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Article ; Online: A Computational Predictor for Accurate Identification of Tumor Homing Peptides by Integrating Sequential and Deep BiLSTM Features.

Arif, Roha / Kanwal, Sameera / Ahmed, Saeed / Kabir, Muhammad

Interdisciplinary sciences, computational life sciences

2024

Abstract: Cancer remains a severe illness, and current research indicates that tumor homing peptides (THPs) play an important part in cancer therapy. The identification of THPs can provide crucial insights for drug-discovery and pharmaceutical industries as they ... ...

Abstract	Cancer remains a severe illness, and current research indicates that tumor homing peptides (THPs) play an important part in cancer therapy. The identification of THPs can provide crucial insights for drug-discovery and pharmaceutical industries as they allow for tailored medication delivery towards cancer cells. These peptides have a high affinity enabling particular receptors present upon tumor surfaces, allowing for the creation of precision medications that reduce off-target consequences and enhance cancer patient treatment results. Wet-lab techniques are considered essential tools for studying THPs; however, they're labor-extensive and time-consuming, therefore making prediction of THPs a challenging task for the researchers. Computational-techniques, on the other hand, are considered significant tools in identifying THPs according to the sequence data. Despite many strategies have been presented to predict new THP, there is still a need to develop a robust method with higher rates of success. In this paper, we developed a novel framework, THP-DF, for accurately identifying THPs on a large-scale. Firstly, the peptide sequences are encoded through various sequential features. Secondly, each feature is passed to BiLSTM and attention layers to extract simplified deep features. Finally, an ensemble-framework is formed via integrating sequential- and deep features which are fed to a support vector machine which with 10-fold cross-validation to carry to validate the efficiency. The experimental results showed that THP-DF worked better on both [Formula: see text] and [Formula: see text] datasets by achieving accuracy of > 95% which are higher than existing predictors both datasets. This indicates that the proposed predictor could be a beneficial tool to precisely and rapidly identify THPs and will contribute to the cutting-edge cancer treatment strategies and pharmaceuticals.
Language	English
Publishing date	2024-05-11
Publishing country	Germany
Document type	Journal Article
ZDB-ID	2493085-4
ISSN	1867-1462 ; 1913-2751
ISSN (online)	1867-1462
ISSN	1913-2751
DOI	10.1007/s12539-024-00628-9
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Article ; Online: A novel stacking-based predictor for accurate prediction of antimicrobial peptides.

Kanwal, Sameera / Arif, Roha / Ahmed, Saeed / Kabir, Muhammad

Journal of biomolecular structure & dynamics

2024 , Page(s) 1–12

Abstract: Antimicrobial peptides (AMPs) are gaining acceptance and support as a chief antibiotic substitute since they boost human immunity. They retain a wide range of actions and have a low risk of developing resistance, which are critical properties to the ... ...

Abstract	Antimicrobial peptides (AMPs) are gaining acceptance and support as a chief antibiotic substitute since they boost human immunity. They retain a wide range of actions and have a low risk of developing resistance, which are critical properties to the pharmaceutical industry for drug discovery. Antibiotic sensitivity, however, is an issue that affects people all around the world and has the potential to one day lead to an epidemic. As cutting-edge therapeutic agents, AMPs are also expected to cure microbial infections. In order to produce tolerable drugs, it is crucial to understand the significance of the basic architecture of AMPs. Traditional laboratory methods are expensive and time-consuming for AMPs testing and detection. Currently, bioinformatics techniques are being successfully applied to the detection of AMPs. In this study, we have developed a novel
Language	English
Publishing date	2024-03-18
Publishing country	England
Document type	Journal Article
ZDB-ID	49157-3
ISSN	1538-0254 ; 0739-1102
ISSN (online)	1538-0254
ISSN	0739-1102
DOI	10.1080/07391102.2024.2329298
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Article ; Online: An explainable stacking-based approach for accelerating the prediction of antidiabetic peptides.

Arshad, Farwa / Ahmed, Saeed / Amjad, Aqsa / Kabir, Muhammad

Analytical biochemistry

2024 Volume 691, Page(s) 115546

Abstract: Diabetes is a chronic disease that is characterized by high blood sugar levels and can have several harmful outcomes. Hyperglycemia, which is defined by persistently elevated blood sugar, is one of the primary concerns. People can improve their overall ... ...

Abstract	Diabetes is a chronic disease that is characterized by high blood sugar levels and can have several harmful outcomes. Hyperglycemia, which is defined by persistently elevated blood sugar, is one of the primary concerns. People can improve their overall well-being and get optimal health outcomes by prioritizing diabetes control. Although the use of experimental approaches in diabetes treatment is cost-effective, it necessitates the development of many strategies for evaluating the efficacy of therapies. Researchers can quickly create new strategies for managing diabetes and get vital insights by enabling virtual screening with computational tools and procedures. In this study, we suggest a predictor named STADIP (STacking-based predictor for AntiDiabetic Peptides), a new method to predict antidiabetic peptides (ADPs) utilizing a stacked-based ensemble approach. It uses 12 different feature encodings and seven machine-learning techniques to construct 84 baseline models. The impacts of various baseline models on ADP prediction were then thoroughly examined. A two-step feature selection method, eXtreme Gradient Boosting with Sequential Forward Selection (XGB-SFS), was employed to determine the optimal number, out of 84 PFs to enhance predictive performance. Subsequently, utilizing the meta-predictor approach, 45 selected PFs were integrated into an XGB classifier to formulate the final hybrid model. The proposed method demonstrated superior predictive capabilities compared to constituent baseline models, as evidenced by evaluations on both cross-validation and independent tests. During extensive independent testing, STADIP achieved promising performance with accuracy and mathew's correlation coefficient of 0.954 and 0.877, respectively. It is anticipated that it will be useful tool in helping the scientific community to identify new antidiabetic proteins.
MeSH term(s)	Hypoglycemic Agents/therapeutic use ; Hypoglycemic Agents/chemistry ; Peptides/chemistry ; Humans ; Machine Learning ; Diabetes Mellitus/drug therapy ; Diabetes Mellitus/blood
Chemical Substances	Hypoglycemic Agents ; Peptides
Language	English
Publishing date	2024-04-25
Publishing country	United States
Document type	Journal Article
ZDB-ID	1110-1
ISSN	1096-0309 ; 0003-2697
ISSN (online)	1096-0309
ISSN	0003-2697
DOI	10.1016/j.ab.2024.115546
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Article ; Online: Machine Learning Techniques for Estimating Soil Moisture from Smartphone Captured Images

Hossain, Muhammad Riaz Hasib / Kabir, Muhammad Ashad

Agriculture. 2023 Feb. 27, v. 13, no. 3

2023

Abstract: Precise Soil Moisture (SM) assessment is essential in agriculture. By understanding the level of SM, we can improve yield irrigation scheduling which significantly impacts food production and other needs of the global population. The advancements in ... ...

Abstract	Precise Soil Moisture (SM) assessment is essential in agriculture. By understanding the level of SM, we can improve yield irrigation scheduling which significantly impacts food production and other needs of the global population. The advancements in smartphone technologies and computer vision have demonstrated a non-destructive nature of soil properties, including SM. The study aims to analyze the existing Machine Learning (ML) techniques for estimating SM from soil images and understand the moisture accuracy using different smartphones and various sunlight conditions. Therefore, 629 images of 38 soil samples were taken from seven areas in Sydney, Australia, and split into four datasets based on the image-capturing devices used (iPhone 6s and iPhone 11 Pro) and the lighting circumstances (direct and indirect sunlight). A comparison between Multiple Linear Regression (MLR), Support Vector Regression (SVR), and Convolutional Neural Network (CNN) was presented. MLR was performed with higher accuracy using holdout cross-validation, where the images were captured in indirect sunlight with the Mean Absolute Error (MAE) value of 0.35, Root Mean Square Error (RMSE) value of 0.15, and R² value of 0.60. Nevertheless, SVR was better with MAE, RMSE, and R² values of 0.05, 0.06, and 0.96 for 10-fold cross-validation and 0.22, 0.06, and 0.95 for leave-one-out cross-validation when images were captured in indirect sunlight. It demonstrates a smartphone camera’s potential for predicting SM by utilizing ML. In the future, software developers can develop mobile applications based on the research findings for accurate, easy, and rapid SM estimation.
Keywords	agriculture ; cameras ; computer software ; computer vision ; data collection ; food production ; irrigation ; mobile telephones ; neural networks ; regression analysis ; soil water ; solar radiation ; Australia
Language	English
Dates of publication	2023-0227
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article ; Online
ZDB-ID	2651678-0
ISSN	2077-0472
ISSN	2077-0472
DOI	10.3390/agriculture13030574
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Resiliency of healthcare expenditure to income shock: Evidence from dynamic heterogeneous panels.

Shimul, Shafiun Nahin / Kabir, Muhammad Ihsan-Ul- / Kadir, Fariha

Frontiers in public health

2023 Volume 11, Page(s) 1085338

Abstract: Using the World Bank data over the period of 1960-2019, this study aims at estimating the resiliency of health expenditures against gross domestic product (GDP). Long-run and short-run elasticities are calculated using the type of panel time series ... ...

Abstract	Using the World Bank data over the period of 1960-2019, this study aims at estimating the resiliency of health expenditures against gross domestic product (GDP). Long-run and short-run elasticities are calculated using the type of panel time series methods that are exclusively designed for dynamic heterogeneous panels: Mean Group, Pooled Mean Group, and Dynamic Fixed Effects estimators. These methods permit better estimations of elasticity with considerable heterogeneity across the 177 countries included in this study. Along with a standard elasticity estimation, this study estimates country-specific long-run and short-run elasticities along with error correction components. The study finds that the long-run elasticity of income is very close to unity, but short-run coefficients are insignificant for most nations. In addition, most countries revert to long-run equilibrium reasonably quickly if there is shock as the error correction coefficients are negative and, in many cases, very close to one. While for most developed countries, the short-run elasticities are lower in comparison with the short-run elasticities of developing countries indicating that many developing countries may face a larger decrease in health expenditure with the forecasted decline in income due to impending economic recession. Therefore, although this study is not directly intended to capture the post-COVID-19 effects, the study estimates may project the potential responses in health expenditure across countries due to potential income shocks.
MeSH term(s)	Humans ; Health Expenditures ; Models, Econometric ; COVID-19 ; Delivery of Health Care ; Income
Language	English
Publishing date	2023-03-07
Publishing country	Switzerland
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2711781-9
ISSN	2296-2565 ; 2296-2565
ISSN (online)	2296-2565
ISSN	2296-2565
DOI	10.3389/fpubh.2023.1085338
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Article ; Online: Farmers’ adaptations strategies towards soil salinity effects in agriculture: the interior coast of Bangladesh

Mazumder, Mohummed Shofi Ullah / Kabir, Muhammad Humayun

Climate Policy. 2022 Apr. 21, v. 22, no. 4 p.464-479

2022

Abstract: In this study, we critically analyze the general and climate-smart adaptation strategies used by farmers in four sub-districts in southern Bangladesh to address the effects of soil salinity in agriculture. Data were collected from 360 respondents using ... ...

Abstract	In this study, we critically analyze the general and climate-smart adaptation strategies used by farmers in four sub-districts in southern Bangladesh to address the effects of soil salinity in agriculture. Data were collected from 360 respondents using face-to-face interviews and were analyzed using multiple analyses (variance inflation factor, ordinary least squares, Tukey’s post hoc test). The adaptations used by farmers to manage the impacts of soil salinity in agriculture were significantly influenced by their age, education, family size, farm size, level of contact with agricultural extension department officials, and their training experience. Farmers found adult education programmes particularly useful, since they focused on smart technique(s) rather than traditional approaches for agricultural development. Adaptation was comparatively more common in ‘early adapters’ and ‘early majority’ (slower to adapt) farmers. Our analysis suggests that climate-smart agriculture (CSA) programmes should be implemented in all affected sub-districts and similarly affected parts of the world. Extension workers should provide face-to-face demonstrations to affected farmers and support them with self-help programmes, which can help to attenuate and minimize the effects of soil salinity on agriculture. It will also be important to provide agricultural innovation support to affected farmers to cope with the adverse effects of soil salinity. Effective extension services are key to increasing farmers’ access to innovative information and advice. Key policy insights A philosophy of ‘learning by viewing’ for climate-smart agricultural innovations may encourage them to adopt adaptation strategies to minimize the impacts of soil salinity on agriculture. Training programmes could be offered to farmers by the Department of Agricultural Extension, especially to target innovators and early adopters of climate-smart practices, supporting them with a ‘self-help’ approach. A motivational approach and good communication skills play a vital role when introducing new adaptation strategies. A climate-smart investment plan could identify policy opportunities to further develop agriculture actions to combat climate change. Building resilience to global climate change would help to sustainably increase agricultural productivity and incomes across farming communities.
Keywords	adult education ; agricultural development ; agricultural extension ; agricultural productivity ; climate change ; climate-smart agriculture ; coasts ; environmental policy ; family size ; farm size ; investment planning ; philosophy ; soil salinity ; variance ; Bangladesh ; Adaptation strategies ; general agricultural practice (GAP) ; climate-smart agriculture (CSA) ; soil salinity effects ; climate policy ; OLS
Language	English
Dates of publication	2022-0421
Size	p. 464-479.
Publishing place	Taylor & Francis
Document type	Article ; Online
ZDB-ID	2051510-8
ISSN	1752-7457 ; 1469-3062
ISSN (online)	1752-7457
ISSN	1469-3062
DOI	10.1080/14693062.2021.2024126
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Article ; Online: Heavy Metal Detection in Fritillaria thunbergii Using Laser-Induced Breakdown Spectroscopy Coupled with Variable Selection Algorithm and Chemometrics

Kabir, Muhammad Hilal / Guindo, Mahamed Lamine / Chen, Rongqin / Luo, Xinmeng / Kong, Wenwen / Liu, Fei

Foods. 2023 Mar. 07, v. 12, no. 6

2023

Abstract: Environmental and health risks associated with heavy metal pollution are serious. Human health can be adversely affected by the smallest amount of heavy metals. Modeling spectrum requires the careful selection of variables. Hence, simple variables that ... ...

Abstract	Environmental and health risks associated with heavy metal pollution are serious. Human health can be adversely affected by the smallest amount of heavy metals. Modeling spectrum requires the careful selection of variables. Hence, simple variables that have a low level of interference and a high degree of precision are required for fast analysis and online detection. This study used laser-induced breakdown spectroscopy coupled with variable selection and chemometrics to simultaneously analyze heavy metals (Cd, Cu and Pb) in Fritillaria thunbergii. A total of three machine learning algorithms were utilized, including a gradient boosting machine (GBM), partial least squares regression (PLSR) and support vector regression (SVR). Three promising wavelength selection methods were evaluated for comparison, namely, a competitive adaptive reweighted sampling method (CARS), a random frog method (RF), and an uninformative variable elimination method (UVE). Compared to full wavelengths, the selected wavelengths produced excellent results. Overall, RC², RV², RP², RSMEC, RSMEV and RSMEP for the selected variables are as follows: 0.9967, 0.8899, 0.9403, 1.9853 mg kg⁻¹, 11.3934 mg kg⁻¹, 8.5354 mg kg⁻¹; 0.9933, 0.9316, 0.9665, 5.9332 mg kg⁻¹, 18.3779 mg kg⁻¹, 11.9356 mg kg⁻¹; 0.9992, 0.9736, 0.9686, 1.6707 mg kg⁻¹, 10.2323 mg kg⁻¹, 10.1224 mg kg⁻¹ were obtained for Cd Cu and Pb, respectively. Experimental results showed that all three methods could perform variable selection effectively, with GBM-UVE for Cd, SVR-RF for Pb, and GBM-CARS for Cu providing the best results. The results of the study suggest that LIBS coupled with wavelength selection can be used to detect heavy metals rapidly and accurately in Fritillaria by extracting only a few variables that contain useful information and eliminating non-informative variables.
Keywords	Fritillaria thunbergii ; algorithms ; atomic absorption spectrometry ; chemometrics ; heavy metals ; human health ; pollution ; regression analysis ; wavelengths
Language	English
Dates of publication	2023-0307
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article ; Online
ZDB-ID	2704223-6
ISSN	2304-8158
ISSN	2304-8158
DOI	10.3390/foods12061125
Database	NAL-Catalogue (AGRICOLA)

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Article: Large-scale comparative review and assessment of computational methods for phage virion proteins identification.

Kabir, Muhammad / Nantasenamat, Chanin / Kanthawong, Sakawrat / Charoenkwan, Phasit / Shoombuatong, Watshara

EXCLI journal

2022 Volume 21, Page(s) 11–29

Abstract: Phage virion proteins (PVPs) are effective at recognizing and binding to host cell receptors while having no deleterious effects on human or animal cells. Understanding their functional mechanisms is regarded as a critical goal that will aid in rational ... ...

Abstract	Phage virion proteins (PVPs) are effective at recognizing and binding to host cell receptors while having no deleterious effects on human or animal cells. Understanding their functional mechanisms is regarded as a critical goal that will aid in rational antibacterial drug discovery and development. Although high-throughput experimental methods for identifying PVPs are considered the gold standard for exploring crucial PVP features, these procedures are frequently time-consuming and labor-intensive. Thusfar, more than ten sequence-based predictors have been established for the
Language	English
Publishing date	2022-01-03
Publishing country	Germany
Document type	Journal Article ; Review
ISSN	1611-2156
ISSN	1611-2156
DOI	10.17179/excli2021-4411
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Article ; Online: Impacts of COVID-19 on the Education, Life and Mental Health of Students in Bangladesh.

Piya, Fahmida Liza / Amin, Sumaiya / Das, Anik / Kabir, Muhammad Ashad

International journal of environmental research and public health

2022 Volume 19, Issue 2

Abstract: COVID-19's unanticipated consequences have resulted in the extended closure of various educational institutions, causing significant hardship to students. Even though many institutions rapidly transitioned to online education programs, various issues ... ...

Abstract	COVID-19's unanticipated consequences have resulted in the extended closure of various educational institutions, causing significant hardship to students. Even though many institutions rapidly transitioned to online education programs, various issues have emerged that are impacting many aspects of students' lives. An online survey was conducted with students of Bangladesh to understand how COVID-19 impacted their study, social and daily activities, plans, and mental health. A total of 409 Bangladeshi students took part in a survey. As a result of the COVID-19 pandemic, 13.7% of all participants are unable to focus on their studies, up from 1.2% previously. More than half of the participants (54%) have spent more time on social media than previously. We found that 45% of the participants have severe to moderate level depression. In addition, 48.6% of the students are experiencing severe to moderate level anxiety. According to our findings, students' inability to concentrate on their studies, their increased use of social media and electronic communications, changing sleep hours during the pandemic, increased personal care time, and changes in plans are all correlated with their mental health.
MeSH term(s)	Anxiety/epidemiology ; Bangladesh/epidemiology ; COVID-19 ; Depression/epidemiology ; Humans ; Mental Health ; Pandemics ; SARS-CoV-2 ; Students ; Universities
Language	English
Publishing date	2022-01-11
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2175195-X
ISSN	1660-4601 ; 1661-7827
ISSN (online)	1660-4601
ISSN	1661-7827
DOI	10.3390/ijerph19020785
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Article: Heavy Metal Detection in

Kabir, Muhammad Hilal / Guindo, Mahamed Lamine / Chen, Rongqin / Luo, Xinmeng / Kong, Wenwen / Liu, Fei

Foods (Basel, Switzerland)

2023 Volume 12, Issue 6

Abstract	Environmental and health risks associated with heavy metal pollution are serious. Human health can be adversely affected by the smallest amount of heavy metals. Modeling spectrum requires the careful selection of variables. Hence, simple variables that have a low level of interference and a high degree of precision are required for fast analysis and online detection. This study used laser-induced breakdown spectroscopy coupled with variable selection and chemometrics to simultaneously analyze heavy metals (Cd, Cu and Pb) in
Language	English
Publishing date	2023-03-07
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2704223-6
ISSN	2304-8158
ISSN	2304-8158
DOI	10.3390/foods12061125
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