Search

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. A non-invasive fetal electrocardiogram (FECG) is used to monitor the electrical pulse of the fetal heart. Decomposing the FECG signal from the maternal ECG (MECG) is a blind source separation problem, which is hard due to the low amplitude of the FECG, the overlap of R waves, and the potential exposure to noise from different sources. Traditional decomposition techniques, such as adaptive filters, require tuning, alignment, or pre-configuration, such as modeling the noise or desired signal to map the MECG to the FECG. The high correlation between maternal and fetal ECG fragments decreases the performance of convolution layers. Therefore, the masking region of interest based on the attention mechanism was performed to improve the signal generators' precision. The sine activation function was also used to retain more details when converting two signal domains. Three available datasets from the Physionet, including the A&D FECG, NI-FECG, and NI-FECG challenge, and one synthetic dataset using FECGSYN toolbox, were used to evaluate the performance. The proposed method could map an abdominal MECG to a scalp FECG with an average of 98% R-Square [CI 95%: 97%, 99%] as the goodness of fit on the A&D FECG dataset. Moreover, it achieved 99.7% F1-score [CI 95%: 97.8-99.9], 99.6% F1-score [CI 95%: 98.2%, 99.9%] and 99.3% F1-score [CI 95%: 95.3%, 99.9%] for fetal QRS detection on the A&D FECG, NI-FECG and NI-FECG challenge datasets, respectively. Also, the distortion was in the “very good” and “good” ranges. These results are comparable to the state-of-the-art results; thus, the proposed algorithm has the potential to be used for high-performance signal-to-signal conversion.

Diabetic nephropathy (DN), the leading cause of end-stage renal disease, has become a massive global health burden. Despite considerable efforts, the underlying mechanisms have not yet been comprehensively understood. In this study, a systematic approach was utilized to identify the microRNA signature in DN and to introduce novel drug targets (DTs) in DN. Using microarray profiling followed by qPCR confirmation, 13 and 6 differentially expressed (DE) microRNAs were identified in the kidney cortex and medulla, respectively. The microRNA-target interaction networks for each anatomical compartment were constructed and central nodes were identified. Moreover, enrichment analysis was performed to identify key signaling pathways. To develop a strategy for DT prediction, the human proteome was annotated with 65 biochemical characteristics and 23 network topology parameters. Furthermore, all proteins targeted by at least one FDA-approved drug were identified. Next, mGMDH-AFS, a high-performance machine learning algorithm capable of tolerating massive imbalanced size of the classes, was developed to classify DT and non-DT proteins. The sensitivity, specificity, accuracy, and precision of the proposed method were 90%, 86%, 88%, and 89%, respectively. Moreover, it significantly outperformed the state-of-the-art (P-value ≤ 0.05) and showed very good diagnostic accuracy and high agreement between predicted and observed class labels. The cortex and medulla networks were then analyzed with this validated machine to identify potential DTs. Among the high-rank DT candidates are Egfr, Prkce, clic5, Kit, and Agtr1a which is a current well-known target in DN. In conclusion, a combination of experimental and computational approaches was exploited to provide a holistic insight into the disorder for introducing novel therapeutic targets.

Coronavirus disease-2019, also known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was a disaster in 2020. Accurate and early diagnosis of coronavirus disease-2019 (COVID-19) is still essential for health policymaking. Reverse transcriptase-polymerase chain reaction (RT-PCR) has been performed as the operational gold standard for COVID-19 diagnosis. We aimed to design and implement a reliable COVID-19 diagnosis method to provide the risk of infection using demographics, symptoms and signs, blood markers, and family history of diseases to have excellent agreement with the results obtained by the RT-PCR and CT-scan. Our study primarily used sample data from a 1-year hospital-based prospective COVID-19 open-cohort, the Khorshid COVID Cohort (KCC) study. A sample of 634 patients with COVID-19 and 118 patients with pneumonia with similar characteristics whose RT-PCR and chest CT scan were negative (as the control group) (dataset 1) was used to design the system and for internal validation. Two other online datasets, namely, some symptoms (dataset 2) and blood tests (dataset 3), were also analyzed. A combination of one-hot encoding, stability feature selection, over-sampling, and an ensemble classifier was used. Ten-fold stratified cross-validation was performed. In addition to gender and symptom duration, signs and symptoms, blood biomarkers, and comorbidities were selected. Performance indices of the cross-validated confusion matrix for dataset 1 were as follows: sensitivity of 96% [confidence interval, CI, 95%: 94–98], specificity of 95% [90–99], positive predictive value (PPV) of 99% [98–100], negative predictive value (NPV) of 82% [76–89], diagnostic odds ratio (DOR) of 496 [198–1,245], area under the ROC (AUC) of 0.96 [0.94–0.97], Matthews Correlation Coefficient (MCC) of 0.87 [0.85–0.88], accuracy of 96% [94–98], and Cohen's Kappa of 0.86 [0.81–0.91]. The proposed algorithm showed excellent diagnosis accuracy and class-labeling agreement, and fair discriminant power. The AUC on the datasets 2 and 3 was 0.97 [0.96–0.98] and 0.92 [0.91–0.94], respectively. The most important feature was white blood cell count, shortness of breath, and C-reactive protein for datasets 1, 2, and 3, respectively. The proposed algorithm is, thus, a promising COVID-19 diagnosis method, which could be an amendment to simple blood tests and screening of symptoms. However, the RT-PCR and chest CT-scan, performed as the gold standard, are not 100% accurate.

Southern Iran is one of the most significant parts of the world in terms of economic aggregation of hydrocarbons. In this paper, reservoir quality of the Cenomanian–Santonian deposits has been evaluated in southwestern Iran. According to the reservoir qualification, five particular rock types have been classified based on log responses through K-nearest neighbors (KNN) method. 1–5 are representing poor, weak, moderate, superior and non-reservoir types of rock, respectively. Rock types 3 and 4 are determined as the best types which can play a suitable reservoir role in hydrocarbon accumulation. The mentioned groups generally consist of porous limestones with low level of clay content. Rock types number 1 and 2 indicate the low porosity tight limestones and rock type 5 belongs to clays and shaly layers with high gamma radiations. Lithology of these intervals is specified using Rhomaa–Umaa and NPHI–RHOB crossplots which displayed calcite mineral is the main mineral formed both formations. SEM analysis and Th–K crossplot confirmed that Illite, Montmorillonite and Kaolinite are major clay minerals settled in these intervals that may have impact on reservoir characteristics. Interparticle and micro-porosities are considered as dominant kinds of porosities based on velocity deviation log (VDL) and it is proved by SEM results. Rock types 3 and 4 have been considered as the most productive zones which can be the best target for future perforating operation. According to petrophysical evaluation results, Ilam (Santonian) reservoir quality is enhanced toward northeast and southwest (Maximum abundance of rock type 4) of the studied area in comparison to central part. The Sarvak (Cenomanian–Turonian) Formation is in the highest level of reservoir quality in the southwest of the studied area (abundance of rock types 4 and 3).

Background: Developing simplified risk assessment model based on non-laboratory risk factors that could determine cardiovascular risk as accurately as laboratory-based one can be valuable, particularly in developing countries where there are limited resources. Objective: To develop a simplified non-laboratory cardiovascular disease risk assessment chart based on previously reported laboratory-based chart and evaluate internal and external validation, and recalibration of both risk models to assess the performance of risk scoring tools in other population. Methods: A 10-year non-laboratory-based risk prediction chart was developed for fatal and non-fatal CVD using Cox Proportional Hazard regression. Data from the Isfahan Cohort Study (ICS), a population-based study among 6504 adults aged = 35 years, followed-up for at least ten years was used for the non-laboratory-based model derivation. Participants were followed up until the occurrence of CVD events. Tehran Lipid and Glucose Study (TLGS) data was used to evaluate the external validity of both non-laboratory and laboratory risk assessment models in other populations rather than one used in the model derivation. Results: The discrimination and calibration analysis of the non-laboratory model showed the following values of Harrell’s C: 0.73 (95% CI 0.71–0.74), and Nam-D’Agostino ¿2:11.01 (p = 0.27), respectively. The non-laboratory model was in agreement and classified high risk and low risk patients as accurately as the laboratory one. Both non-laboratory and laboratory risk prediction models showed good discrimination in the external validation, with Harrell’s C of 0.77 (95% CI 0.75–0.78) and 0.78 (95% CI 0.76–0.79), respectively. Conclusions: Our simplified risk assessment model based on non-laboratory risk factors could determine cardiovascular risk as accurately as laboratory-based one. This approach can provide simple risk assessment tool where laboratory testing is unavailable, inconvenient, and costly. The baseline survey was supported by the undersecretary of research of the ministry of health and Isfahan cardiovascular research center, cardiovascular research institute affiliated to Isfahan University of Medical Sciences [grant number 31309304]. This work was also supported by the People Programme (Marie Curie Actions) of the European Union Seventh Framework Programme (FP7/2007–2013) [grant number 600388] and from the Agency for Business Competitiveness of the Government of Catalonia, ACC1Ó.

Identifying the possible factors of psychiatric symptoms among children can reduce the risk of adverse psychosocial outcomes in adulthood. We designed a classification tool to examine the association between modifiable risk factors and psychiatric symptoms, defined based on the Persian version of the WHO-GSHS questionnaire in a developing country. Ten thousand three hundred fifty students, aged 6–18 years from all Iran provinces, participated in this study. We used feature discretization and encoding, stability selection, and regularized group method of data handling (GMDH) to classify the a priori specific factors (e.g., demographic, sleeping-time, life satisfaction, and birth-weight) to psychiatric symptoms. Self-rated health was the most critical feature. The selected modifiable factors were eating breakfast, screentime, salty snack for depression symptom, physical activity, salty snack for worriedness symptom, (abdominal) obesity, sweetened beverage, and sleep-hour for mild-to-moderate emotional symptoms. The area under the ROC curve of the GMDH was 0.75 (CI 95% 0.73–0.76) for the analyzed psychiatric symptoms using threefold cross-validation. It significantly outperformed the state-of-the-art (adjusted p¿

Early diagnosis of psychiatric disorders among children can reduce the risk of adverse psychosocial outcomes in adulthood. We aimed to design a computer-aided screening tool to examine the association between modifiable risk factors and psychiatric disorders in a developing country. Ten thousand three hundred fifty students, aged 6–18 years from all Iran provinces, participated in this study. We used feature discretization and encoding, stability selection, and regularized group method of data handling (GMDH) to classify the comprehensive risk factors to depression (the prevalence of 20.1%), worriedness (23.7%), and emotional problems (11.1%). Self-rated health was the most important feature. The selected modifiable factors were eating breakfast, screentime, salty snack for depression, physical activity, salty snack for worriedness, (abdominal) obesity, sweetened beverage, and sleep-hour for emotional problems classification. The area under the ROC Curve (AUC) of the GMDH was 0.88 [CI 95%: 0.87-0.89], 0.79 [0.77-0.80], and 0.70 [0.68-0.72] for depression, worriedness, and emotional problem outcomes, respectively. The GMDH provided a deep interaction network to introduce important features that univariate modeling had not identified. It significantly outperformed the state-of-the-art (adjusted p

Non-invasive determination of leaf nitrogen (N) and water contents is essential for ensuring the healthy growth of the plants. However, most of the existing methods to measure them are expensive. In this paper, a low-cost, portable multispectral sensor system is proposed to determine N and water contents in the leaves, non-invasively. Four different species of plants&mdash, canola, corn, soybean, and wheat&mdash, are used as test plants to investigate the utility of the proposed device. The sensor system comprises two multispectral sensors, visible (VIS) and near-infrared (NIR), detecting reflectance at 12 wavelengths (six from each sensor). Two separate experiments were performed in a controlled greenhouse environment, including N and water experiments. Spectral data were collected from 307 leaves (121 for N and 186 for water experiment), and the rational quadratic Gaussian process regression (GPR) algorithm was applied to correlate the reflectance data with actual N and water content. By performing five-fold cross-validation, the N estimation showed a coefficient of determination () of 63.91% for canola, 80.05% for corn, 82.29% for soybean, and 63.21% for wheat. For water content estimation, canola showed an of 18.02%, corn showed an of 68.41%, soybean showed an of 46.38%, and wheat showed an of 64.58%. The result reveals that the proposed low-cost sensor with an appropriate regression model can be used to determine N content. However, further investigation is needed to improve the water estimation results using the proposed device.

Ascertaining the pore connection criterion has always been a challenge for geoscientists. In the framework of this research, an attempt was made to study the pore connectivity in a heterogeneous carbonate rock by combining several techniques. Pore types and rock facies were investigated for 321 thin sections. The abundance of connected porosities, cementation type and intensity which significantly control the matrix pore connectivity were specified on thin sections and finally a connectivity log was extracted that showed an accurate result on poro-perm cross-plots. Most of the porosity volumes were related to interparticle, vuggy and moldic porosities which are microscale. The velocity deviation log, estimated in four wells, indicates the presence of micro-porosity, which is consistent with the results of thin-sections. Zero velocity deviation is usually associated with the predominant presence of micro-pores. Different permeability values in identical lithological facies, as well as micro-fracture markers, prompted us to conduct more extensive research on fractures. FMI analysis proved that the studied reservoir is intensively fractured. The recorded high permeability in a sample with low matrix connectivity is specifically a fracture indication. Fracture apertures vary from microscale (

Dyslipidemia, the disorder of lipoprotein metabolism resulting in high lipid profile, is an important modifiable risk factor for coronary heart diseases. It is associated with more than four million worldwide deaths per year. Half of the children with dyslipidemia have hyperlipidemia during adulthood, and its prediction and screening are thus critical. We designed a new dyslipidemia diagnosis system. The sample size of 725 subjects (age 14.66 ± 2.61 years; 48% male; dyslipidemia prevalence of 42%) was selected by multistage random cluster sampling in Iran. Single nucleotide polymorphisms (rs1801177, rs708272, rs320, rs328, rs2066718, rs2230808, rs5880, rs5128, rs2893157, rs662799, and Apolipoprotein-E2/E3/E4), and anthropometric, life-style attributes, and family history of diseases were analyzed. A framework for classifying mixed-type data in imbalanced datasets was proposed. It included internal feature mapping and selection, re-sampling, optimized group method of data handling using convex and stochastic optimizations, a new cost function for imbalanced data and an internal validation. Its performance was assessed using hold-out and 4-foldcross-validation. Four other classifiers namely as supported vector machines, decision tree, and multilayer perceptron neural network and multiple logistic regression were also used. The average sensitivity, specificity, precision and accuracy of the proposed system were 93%, 94%, 94% and 92%, respectively in cross validation. It significantly outperformed the other classifiers and also showed excellent agreement and high correlation with the gold standard. A non-invasive economical version of the algorithm was also implemented suitable for low- and middle-income countries. It is thus a promising new tool for the prediction of dyslipidemia., Highlights • Dyslipidemia is an important modifiable risk factor for coronary heart diseases. • Its prediction and screening are critical in children and adolescents. • We designed a new dyslipidemia diagnosis system as a new classification framework. • Gene mutations, genetic, and anthropometric attributes were analyzed. • The proposed framework showed excellent agreement with the gold standard.

Automatic classification of seismic reflection data plays an important role in reservoir characterization and petroleum geosciences. Recently, the relevance vector machines (RVM) have attracted substantial interest in data classification literature. The RVM is a Bayesian variant of support vector machine (SVM) that can surmount the limitations of other conventional methods. The main purpose of this study is to show the effectiveness of RVM as a linear classifier to classify the variation in seismic data. In this study, we explored the potentials of RVM in identifying anomalous seismic reflection signals. First, the efficiency of RVM technique is compared with other conventional methods (SVM and probabilistic neural network (PNN)), and then a multi-class RVM algorithm based on the Gaussian approximation model is used to successfully classify synthetic and field seismic data. The result of this study shows that RVM is successful in the determination of gas-bearing zone in one of the Iranian southern oil fields.

A robust methodology to determine geological facies in oil and gas fields is the integration of seismic attributes and well data to estimate flow zone indicator (FZI). Recently, the application of fuzzy and Neuro-Fuzzy approach regarding this purpose has enjoyed an increasing attention. The current study was carried out in the Surmeh (Arab) formation at Persian Gulf basin, Southern Iran. A Nero-fuzzy system was applied to estimate FZI cube from seismic attributes. To do so, core data and seismic data from four wells were imported to ANFIS system. Subsequently, the outcomes were compared with those of probabilistic neural network (PNN). Finally, a fuzzy C-Means clustering (FCM) technique was applied to characterize different hydraulic flow units (HFUs). The results of this study demonstrate that adaptive neuro-fuzzy inference systems (ANFIS) turn out to be successful in modeling FZI from seismic attributes and well data for a faraway well location. Moreover, the results achieved suggest that using the FCM technique is an efficient methodology to determine different HFUs from FZI cube.

Despite the progress in understanding of neural codes, the studies of the cortico-muscular coupling still largely rely on interferential electromyographic (EMG) signal or its rectification for the assessment of motor neuron pool behavior. This assessment is non-trivial and should be used with precaution. Direct analysis of neural codes by decomposing the EMG, also known as neural decoding, is an alternative to EMG amplitude estimation. In this study, we propose a fully-deterministic hybrid surface EMG (sEMG) decomposition approach that combines the advantages of both template-based and Blind Source Separation (BSS) decomposition approaches, a.k.a. guided source separation (GSS), to identify motor unit (MU) firing patterns. We use the single-pass density-based clustering algorithm to identify possible cluster representatives in different sEMG channels. These cluster representatives are then used as initial points of modified gradient Convolution Kernel Compensation (gCKC) algorithm. Afterwards, we use the Kalman filter to reduce the noise impact and increase convergence rate of MU filter identification by gCKC. Moreover, we designed an adaptive soft-thresholding method to identify MU firing times out of estimated MU spike trains. We tested the proposed algorithm on a set of synthetic sEMG signals with known MU firing patterns. A grid of 9 × 10 monopolar surface electrodes with 5-mm inter-electrode distances in both directions was simulated. Muscle excitation was set to 10, 30, and 50%. Colored Gaussian zero-mean noise with the signal-to-noise ratio (SNR) of 10, 20, and 30 dB, respectively, was added to 16 s long sEMG signals that were sampled at 4,096 Hz. Overall, 45 simulated signals were analyzed. Our decomposition approach was compared with gCKC algorithm. Overall, in our algorithm, the average numbers of identified MUs and Rate-of-Agreement (RoA) were 16.41 ± 4.18 MUs and 84.00 ± 0.06%, respectively, whereas the gCKC identified 12.10 ± 2.32 MUs with the average RoA of 90.78 ± 0.08%. Therefore, the proposed GSS method identified more MUs than the gCKC, with comparable performance. Its performance was dependent on the signal quality but not the signal complexity at different force levels. The proposed algorithm is a promising new offline tool in clinical neurophysiology

This study was designed to develop a risk assessment chart for the clinical management and prevention of the risk of cardiovascular disease (CVD) in Iranian population, which is vital for developing national prevention programs. The Isfahan Cohort Study (ICS) is a population-based prospective study of 6504 Iranian adults ≥35 years old, followed-up for ten years, from 2001 to 2010. Behavioral and cardiometabolic risk factors were examined every five years, while biennial follow-ups for the occurrence of the events was performed by phone calls or by verbal autopsy. Among these participants, 5432 (2784 women, 51.3%) were CVD free at baseline examination and had at least one follow-up. Cox proportional hazard regression was used to predict the risk of ischemic CVD events, including sudden cardiac death due to unstable angina, myocardial infarction, and stroke. The model fit statistics such as area under the receiver-operating characteristic (AUROC), calibration chi-square and the overall bias were used to assess the model performance. We also tested the Framingham model for comparison. Seven hundred and five CVD events occurred during 49452.8 person-years of follow-up. The event probabilities were calculated and presented color-coded on each gender-specific PARS chart. The AUROC and Harrell's C indices were 0.74 (95% CI, 0.72-0.76) and 0.73, respectively. In the calibration, the Nam-D'Agostino χ2 was 10.82 (p = 0.29). The overall bias of the proposed model was 95.60%. PARS model was also internally validated using cross-validation. The Android app and the Web-based risk assessment tool were also developed as to have an impact on public health. In comparison, the refitted and recalibrated Framingham models, estimated the CVD incidence with the overall bias of 149.60% and 128.23% for men, and 222.70% and 176.07% for women, respectively. In conclusion, the PARS risk assessment chart is a simple, accurate, and well-calibrated tool for predicting a 10-year risk of CVD occurrence in Iranian population and can be used in an attempt to develop national guidelines for the CVD management.

In this protocol, we aimed to show the step-by.step procedures used to develop, evaluate and validate (CVD) risk charts.

This study was designed to develop a risk assessment chart for the clinical management and prevention of the risk of cardiovascular disease (CVD) in Iranian population, which is vital for developing national prevention programs. The Isfahan Cohort Study (ICS) is a popu- lation-based prospective study of 6504 Iranian adults 35 years old, followed-up for ten years, from 2001 to 2010. Behavioral and cardiometabolic risk factors were examined every five years, while biennial follow-ups for the occurrence of the events was performed by phone calls or by verbal autopsy. Among these participants, 5432 (2784 women, 51.3%) were CVD free at baseline examination and had at least one follow-up. Cox proportional hazard regression was used to predict the risk of ischemic CVD events, including sudden cardiac death due to unstable angina, myocardial infarction, and stroke. The model fit statis- tics such as area under the receiver-operating characteristic (AUROC), calibration chi- square and the overall bias were used to assess the model performance. We also tested the Framingham model for comparison. Seven hundred and five CVD events occurred during 49452.8 person-years of follow-up. The event probabilities were calculated and presented color-coded on each gender-specific PARS chart. The AUROC and Harrell’s C indices were 0.74 (95% CI, 0.72–0.76) and 0.73, respectively. In the calibration, the Nam-D’Ago stino ¿ 2 was 10.82 (p = 0.29). The overall bias of the proposed model was 95.60%. PARS model was also internally validated using cross-validation. The Android app and the Web-based risk assessment tool were also developed as to have an impact on public health. In compari- son, the refitted and recalibrated Framingham models, estimated the CVD incidence with the overall bias of 149.60% and 128.23% for men, and 222.70% and 176.07% for women, respectively. In conclusion, the PARS risk assessment chart is a simple, accurate, and well- calibrated tool for predicting a 10-year risk of CVD occurrence in Iranian population and can be used in an attempt to develop national guidelines for the CVD management .

Cancer is a collection of diseases that involves growing abnormal cells with the potential to invade or spread to the body. Breast cancer is the second leading cause of cancer death among women. A method for 5-year breast cancer recurrence prediction is presented in this manuscript. Clinicopathologic characteristics of 579 breast cancer patients (recurrence prevalence of 19.3%) were analyzed and discriminative features were selected using statistical feature selection methods. They were further refined by Particle Swarm Optimization (PSO) as the inputs of the classification system with ensemble learning (Bagged Decision Tree: BDT). The proper combination of selected categorical features and also the weight (importance) of the selected interval-measurement-scale features were identified by the PSO algorithm. The performance of HPBCR (hybrid predictor of breast cancer recurrence) was assessed using the holdout and 4-fold cross-validation. Three other classifiers namely as supported vector machines, DT, and multilayer perceptron neural network were used for comparison. The selected features were diagnosis age, tumor size, lymph node involvement ratio, number of involved axillary lymph nodes, progesterone receptor expression, having hormone therapy and type of surgery. The minimum sensitivity, specificity, precision and accuracy of HPBCR were 77%, 93%, 95% and 85%, respectively in the entire cross-validation folds and the hold-out test fold. HPBCR outperformed the other tested classifiers. It showed excellent agreement with the gold standard (i.e. the oncologist opinion after blood tumor marker and imaging tests, and tissue biopsy). This algorithm is thus a promising online tool for the prediction of breast cancer recurrence., Highlights • Breast cancer is the second leading cause of cancer death among women. • Recurrence prediction could be used by clinicians to make proper treatment plan. • A hybrid prognosis tool with optimized ensemble learning is proposed in this paper. • 579 breast cancer patients (with the recurrence prevalence of 19.3%) were analyzed. • The average accuracy and precision were 89.2 ± 0.6% and 96.5 ± 1.0%, respectively.

Many studies have used the microarray gene expression data in order to classifying breast cancer subtypes. However, the classification accuracy was not acceptable in many cases even by applying the algorithms to only a single set of data. In this regard, using appropriate algorithm in every step of whole procedure, applying useful bioinformatics databases, considering the interaction among genes, and properly combining analytical steps are the main challenging problems. In this study a solution was proposed which followed a three step process. In the first step a filter feature selection method was used to produce a small set of informative genes. In the second step, the primary selected genes were mapped on the protein-protein interaction network to extend the gene set according to the linking among corresponding proteins. Thus, a portion of genes that was pruned in the first stage is added again to the primary set of selected genes. In the final stage, by using support vector machine-based recursive feature elimination (SVMRFE) method, the final set of informative genes was identified. After that, we compared our proposed algorithm with decision tree methods in the same datasets. The proposed procedure was evaluated on two publicly available DNA microarray dataset, including 456 samples on breast cancer. The proposed algorithm reached to 100% accuracy for predicting Luminal B by using the JMI method in the first step. In conclusion the proposed method showed an appealing improvement in classification accuracy for a multiclass prediction problem. We can predict subtypes with greater than 91.2% overall accuracy by proposed algorithm. However, the accuracy of prediction subtypes in tree decision method is 78.6%.

Seismic data, being non-stationary in nature, have varying frequency content in time. Time-frequency decomposition of a seismic signal aims to characterize the time-dependent frequency response of subsurface rocks and reservoirs. There are a variety of spectral decomposition methods; these include the Short Time Fourier Transform (STFT), Continuous Wavelet Transform (CWT) and Matching Pursuit Decomposition (MPD). In STFT, time-frequency resolution is fixed over the entire time-frequency space by preselecting a window length. Therefore, resolution in seismic data analysis becomes dependent on a user specified window length. The CWT dilates and compresses wavelets to provide a time-scale spectrum instead of a time-frequency spectrum. Converting a scalogram into a time-frequency spectrum using the center frequency of a scale gives an erroneous attenuation in the spectrum. Decomposition (MPD) do not involve windowing of the seismic data and thus have the best combination of temporal and spectral resolution as compared to STFT and the continuous wavelet transform (CWT). Castagna et al, 2003, used matching-pursuit decomposition for instantaneous spectral analysis to detect low-frequency shadows beneath hydrocarbon reservoirs. In this paper we analyze performance of matching pursuit method in mapping channel sediments at one of the south-west oil- field in Iran.

In early years, transforming of seismic traces into time and frequency domain was done via windowed Fourier transform, called a Short Time Fourier Transform (STFT). In this method the resolution of the results in time-frequency domain was controlled by the width of the selected window. Continuous Wavelet transform (CWT) was a remedy to solve this problem by using the scale able wavelets. Converting a scalogram into a time frequency spectrum using the center frequency of a scale gives an erroneous attenuation in the spectrum. The main focus of this research is on Time-Frequency Continuous Wavelet Transform (TFCWT), which is Time-Frequency map obtained from (CWT). This method has recently proven to be an efficient tool among spectral decomposition methods, since it provides higher frequency-resolution in low frequencies, and higher time-resolution in higher frequencies. In this paper, it has been tried to investigate application of Time-Frequency Continuous Wavelet Transform (TFCWT) in time-frequency analysis of seismic sections to detect low-frequency gas shadow on real data from one Iranian gas reservoir. The results from (TFCWT) are compared with (CWT) and (STFT) applying single frequency seismic section at frequency 25Hz, while it is expected to observe low-frequency gas shadows in this frequency.