Robust identification of significant interactions between toxicogenomic biomarkers and their regulatory chemical compounds using logistic moving range chart

Hasan, Mohammad Nazmol; Begum, Anjuman Ara; Rahman, Md. Ferdous; Mollah, Md. Nurul Haque

doi:10.1016/j.compbiolchem.2018.12.020

Cited by 4 publications

(4 citation statements)

References 21 publications

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“…Therefore, treatment on the outlier observations is very important. There are three ways to obtain robust estimates in presence of outlier observations as deleting the observations with outlier from the dataset, applying the robust methods, and applying conventional methods on the modified dataset (Hasan, Rana, Begum, Rahman and Mollah, 2018a;Hasan, Rana, Begum, Rahman and Mollah, 2018b;Hasan, Badsha & Mollah, 2020). Among these first one is cooperatively easy to use.…”

Section: Treatment On the Outlier Observationsmentioning

confidence: 99%

A Comparison of Trend Models for Predicting Tea Production in Bangladesh

Hasan¹

2022

Nep. J. Stats.

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Background: Tea (Camellia sinensis) is a manufactured popular beverage that is mostly consumed around the globe including Bangladesh. Tea consumptions are increasing day by day due to its healthcare effects in the world as well as in Bangladesh. To meet the ever-growing population’s demand in Bangladesh, it is important to predict the production of tea. Objective: Although it is found in the literatures that studies have examined different trend models for tea and agricultural crops, no such study has been found to choose best trend model for predicting the production of tea in Bangladesh. Therefore, an attempt is made to identify the best trend model based on different model selection criterion for the prediction of tea production in Bangladesh. Materials and Methods: Six trend models namely linear, logarithmic, inverse, quadratic, cubic, and compound were applied on tea production dataset (1976 to 2020) collected from the Food and Agriculture Organization Corporate Statistical Database (FAOSTAT) website. For justification of the model, different model section criteria have been checked. Results: The results showed that the compound trend model is the most suitable among considered trend models for predicting the tea production in Bangladesh. Finally, the yearly growth rate of tea production for the period from1976 to 2020 found the compound growth rate of tea production in Bangladesh as 1.02 per year. It is important to note that for the next five years, forecasted increasing rate could be 4840.91 tonnes for the yearly tea production of Bangladesh. Furthermore, remarkable increased progresses of production have been noticed as 23585.7 and 21691.01 tonnes, respectively for the years 2019 and 2020 by the re-estimated model due to the proper nourishing and maintaining the input supply chain from government ends. Conclusion: In the respect of the growing demand, the best-fitted trend model applied herewith through outlier checking and related sophisticated justification tools on the tea production area could help to accumulate knowledge for the practitioner.

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Section: Treatment On the Outlier Observationsmentioning

confidence: 99%

A Comparison of Trend Models for Predicting Tea Production in Bangladesh

Hasan¹

2022

Nep. J. Stats.

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“…The CCIM is a special type of control chart is preferable for individual measurement or when the sample size is one for observing the quality characteristics of a product in a production process. This control chart is efficient to differentiate assailable causes of variation (variations due to improperly control machine, operators' errors, and defective raw materials) from the chance/natural causes of variation of a production process (Montgomery, 2016, Hasan et al, 2019a. The CCIM contains a central line (CL) which represents the average value of the quality characteristics corresponding to in-control state, an upper control limit (UCL) and a lower control limit (LCL).…”

Section: Extraction Of Toxicogenomic Biomarkers and Their Regulatory mentioning

confidence: 99%

“…Wheeler, 1995 has said that the control charts work irrespective of the distribution, so CCIM can be used for individual measurement even the observations do not follow normal distribution. In fact, there is an analogy between the process monitoring consisting of individual measurement and assessing the DCCs/drugs toxicity based on toxicogenomic data (Hasan et al, 2019a). Therefore, in this study, we have used CCIM to identify the biomarker co-clusters to explore toxicogenomic biomarkers and their regulatory DCCs.…”

Section: Extraction Of Toxicogenomic Biomarkers and Their Regulatory mentioning

confidence: 99%

Robust Hierarchical Co-clustering to Explore Toxicogenomic Biomarkers and Their Regulatory Doses of Chemical Compounds

Hasan

Badsha

Mollah

2020

Preprint

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Toxicogenomics combines high throughput molecular technologies with statistical and machine learning approaches to discover a similar group of doses of chemical compounds (DCCs) and genes to explore toxicogenomic biomarkers and their regulatory DCCs. This is also very important in the toxicity study of environmental stressors, synthetic chemicals and drug discovery and development process. Different clustering algorithms are concerned with the discovering of interesting clusters/groups of row or column entities of a dataset. Among those hierarchical clustering (HC) and logistic probabilistic hidden variable model (LPHVM) can identify toxicogenomic biomarkers and their regulatory DCCs forming co-cluster. However, the HC method is very sensitive to outlying observations. On the other hand, though LPHVM is a robust approach, it consumes more time for calculation since it is Expectation-Maximization (EM) based iterative approach. Additionally, the LPHVM creates artificiality problem taking absolute value of the data matrix. Therefore, to overcome these problems in this paper, we proposed a robust hierarchical co-clustering (RHCOC) algorithm to co-cluster genes and DCCs simultaneously with a view to explore toxicogenomic biomarkers and their regulatory DCCs. The performance of the proposed RHCOC algorithm over the github (https://github.com/mdbahadur/rhcoclust).

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“…Verification was done by using a Moving Range chart to compare observed values (actual data) with forecasted values of the same needs. Moving Range is a type of control chart used in statistical quality control (Hasan et al, 2019). In this case, this Moving Range was used to check the quality of the forecast used.…”

Section: Model Verificationmentioning

confidence: 99%

A Production Planning of Mixed Champignon Product to Optimize Profit Gained By PT X

Darmawan

Widiawati

2020

j.tek.ind.pert.

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PT X adalah perusahaan di bidang agroindustri khususnya pada komoditas jamur kancing. Jamur kancing memiliki umur simpan yang pendek sehingga harus segera dijual ke konsumen atau diproses menjadi produk olahan. Jumlah permintaan jamur tidak pasti, menyebabkan terjadinya kelebihan atau kekurangan produksi baik pada produk segar maupun olahan. Oleh karena itu, perlu untuk menghitung rencana kombinasi produk yang dapat memenuhi tujuan yang diinginkan dan mendapatkan solusi yang memuaskan. Penelitian ini menggunakan metode goal programming dalam perencanaan produk kombinasi. Penelitian ini juga menggunakan permintaan produk dan ketersediaan bahan baku dari Januari 2017 hingga Desember 2019. Selain itu, data kebutuhan bahan baku, jam tenaga kerja, biaya produksi, dan harga setiap produk juga digunakan. Data historis terkait jumlah permintaan dan ketersediaan bahan baku digunakan untuk meramalkan jumlah permintaan dan ketersediaan bahan baku pada periode perencanaan menggunakan model ARIMA. Penentuan tingkat prioritas yang digunakan dalam pemodelan goal programming pada penelitian ini menggunakan pairwise comparison. Berdasarkan hasil perencanaan yang telah dibuat untuk bulan Januari 2020, total jumlah produksi fresh champignon yaitu 22.584 kg, produk 250 mL whole champignon sebanyak 4.499 unit, produk 900 mL whole champignon sebanyak 2.571 unit, produk 250 mL sliced champignon sebanyak 4.567 unit, produk 900 mL sliced champignon sebanyak 32.306 unit, dan produk canned champignon sebanyak 85 unit. Model peramalan yang digunakan dalam penelitian ini representatif berdasarkan verifikasi yang telah dilakukan.

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Robust identification of significant interactions between toxicogenomic biomarkers and their regulatory chemical compounds using logistic moving range chart

Cited by 4 publications

References 21 publications

A Comparison of Trend Models for Predicting Tea Production in Bangladesh

A Comparison of Trend Models for Predicting Tea Production in Bangladesh

Robust Hierarchical Co-clustering to Explore Toxicogenomic Biomarkers and Their Regulatory Doses of Chemical Compounds

A Production Planning of Mixed Champignon Product to Optimize Profit Gained By PT X

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