Browsing by Author "Ahmed, Kawsar"

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A Bioinformatics Analysis to Identify Hub Genes from Protein-Protein Interaction Network for Cancer and Stress
(Springer, 2020-07-30) Ahmed, Md. Liton; Islam, Md. Rakibul; Paul, Bikash Kumar; Ahmed, Kawsar; Bhuyian, Touhid
Cancer is a disease involving the uncontrollable growth of cells with potential strafe to other organs of the body. Stress is a state of the body a non-specific response to any demand for change. Cancer had a deep relation with stress. Activation of the stress response and exposure to the associated hormones could promote the growth and spread of tumors. The immune system can be important for finding and eliminating cancer cells. This study is based on Cancer and Stress. In this study, we collect responsible genes from NCBI’s Gene database individually for stress and cancer. After that, common responsible genes were collected by using Venny online tools. From the common genes, we had constructed a protein-protein interaction network using the STRING database. Afterward, the top 10 hub genes were identified by using CytoHubba. Hub genes were identified based on their degree value where degree value more than or equal 72 are considered as hub gene. These hub genes may use to design a potential drug for cancer and stress combine. We have collected 3264 and 9433 human genes for Cancer and Stress respectively. 2477 common genes are found through Venny. We have been identified the UBC, TP53, RPS3, RPL5, RPL11, RPS27A, RPL19, RPL3, RPS7, and CTNNB1 as targeted hub genes by using the CytoHubba plugin of Cytoscape.
A comparative analysis of two different PCF structures for gas sensing application
(IEEE Xplore, 2016-07-11) Arif, Md. Faizul Huq; Ahmed, Kawsar; Asaduzzaman, Sayed
Toxic or Flammable gases and leakage of the gas lines can be detected by photonic crystal fibers for avoiding pollution, explosion or fire. Two different structures of hollow core based Photonic crystal fiber (HC-PCF) has been presented in this paper. The sensitivity of the proposed structures was numerically investigated by finite element method (FEM) varying the optical and geometrical properties. The numerical results show that the proposed PCF with six air holes shows 2.22 times higher sensitivity than the PCF with eight air holes in the first layer. Both of the PCFs exhibit reduced confinement loss also. The proposed PCFs can sense the lower refractive index based gases (toxic/ flammable) at a wide range of wavelength 0.8 μm to 2μm. Full Text Link: http://doi.org/10.1109/ICAEE.2015.7506842
A Machine Learning Approach for Risk Factors Analysis and Survival Prediction of Heart Failure Patients
(Elsevier, 2023-04-18) Ali, Md. Mamun; Al-Doori, Vian S.; Mirzah, Nubogh; Hemu, Asifa Afsari; Mahmud, Imran; Azam, Sami; Al-tabatabaie, Kusay Faisal; Ahmed, Kawsar; Bu, Francis M.; Moni, Mohammad Ali
In this study, we propose machine learning (ML) for risk factors analysis and survival prediction of Heart Failure (HF) patients using a survival dataset. Five supervised ML methods are applied to the dataset: Decision Tree (DT), Decision Tree Regressor (DTR), Random Forest (RF), XGBoost, and Gradient Boosting (GB) algorithms. We compare the applied algorithms’ performances based on accuracy, precision, recall, F-measure, and log loss value and show RF provides the highest accuracy of 97.78%. The analysis of the risk factors shows the most predictive features based on coefficients and feature importance. The top six risk factors for HF patients are serum creatinine (SC), age, ejection fraction (EF), platelets, creatinine phosphokinase (CPK), and SS (SS). Further analysis of these factors shows significant clustering of the features. The survival analysis finds that the increment of SC, age, and SS and the decrement of EF are the most significant risk factors for HF patients. Our results suggest that HF survival prediction is possible with higher accuracy using the proposed model. Our ML models are useful in clinical settings for screening patients with HF probability.
A Novel Hexahedron Photonic Crystal Fiber in Terahertz Propagation
(Scopus, 2020) Paul, Bikash Kumar; Haque, Md. Ashraful; Ahmed, Kawsar; Sen, Shuvo
A novel hexahedron fiber has been proposed for biomedical imaging applications and efficient guiding of terahertz radiation. A finite element method (FEM) has been applied to investigate the guiding properties rigorously. All numerically computational investigated results for optimum parameters have revealed the high numerical aperture (NA) of 0.52, high core power fraction of 64%, near zero flattened dispersion of 0.5 ± 0.6 ps/THz/cm over the 0.8–1.4 THz band and low losses with 80% of the bulk absorption material loss. In addition, the V–parameter is also inspected for checking the proposed fiber modality. The proposed single-mode hexahedron photonic crystal fiber (PCF) can be highly applicable for convenient broadband transmission and numerous applications in THz technology.
A Novel Hybrid Approach for Classifying Osteosarcoma Using Deep Feature Extraction and Multilayer Perceptron
(MDPI, 2023-06-18) Aziz, Md. Tarek; Mahmud, S. M. Hasan; Elahe, Md. Fazla; Jahan, Hosney; Rahman, Md Habibur; Nandi, Dip; Smirani, Lassaad K.; Ahmed, Kawsar; Bui, Francis M.; Moni, Mohammad Ali
Osteosarcoma is the most common type of bone cancer that tends to occur in teenagers and young adults. Due to crowded context, inter-class similarity, inter-class variation, and noise in H&E-stained (hematoxylin and eosin stain) histology tissue, pathologists frequently face difficulty in osteosarcoma tumor classification. In this paper, we introduced a hybrid framework for improving the efficiency of three types of osteosarcoma tumor (nontumor, necrosis, and viable tumor) classification by merging different types of CNN-based architectures with a multilayer perceptron (MLP) algorithm on the WSI (whole slide images) dataset. We performed various kinds of preprocessing on the WSI images. Then, five pre-trained CNN models were trained with multiple parameter settings to extract insightful features via transfer learning, where convolution combined with pooling was utilized as a feature extractor. For feature selection, a decision tree-based RFE was designed to recursively eliminate less significant features to improve the model generalization performance for accurate prediction. Here, a decision tree was used as an estimator to select the different features. Finally, a modified MLP classifier was employed to classify binary and multiclass types of osteosarcoma under the five-fold CV to assess the robustness of our proposed hybrid model. Moreover, the feature selection criteria were analyzed to select the optimal one based on their execution time and accuracy. The proposed model achieved an accuracy of 95.2% for multiclass classification and 99.4% for binary classification. Experimental findings indicate that our proposed model significantly outperforms existing methods; therefore, this model could be applicable to support doctors in osteosarcoma diagnosis in clinics. In addition, our proposed model is integrated into a web application using the FastAPI web framework to provide a real-time prediction.
A Novel Star Shape Photonic Crystal Fiber for Low Loss Terahertz Pulse Propagation
(Scopus, 2020) Ahmed, Fahad; Roy, Subrata; Ahmed, Kawsar; Paul, Bikash Kumar; Bahar, Ali Newaz
An extremely low loss circle-based star shape photonic crystal fiber (CS-PCF) has been proposed for terahertz (THz) spectrum. A dielectric material TOPAS has been used for constructing the proposed model. Some important parameters for the proposed waveguide have been investigated a wide THz spectrum ranging from 0.5 to 2.50 THz. The simulation result exhibits an extremely-low material loss of 0.01607 cm−1 and a large effective area of 1.386 × 106 μm2 at 1.0 THz frequency with 55% core porosity. In addition, the proposed structure establishes comparatively higher core power fraction maintaining lower scattering loss about 1.235 × 10−15 dB/cm and 91.97% of bulk absorption material loss at the same operating frequency. From the above results, it is anticipated that proposed CS-PCF model of the THz waveguide will make the conventional communication very effective over the current designs.
A novel star shape photonic crystal fiber for low loss terahertz pulse propagation
(2018-11-26) Ahmed, Fahad; Roy, Subrata; Ahmed, Kawsar; Paul, Bikash Kumar; Bahar, Ali Newaz
An extremely low loss circle-based star shape photonic crystal fiber (CS-PCF) has been proposed for terahertz (THz) spectrum. A dielectric material TOPAS has been used for constructing the proposed model. Some important parameters for the proposed waveguide have been investigated a wide THz spectrum ranging from 0.5 to 2.50 THz. The simulation result exhibits an extremely-low material loss of 0.01607 cm−1 and a large effective area of 1.386 × 106 μm2 at 1.0 THz frequency with 55% core porosity. In addition, the proposed structure establishes comparatively higher core power fraction maintaining lower scattering loss about 1.235 ×10−15 dB/cm and 91.97% of bulk absorption material loss at the same operating frequency. From the above results, it is anticipated that proposed CS-PCF model of the THz waveguide will make the conventional communication very effective over the current designs.
A robust computational quest: Discovering potential hits to improve the treatment of pyrazinamide-resistant Mycobacterium tuberculosis
(Scopus, 2024-09-28) Shaon, Md. Shazzad Hossain; Karim, Tasmin; Ali, Md. Mamun; Ahmed, Kawsar; Bui, Francis M.; Chen, Li; Moni, Mohammad Ali
RNA 5-methyluridine (m5U) sites play a significant role in understanding RNA modifications, which influence numerous biological processes such as gene expression and cellular functioning. Consequently, the identification of m5U sites can play a vital role in the integrity, structure, and function of RNA molecules. Therefore, this study introduces GRUpred-m5U, a novel deep learning based framework based on a gated recurrent unit in mature RNA and full transcript RNA datasets. We used three descriptor groups: nucleic acid composition, pseudo nucleic acid composition, and physicochemical properties, which include five feature extraction methods ENAC, Kmer, DPCP, DPCP type 2, and PseDNC. Initially, we aggregated all the feature extraction methods and created a new merged set. Three hybrid models were developed employing deep-learning methods and evaluated through 10-fold cross-validation with seven evaluation metrics. After a comprehensive evaluation, the GRUpred-m5U model outperformed the other applied models, obtaining 98.41% and 96.70% accuracy on the two datasets, respectively. To our knowledge, the proposed model outperformed all the existing state-of-the-art technology. The proposed supervised machine learning model was evaluated using unsupervised machine learning techniques such as principal component analysis (PCA), and it was observed that the proposed method provided a valid performance for identifying m5U. Considering its multi-layered construction, the GRUpred-m5U model has tremendous potential for future applications in the biological industry. The model, which consisted of neurons processing complicated input, excelled at pattern recognition and produced reliable results. Despite its greater size, the model obtained accurate results, essential in detecting m5U.
Advancing Thyroid Care: An Accurate Trustworthy Diagnostics System with Interpretable Ai and Hybrid Machine Learning Techniques
(Elsevier, 2024-08-20) Sutradhar, Ananda; Akter, Sharmin; Shamrat, F M Javed Mehedi; Idris, Mohd Yamani Idna Bin; Ahmed, Kawsar; Moni, Mohammad Ali
The worldwide prevalence of thyroid disease is on the rise, representing a chronic condition that significantly impacts global mortality rates. Machine learning (ML) approaches have demonstrated potential superiority in mitigating the occurrence of this disease by facilitating early detection and treatment. However, there is a growing demand among stakeholders and patients for reliable and credible explanations of the generated predictions in sensitive medical domains. Hence, we propose an interpretable thyroid classification model to illustrate outcome explanations and investigate the contribution of predictive features by utilizing explainable AI. Two real-time thyroid datasets underwent various preprocessing approaches, addressing data imbalance issues using the Synthetic Minority Over-sampling Technique with Edited Nearest Neighbors (SMOTE-ENN). Subsequently, two hybrid classifiers, namely RDKVT and RDKST, were introduced to train the processed and selected features from Univariate and Information Gain feature selection techniques. Following the training phase, the Shapley Additive Explanation (SHAP) was applied to identify the influential characteristics and corresponding values contributing to the outcomes. The conducted experiments ultimately concluded that the presented RDKST classifier achieved the highest performance, demonstrating an accuracy of 98.98 % when trained on Information Gain selected features. Notably, the features T3 (triiodothyronine), TT4 (total thyroxine), TSH (thyroid-stimulating hormone), FTI (free thyroxine index), and T3_measured significantly influenced the generated outcomes. By balancing classification accuracy and outcome explanation ability, this study aims to enhance the clinical decision-making process and improve patient care.
Alcohol sensing over O+E+S+C+L+U transmission band based on porous cored octagonal photonic crystal fiber
(Springer, 2017-03-17) Paul, Bikash Kumar; Islam, Md. Shadidul; Ahmed, Kawsar; Asaduzzaman, Sayed
A micro structure porous cored octagonal photonic crystal fiber (P-OPCF) has been proposed to sense aqueous analysts (alcohol series) over a wavelength range of 0.80 μm to 2.0 μm. By implementing a full vectorial finite element method (FEM), the numerical simulation on the proposed O-PCF has been analyzed. Numerical investigation shows that high sensitivity can be gained by changing the structural parameters. The obtained result shows the sensitivities of 66.78%, 67.66%, 68.34%, 68.72%, and 69.09%, and the confinement losses of 2.42×10−10 dB/m, 3.28×10−11 dB/m, 1.21×10−6 dB/m, 4.79×10−10 dB/m, and 4.99×10−9 dB/m at the 1.33 μm wavelength for methanol, ethanol, propanol, butanol, and pentanol, respectively can satisfy the condition of much legibility to install an optical system. The effects of the varying core and cladding diameters, pitch distance, operating wavelength, and effective refractive index are also reported here. It reflects that a significant sensitivity and low confinement loss can be achieved by the proposed P-OPCF. The proposed P-OPCF also covers the wavelength band (O+E+S+C+L+U). The investigation also exhibits that the sensitivity increases when the wavelength increases like SO-band
AlzheimerNet: An Effective Deep Learning Based Proposition for Alzheimer’s Disease Stages Classification From Functional Brain Changes in Magnetic Resonance Images
(IEEE, 2023-02-14) Shamrat, F M Javed Mehedi; Akter, Shamima; Azam, Sami; Karim, Asif; Ghosh, Pronab; Hasib, Khan Md.; Boer, Frisode; Ahmed, Kawsar
Alzheimer’s disease is largely the underlying cause of dementia due to its progressive neurodegenerative nature among the elderly. The disease can be divided into five stages: Subjective Memory Concern (SMC), Mild Cognitive Impairment (MCI), Early MCI (EMCI), Late MCI (LMCI), and Alzheimer’s Disease (AD). Alzheimer’s disease is conventionally diagnosed using an MRI scan of the brain. In this research, we propose a fine-tuned convolutional neural network (CNN) classifier called AlzheimerNet, which can identify all five stages of Alzheimer’s disease and the Normal Control (NC) class. The ADNI database’s MRI scan dataset is obtained for use in training and testing the proposed model. To prepare the raw data for analysis, we applied the CLAHE image enhancement method. Data augmentation was used to remedy the unbalanced nature of the dataset and the resultant dataset consisted of 60000 image data on the 6 classes. Initially, five existing models including VGG16, MobileNetV2, AlexNet, ResNet50 and InceptionV3 were trained and tested to achieve test accuracies of 78.84%, 86.85%, 78.87%, 80.98% and 96.31% respectively. Since InceptionV3 provides the highest accuracy, this model is later modified to design the AlzheimerNet using RMSprop optimizer and learning rate 0.00001 to achieve the highest test accuracy of 98.67%. The five pre-trained models and the proposed fine-tuned model were compared in terms of various performance matrices to demonstrate whether the AlzheimerNet model is in fact performing better in classifying and detecting the six classes. An ablation study shows the hyperparameters used in the experiment. The suggested model outperforms the traditional methods for classifying Alzheimer’s disease stages from brain MRI, as measured by a two-tailed Wilcoxon signed-rank test, with a significance of < 0.05.
AMP-RNNpro: a two-stage approach for identification of antimicrobials using probabilistic features
(Scopus, 2024-06-05) Shaon, Md. Shazzad Hossain; Karim, Tasmin; Sultan, Md. Fahim; Ali, Md. Mamun; Ahmed, Kawsar; Hasan, Md. Zahid; Moustafa, Ahmed; Bui, Francis M.; Al-Zahrani, Fahad Ahmed
Antimicrobials are molecules that prevent the formation of microorganisms such as bacteria, viruses, fungi, and parasites. The necessity to detect antimicrobial peptides (AMPs) using machine learning and deep learning arises from the need for efficiency to accelerate the discovery of AMPs, and contribute to developing effective antimicrobial therapies, especially in the face of increasing antibiotic resistance. This study introduced AMP-RNNpro based on Recurrent Neural Network (RNN), an innovative model for detecting AMPs, which was designed with eight feature encoding methods that are selected according to four criteria: amino acid compositional, grouped amino acid compositional, autocorrelation, and pseudo-amino acid compositional to represent the protein sequences for efficient identification of AMPs. In our framework, two-stage predictions have been conducted. Initially, this study analyzed 33 models on these feature extractions. Then, we selected the best six models from these models using rigorous performance metrics. In the second stage, probabilistic features have been generated from the selected six models in each feature encoding and they are aggregated to be fed into our final meta-model called AMP-RNNpro. This study also introduced 20 features with SHAP, which are crucial in the drug development fields, where we discover AAC, ASDC, and CKSAAGP features are highly impactful for detection and drug discovery. Our proposed framework, AMP-RNNpro excels in the identification of novel Amps with 97.15% accuracy, 96.48% sensitivity, and 97.87% specificity. We built a user-friendly website for demonstrating the accurate prediction of AMPs based on the proposed approach
Amp-rnnpro: A Two-stage Approach for Identification of Antimicrobials Using Probabilistic Features
(Springer Nature, 2024-06-05) Shaon, Md. Shazzad Hossain; Karim, Tasmin; Sultan, Md. Fahim; Ali, Md. Mamun; Ahmed, Kawsar; Hasan, Md. Zahid; Moustafa, Ahmed; Bui, Francis M.; Al-Zahrani, Fahad Ahmed
Antimicrobials are molecules that prevent the formation of microorganisms such as bacteria, viruses, fungi, and parasites. The necessity to detect antimicrobial peptides (AMPs) using machine learning and deep learning arises from the need for efficiency to accelerate the discovery of AMPs, and contribute to developing effective antimicrobial therapies, especially in the face of increasing antibiotic resistance. This study introduced AMP-RNNpro based on Recurrent Neural Network (RNN), an innovative model for detecting AMPs, which was designed with eight feature encoding methods that are selected according to four criteria: amino acid compositional, grouped amino acid compositional, autocorrelation, and pseudo-amino acid compositional to represent the protein sequences for efficient identification of AMPs. In our framework, two-stage predictions have been conducted. Initially, this study analyzed 33 models on these feature extractions. Then, we selected the best six models from these models using rigorous performance metrics. In the second stage, probabilistic features have been generated from the selected six models in each feature encoding and they are aggregated to be fed into our final meta-model called AMP-RNNpro. This study also introduced 20 features with SHAP, which are crucial in the drug development fields, where we discover AAC, ASDC, and CKSAAGP features are highly impactful for detection and drug discovery. Our proposed framework, AMP-RNNpro excels in the identification of novel Amps with 97.15% accuracy, 96.48% sensitivity, and 97.87% specificity. We built a user-friendly website for demonstrating the accurate prediction of AMPs based on the proposed approach which can be accessed at http://13.126.159.30/.
An Intelligent Thyroid Diagnosis System Utilising Multiple Ensemble and Explainable Algorithms with Medical Supported Attributes
(Elsevier, 2023-01-15) Sutradhar, Ananda; Al Rafi, Mustahsin; Ghosh, Pronab; Shamrat, F. M.Javed Mehedi; Moniruzzaman, Md.; Ahmed, Kawsar; Azad, AKM; Bui, Francis M.; Chen, Li; Moni, Mohammad Ali
The widespread impact of thyroid disease and its diagnosis is a challenging task for healthcare experts. The conventional technique for predicting such a vital disease is complex and time-consuming. A data-driven approach may offer predictive solutions, but it relies on all relevant attributes, which are computationally expensive. Hence, we propose a novel machine learning (ML) based disease prediction system that could potentially predict it by considering three crucial steps. First, to reduce the dimension of the dataset, three feature selection techniques were employed, including Feature Importance (FIS), Information Gain Selections (IGS), and Least Absolute Shrinkage and Selection Operator (LAS). Moreover, recommended medical references were considered while developing a feature set having the identical attributes as High-Risk Factors (HRF). Second, the models, including the Three Stage Hybrid Classifier (3SHC) and the Three Stage Hybrid Artificial Neural Network (3SHANN), are used as classifiers on the training data set. Third, a Local Interpretable Model-agnostic Explanations (LIME) to the 3SHC with the HRF samples was applied to individually explain the predictions. Then, the overall behaviors of both gender and age categories were explored with the help of a Partial Dependence Plot (PDP). Finally, the proposed system is validated with extensive experiments where the 3SHC achieves an accuracy (ACC) of 99.29%, which can play a crucial role in preventing thyroid disease and alleviating stress in the healthcare sector.
Analysis of Gene Network Model of Thyroid Disorder and Associated Diseases
(Informatics in Medicine Unlocked, Science Direct, 2020) Kawsar, Md; Taz, Tasnimul Alam; Paul, Bikash Kumar; Mahmud, Shahin; Islam, Md Manowarul; Bhuyian, Touhid; Ahmed, Kawsar
Chronic Kidney Disease (CKD), High Blood Pressure (HBP), and Thyroid Disorder (TD) diseases are interrelated. When human patients are affected by one of them, then the possibility of affectness by the other two diseases is increased. Background studies indicate that there are large numbers of similar biological and genetic features among HBP, CKD, and TD. For this reason, the common gene network models among these three diseases are explored. The gene number is reduced through preprocessing and filtering. Then the common genes among the selected diseases and the most significant genes are explored. After completing this process, ten common genes among HBP, CKD, and TD are recognized. This analysis identifies the most significant hub proteins based on biological, biochemical, and genetic relationships between common genes. Following these relationships, the Protein-Protein Interactions network, Co-Expression network, Enrichment Analysis, Topological properties analysis, Gene regulatory network, and Physical Interaction network are exhibited. This analysis helps us to identify similar biological and genetic features among HBP, CKD, and TD. Interaction of proteins with drug molecules enables an efficient drug design for this research. These drugs can be considered for further verification by chemical experiments.
Analysis of Optical Sensitivity of Analytes in Aqua Solutions
(Optik, Elsevier, 2019-02) Ahmed, Kawsar; Islam, Md. Ibadul; Jabin, Md. Asaduzzaman; Baha, Ali Newaz; Rajan, M.S.Mani; M.Suganthy; Bikash Kumar, Paul
This proposed structure depicts Photonic crystal fiber (PCF) including hexagonal-core for liquid sensitivity. The pretending result shows that the suggested H-PCF obtains the higher level of relative sensitivity using Finite-element-Method (FEM). At wavelength 1330 nm for liquid Ethanol (n = 1.354) having the high relative sensitivity of 76.78%; low confinement loss of 6.94 × 10−12 dB/m and the effective area of 7.04 μm2 are achieved under the Water (H2O), Ethanol (C2H5OH) and Benzyne analytes (C6H5-X, X = H, OH, CH3).
Analysis of optical sensitivity of analytes in aqua solutions
(2018-10-03) Suganthy, M; Paul, Bikash Kumar; Ahmed, Kawsar; Islam, Md. Ibadul; Jabin, Md. Asaduzzaman; Bahar, Ali Newaz; Rajan, M.S.Mani
This proposed structure depicts Photonic crystal fiber (PCF) including hexagonal-core for liquid sensitivity. The pretending result shows that the suggested H-PCF obtains the higher level of relative sensitivity using Finite-element-Method (FEM). At wavelength 1330 nm for liquid Ethanol (n = 1.354) having the high relative sensitivity of 76.78%; low confinement loss of 6.94 × 10−12 dB/m and the effective area of 7.04 μm2are achieved under the Water (H2O), Ethanol (C2H5OH) and Benzyne analytes (C6H5-X, X = H, OH, CH3).
Analysis of Terahertz Waveguide Properties of Q-PCF Based on FEM Scheme
(Optical Materials, 2020) Paul, Bikash Kumar; Ahmed, Kawsar
A novel photonic crystal fiber in quasi pattern (Q-PCF) is designed and proposed for terahertz wave (T-wave) guidance. A cyclic olefin copolymer (COC) has been used as the bulk material of the proposed fiber. The proposed Q-PCF is introduced here for T-wave propagation, scaling from 0.8 to 2.40 THz. It is noted that, a low effective material loss (EML) of 0.038 cm−1 is obtained at working frequency f = 1 THz. It leads to a reduction in bulk absorption loss of 81% at the same working frequency. Moreover, some essential optical guiding parameters such as confinement loss, dispersion, modality, power fraction and effective mode area are also examined briefly. The fabrication of the proposed PCF is simple as it contains only air holes of circular shape. It is anticipated that the mentioned Q-PCF terahertz waveguide will provide a significant improvement over existing designs.
Analysis of Topological Properties and Drug Discovery for Bipolar Disorder and Associated Diseases
(Cellular and molecular biology, 2020) Shadhin, Khairul Alam; Hasan, Md. Rakibul; Paul, Bikash Kumar; Ahmed, Kawsar; Bhuyian, Touhid
With the advancement and development of sophisticated bioinformatics tools, the area of computational bioinformatics and systems biology analysis is expanding day by day. The bipolar or manic-depressive disorder might be characterized as one of the most crippling mental problems that affect the people of early age and grown-ups. The objective of the present study was to investigate the association between genetic mutations in the four above listed diseases and to create a Protein-protein interaction (PPI) network or common pathways. Firstly, we need to find out the genetic relationship between them. Thus it will help us to understand the genetic association between them and help to develop the drug design for all the diseases. Genes responsible for these diseases are gathered, pre-processed, processed and mining using python scripts. This exploration is expected to carry out further measurements in the field of drug structure and also contributes to the biological and biomedical sectors.
Analyzing the Protein-protein Interaction Network and the Topological Properties of Prostate Cancer and Allied Diseases
(Gene Reports, Science Direct, Elsevier, 2020) Puspo, Nadira Akter; Akter, Laboni; Siddique, Sinthia; Paul, Bikash Kumar; Ahmed, Kawsar; Bhuiyan, Touhid; Islam, Md Kabirul
Background and objectives Some of cancer diseases are related to each other by their metabolic structures. Literature reviews show that Prostate Cancer (PC), Breast Cancer (BC), Bladder Cancer (BDC) and Colorectal Cancer (CRC) are related. Some are shown up for affected family background in their early or grown-up age. Materials and methods Python programming language is used for data mining, pre-processing and sorting and finding common genes from gathered data whose are collected National Centre of Biotechnology Information (NCBI). Protein-Protein Interaction (PPIs) and Protein Disease Interaction (PDI) are displayed by using bioinformatics technology. We use identified hub genes for making co-expression and physical interaction. Results Interactions for selected top 8 genes are exhibited following different bioinformatics tools. The gene-miRNA interaction generates interactions with a total of 651 links between 8 genes. Where, the TF-gene Interaction creates relationships between 176 nodes and 278 edges. There are 6 seed nodes. Besides, PDI represents a subnetwork which creates relationships between 47 nodes and 46 edges. There are 1 seed nodes. In addition, PCI creates relationships between 1437 nodes and 2165 edges. There are 7 seed nodes. Furthermore, GDA creates relationships between 235 nodes and 272 edges. There are 5 seed nodes. Conclusion This study will be helpful for further studies of different bioinformatics tools for designing gene network models and drugs design. These drugs can be considered for further verification by chemical experiments.