of the Gaussian kernel decreases, while it provides inappropriate features with all the data concentrated in a few points. This is caused by the ill-posed ness of the kernel CCA with the 4th order moment of canonical variates becomes large. To overcome this problem, we propose to use constraints on the 4th order moments of canonical variates in addition to the variances. Experiments on synthesized and real world datasets demonstrate that the proposed kernel CCA provides well-posed and robust solution in reasonable ranges of all the hyper parameters.
The objectives of the study were to assess the variability of growth contributing characters of 5... more The objectives of the study were to assess the variability of growth contributing characters of 50 okra (Abelmoschus esculentus L.) accessions and their interrelation effects on the yield of green pods. The experiment was undertaken at the Horticulture Farm of Bangladesh Agricultural University, Mymensingh during the period from February to May, 2002. There was a wide range of variation for spread of plant (43.73 cm), height of plant (80.90 cm) and length of petiole (12.31 cm). Moderate variation for number of nodes per plant (14.58), number of leaves per plant (24.51 at 80 DAS), length of leaf (12.20 cm), breadth of leaf (13.05 cm); and lesser variation for number of primary branches per plant (1.57) was observed. The yield of green pod varied significantly and ranged from 4.39 t/ha (accession 19) to 12.77 t/ha (accession 69) with the average value of 7.86 t/ha. Number of primary branches per plant, which showed a lesser range of variation, recorded the highest genotypic co-efficie...
Uploads
Papers by Md. Ashad Alam