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Fuzzy C means Based Evaluation Algorithms For Cancer Gene Expression Data Clustering
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The influx of data in bioinformatics is primarily in the form of DNA, RNA, and protein sequences. This condition places a significant burden on scientists and computers. Some genomics studies depend on clustering techniques to group similarly expressed genes into one cluster. Clustering is a type of unsupervised learning that can be used to divide unknown cluster data into clusters. The k-means and fuzzy c-means (FCM) algorithms are examples of algorithms that can be used for clustering. Consequently, clustering is a common approach that divides an input space into several homogeneous zones; it can be achieved using a variety of algorithms. This study used three models to cluster a brain tumor dataset. The first model uses FCM, which is used to cluster genes. FCM allows an object to belong to two or more clusters with a membership grade between zero and one and the sum of belonging to all clusters of each gene is equal to one. This paradigm is useful when dealing with microarray data. The total time required to implement the first model is 22.2589 s. The second model combines FCM and particle swarm optimization (PSO) to obtain better results. The hybrid algorithm, i.e., FCM–PSO, uses the DB index as objective function. The experimental results show that the proposed hybrid FCM–PSO method is effective. The total time of implementation of this model is 89.6087 s. The third model combines FCM with a genetic algorithm (GA) to obtain better results. This hybrid algorithm also uses the DB index as objective function. The experimental results show that the proposed hybrid FCM–GA method is effective. Its total time of implementation is 50.8021 s. In addition, this study uses cluster validity indexes to determine the best partitioning for the underlying data. Internal validity indexes include the Jaccard, Davies Bouldin, Dunn, Xie–Beni, and silhouette. Meanwhile, external validity indexes include Minkowski, adjusted Rand, and percentage of correctly categorized pairings. Experiments conducted on brain tumor gene expression data demonstrate that the techniques used in this study outperform traditional models in terms of stability and biological significance.

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Publication Date
Wed Oct 27 2021
Journal Name
Plants
Natural Biostimulant Attenuates Salinity Stress Effects in Chili Pepper by Remodeling Antioxidant, Ion, and Phytohormone Balances, and Augments Gene Expression
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A biostimulant is any microorganism or substance used to enhance the efficiency of nutrition, tolerance to abiotic stress and/or quality traits of crops, depending on its contents from nutrients. Plant biostimulants like honey bee (HB) and silymarin (Sm) are a strategic trend for managing stressed crops by promoting nutritional and hormonal balance, regulating osmotic protectors, antioxidants, and genetic potential, reflecting plant growth and productivity. We applied diluted honey bee (HB) and silymarin-enriched honey bee (HB- Sm) as foliar nourishment to investigate their improving influences on growth, yield, nutritional and hormonal balance, various osmoprotectant levels, different components of antioxidant system, and genetic p

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Publication Date
Mon Mar 31 2025
Journal Name
The Iraqi Geological Journal
Evaluation of Machine Learning Techniques for Missing Well Log Data in Buzurgan Oil Field: A Case Study
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The investigation of machine learning techniques for addressing missing well-log data has garnered considerable interest recently, especially as the oil and gas sector pursues novel approaches to improve data interpretation and reservoir characterization. Conversely, for wells that have been in operation for several years, conventional measurement techniques frequently encounter challenges related to availability, including the lack of well-log data, cost considerations, and precision issues. This study's objective is to enhance reservoir characterization by automating well-log creation using machine-learning techniques. Among the methods are multi-resolution graph-based clustering and the similarity threshold method. By using cutti

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Publication Date
Fri Jan 24 2020
Journal Name
Petroleum And Coal
Evaluation of Geomechanical Properties for Tight Reservoir Using Uniaxial Compressive Test, Ultrasonic Test, and Well Logs Data
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Tight reservoirs have attracted the interest of the oil industry in recent years according to its significant impact on the global oil product. Several challenges are present when producing from these reservoirs due to its low to extra low permeability and very narrow pore throat radius. Development strategy selection for these reservoirs such as horizontal well placement, hydraulic fracture design, well completion, and smart production program, wellbore stability all need accurate characterizations of geomechanical parameters for these reservoirs. Geomechanical properties, including uniaxial compressive strength (UCS), static Young’s modulus (Es), and Poisson’s ratio (υs), were measured experimentally using both static and dynamic met

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Publication Date
Wed Jan 01 2020
Journal Name
Petroleum And Coal
Evaluation of geomechanical properties for tight reservoir using uniaxial compressive test, ultrasonic test, and well logs data
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Publication Date
Fri Dec 01 2023
Journal Name
Advances In Science And Technology Research Journal
Experimental Investigation and Fuzzy Based Prediction of Titanium Alloy Performance During Drilling Process
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Publication Date
Fri Feb 13 2026
Journal Name
Journal Of The College Of Basic Education
Fuzzy Nonparametric Regression Model Estimation Based on some Smoothing Techniques With Practical Application
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In this research, we use fuzzy nonparametric methods based on some smoothing techniques, were applied to real data on the Iraqi stock market especially the data about Baghdad company for soft drinks for the year (2016) for the period (1/1/2016-31/12/2016) .A sample of (148) observations was obtained in order to construct a model of the relationship between the stock prices (Low, high, modal) and the traded value by comparing the results of the criterion (G.O.F.) for three techniques , we note that the lowest value for this criterion was for the K-Nearest Neighbor at Gaussian function .

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Publication Date
Thu Jul 14 2022
Journal Name
Egyptian Journal Of Medical Human Genetics
A single-nucleotide polymorphism of IL12A gene (rs582537 A/C/G) and susceptibility to chronic hepatitis B virus infection among Iraqi patients
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A case–control study (80 patients with chronic hepatitis B virus [HBV] infection and 96 controls) was performed to evaluate the association of an IL12A gene variant (rs582537 A/C/G) with HBV infection. Allele G showed a signifcantly lower frequency in patients compared to controls (31.2 vs. 46.9%; probability [p]=0.009; corrected p [pc]=0.027) and was associated with a lower risk of HBV infection (odds ratio [OR]=0.49; 95% confdence interval [CI]=0.29–0.83). A similar lower risk was associated with genotypes CG (17.5 vs. 29.2; OR=0.25; 95% CI=0.08–0.81; p=0.02) and GG (10.0 vs. 16.7; OR=0.25; 95% CI=0.07–0.91; p=0.036), but the pc value was not signifcant (0.12 and 0.126, respec‑ tively). Serum IL35 levels showed signifcant difere

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Publication Date
Tue Mar 03 2020
Journal Name
Molecular Biology Reports
Correlation of GSTP1 gene variants of male Iraqi waterpipe (Hookah) tobacco smokers and the risk of lung cancer
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Glutathione-S-transferases (GSTs) play a role in the detoxification of environmental chemicals and mutagens, such as those inhaled during tobacco smoking. There have been conflicting reports concerning GST polymorphisms as risk factors in the development of lung cancer. No studies focused on Arab populations exposed to Waterpipe (WP) tobacco smoke have been undertaken. Here Polymerase Chain Reaction-Restriction Fragment Length Polymorphism (PCR-RFLP) and gene sequenc- ing were applied to analyze allelic variations in GSTP1-rs1695 and -rs1138272 amongst 123 lung cancer patients and 129 controls. The data suggest that WP smoking raised the risk of lung cancer more than three-fold (OR 3.6; 95% CI 2.1–6.0; p < 0.0001). However, there was no s

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Publication Date
Fri Jun 30 2017
Journal Name
International Journal Of Medical Research & Health Sciences
FLI1 Expression in Breast Cancer Cell Lines and Primary Breast Carcinomas is Correlated with ER, PR and HER2
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FLI1 is a member of ETS family of transcription factors that regulate a variety of normal biologic activities including cell proliferation, differentiation, and apoptosis. The expression of FLI1 and its correlation with well-known breast cancer prognostic markers (ER, PR and HER2) was determined in primary breast tumors as well as four breast cancer lines including: MCF-7, T47D, MDA-MB-231 and MDA-MB-468 using RT-qPCR with either 18S rRNA or ACTB (β-actin) for normalization of data. FLI1 mRNA level was decreased in the breast cancer cell lines under study compared to the normal breast tissue; however, Jurkat cells, which were used as a positive control, showed overexpression compared to the normal breast. Regarding primary breast carcinoma

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Publication Date
Fri Apr 07 2017
Journal Name
Oncology Letters
AURKA mRNA expression is an independent predictor of poor prognosis in patients with non-small cell lung cancer
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