The current standard for measuring tumor response using X-ray, CT and MRI is based on the response evaluation criterion in solid tumors (RECIST) which, while providing simplifications over previous (WHO) 2-D methods, ...The current standard for measuring tumor response using X-ray, CT and MRI is based on the response evaluation criterion in solid tumors (RECIST) which, while providing simplifications over previous (WHO) 2-D methods, stipulate four response categories: CR (complete response), PR (partial response), PD (progressive disease), SD (stable disease) based purely on percentage changes without consideration of any measurement uncertainty. In this paper, we propose a statistical procedure for tumor response assessment based on uncertainty measures of radiologist’s measurement data. We present several variance estimation methods using time series methods and empirical Bayes methods when a small number of serial observations are available on each member of a group of subjects. We use a publically available database which contains a set of over 100 CT scan images on 23 patients with annotated RECIST measurements by two radiologist readers. We show that despite of bias in each individual reader’s measurements, statistical decisions on tumor change can be made on each individual subject. The consistency of the two readers can be established based on the intra-reader change assessments. Our proposal compares favorably with the RECIST standard protocol, raising the hope that, statistically sound decision on change analysis can be made in future based on careful variability and measurement uncertainty analysis.展开更多
Phosphoaspartate is one of the major components of eukaryotes and prokaryotic two-component signaling pathways,and it communicates the signal from the sensor of histidine kinase,through the response regulator,to the D...Phosphoaspartate is one of the major components of eukaryotes and prokaryotic two-component signaling pathways,and it communicates the signal from the sensor of histidine kinase,through the response regulator,to the DNA alongside transcription features and initiates the transcription of correct response genes.Thus,the prediction of phosphoaspartate sites is critical,and its experimental identification can be expensive,time-consuming,and tedious.For this purpose,we propose iPhosD-PseAAC,a new computational model for predicting phosphoaspartate sites in a particular protein sequence using Chou’s 5-steps rues:(1)Benchmark dataset.(2)The feature extraction techniques such as pseudo amino acid composition(PseAAC),statistical moments,and position relative features.(3)For the classification,artificial neural network AAN will be used.(4)In this step,10-fold cross-validation and self-consistency testing will be used for validation.For self-consistency testing,100%Acc is achieved,whereas,for 10-fold crossvalidation 95.14%Acc,95.58%Sn,94.70%Sp and 0.95 MCC are observed.(5).The final step is the development of a user-friendly web server for the ease of users.Thus,the iPhosD-PseAAC is the first and novel predictor for accurate and efficient identification of phosphoaspartate sites.展开更多
We develop various statistical methods important for multidimensional genetic data analysis. Theorems justifying application of these methods are established. We concentrate on the multifactor dimensionality reduction...We develop various statistical methods important for multidimensional genetic data analysis. Theorems justifying application of these methods are established. We concentrate on the multifactor dimensionality reduction, logic regression, random forests, stochastic gradient boosting along with their new modifications. We use complementary approaches to study the risk of complex diseases such as cardiovascular ones. The roles of certain combinations of single nucleotide polymorphisms and non-genetic risk factors are examined. To perform the data analysis concerning the coronary heart disease and myocardial infarction the Lomonosov Moscow State University supercomputer “Chebyshev” was employed.展开更多
The Paraconsistent Many-Valued Similarity (PMVS) method for multi-attribute decision making will be incomplete as a decision model if it is not extended to the realm of group decision-making. Therefore, in this articl...The Paraconsistent Many-Valued Similarity (PMVS) method for multi-attribute decision making will be incomplete as a decision model if it is not extended to the realm of group decision-making. Therefore, in this article, our primary objective is to show how the paraconsistent many-valued similarity method can be used to solve group decision-making problems involving choice making or ranking of a finite set of decision alternatives. Moreover, since weights are very important parameters in multi-attribute decision-making, we have introduced the Borda rule to calculate the weights of experts and that of every criterion under consideration. To demonstrate how the proposed method works, a numerical example on energy sources of an economy from the points of view of a group of experts is investigated. Further, we compare the results of this new approach with that of fuzzy TOPSIS group decision-making method to illustrate the robustness and effectiveness of the former.展开更多
Mining the data from the huge collection that are present in the database and uncovering the relationships between the item set are one of the key aspects of data mining technologies. Itinerary planning system with pe...Mining the data from the huge collection that are present in the database and uncovering the relationships between the item set are one of the key aspects of data mining technologies. Itinerary planning system with personalization in selecting the places to the users is one of the demanding features in most of the travel plan. In this work, the system is designed in such a way to provide the customized journey plan to the users and also the effective one to the back pack travelers. Here the Points of Interests are the places to visit in each destination for the number of days chosen by the travelers. In this system, the users are allowed to specify the desired POIs to visit for the selected destination and can make their customized travel plan effectively. This proposed system is designed to choose the customized places to visit and to plan travel for K-day itineraries. The most visited itineraries are saved and updated in the database. Association rules are used to find out the frequent places visited in each destination and to provide the reputed places to the users to plan the journey. Here the Weka tool is used to evaluate the performance of the algorithm and the rules that are generated for the given travel dataset. Data set is designed by considering several attributes that can take part during travel such as source, destination, travel cost, budget, etc. Statistical analysis is done to evaluate the performance of the proposed system and the list of features that are present in the system. During the analysis part, registered users, number of logins, frequent visits, and attributes are analyzed. Thus the system can be redefined further with the help of this statistical analysis. It is mostly used at the organization end to evaluate their performance and improve the features. Report is generated once the user has chosen their customized places to visit and all detailed description of journey is presented to the user. Report could be saved at the user end and they can use it for the future reference. Thus the goal of the system is to provide the customized travel with personalization in choosing POIs and to find the frequent places visited with desired amenities.展开更多
文摘The current standard for measuring tumor response using X-ray, CT and MRI is based on the response evaluation criterion in solid tumors (RECIST) which, while providing simplifications over previous (WHO) 2-D methods, stipulate four response categories: CR (complete response), PR (partial response), PD (progressive disease), SD (stable disease) based purely on percentage changes without consideration of any measurement uncertainty. In this paper, we propose a statistical procedure for tumor response assessment based on uncertainty measures of radiologist’s measurement data. We present several variance estimation methods using time series methods and empirical Bayes methods when a small number of serial observations are available on each member of a group of subjects. We use a publically available database which contains a set of over 100 CT scan images on 23 patients with annotated RECIST measurements by two radiologist readers. We show that despite of bias in each individual reader’s measurements, statistical decisions on tumor change can be made on each individual subject. The consistency of the two readers can be established based on the intra-reader change assessments. Our proposal compares favorably with the RECIST standard protocol, raising the hope that, statistically sound decision on change analysis can be made in future based on careful variability and measurement uncertainty analysis.
基金the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,https://dsr.kau.edu.sa/Default-305-EN under Grant No.G:136-611-1441.
文摘Phosphoaspartate is one of the major components of eukaryotes and prokaryotic two-component signaling pathways,and it communicates the signal from the sensor of histidine kinase,through the response regulator,to the DNA alongside transcription features and initiates the transcription of correct response genes.Thus,the prediction of phosphoaspartate sites is critical,and its experimental identification can be expensive,time-consuming,and tedious.For this purpose,we propose iPhosD-PseAAC,a new computational model for predicting phosphoaspartate sites in a particular protein sequence using Chou’s 5-steps rues:(1)Benchmark dataset.(2)The feature extraction techniques such as pseudo amino acid composition(PseAAC),statistical moments,and position relative features.(3)For the classification,artificial neural network AAN will be used.(4)In this step,10-fold cross-validation and self-consistency testing will be used for validation.For self-consistency testing,100%Acc is achieved,whereas,for 10-fold crossvalidation 95.14%Acc,95.58%Sn,94.70%Sp and 0.95 MCC are observed.(5).The final step is the development of a user-friendly web server for the ease of users.Thus,the iPhosD-PseAAC is the first and novel predictor for accurate and efficient identification of phosphoaspartate sites.
文摘We develop various statistical methods important for multidimensional genetic data analysis. Theorems justifying application of these methods are established. We concentrate on the multifactor dimensionality reduction, logic regression, random forests, stochastic gradient boosting along with their new modifications. We use complementary approaches to study the risk of complex diseases such as cardiovascular ones. The roles of certain combinations of single nucleotide polymorphisms and non-genetic risk factors are examined. To perform the data analysis concerning the coronary heart disease and myocardial infarction the Lomonosov Moscow State University supercomputer “Chebyshev” was employed.
文摘The Paraconsistent Many-Valued Similarity (PMVS) method for multi-attribute decision making will be incomplete as a decision model if it is not extended to the realm of group decision-making. Therefore, in this article, our primary objective is to show how the paraconsistent many-valued similarity method can be used to solve group decision-making problems involving choice making or ranking of a finite set of decision alternatives. Moreover, since weights are very important parameters in multi-attribute decision-making, we have introduced the Borda rule to calculate the weights of experts and that of every criterion under consideration. To demonstrate how the proposed method works, a numerical example on energy sources of an economy from the points of view of a group of experts is investigated. Further, we compare the results of this new approach with that of fuzzy TOPSIS group decision-making method to illustrate the robustness and effectiveness of the former.
文摘Mining the data from the huge collection that are present in the database and uncovering the relationships between the item set are one of the key aspects of data mining technologies. Itinerary planning system with personalization in selecting the places to the users is one of the demanding features in most of the travel plan. In this work, the system is designed in such a way to provide the customized journey plan to the users and also the effective one to the back pack travelers. Here the Points of Interests are the places to visit in each destination for the number of days chosen by the travelers. In this system, the users are allowed to specify the desired POIs to visit for the selected destination and can make their customized travel plan effectively. This proposed system is designed to choose the customized places to visit and to plan travel for K-day itineraries. The most visited itineraries are saved and updated in the database. Association rules are used to find out the frequent places visited in each destination and to provide the reputed places to the users to plan the journey. Here the Weka tool is used to evaluate the performance of the algorithm and the rules that are generated for the given travel dataset. Data set is designed by considering several attributes that can take part during travel such as source, destination, travel cost, budget, etc. Statistical analysis is done to evaluate the performance of the proposed system and the list of features that are present in the system. During the analysis part, registered users, number of logins, frequent visits, and attributes are analyzed. Thus the system can be redefined further with the help of this statistical analysis. It is mostly used at the organization end to evaluate their performance and improve the features. Report is generated once the user has chosen their customized places to visit and all detailed description of journey is presented to the user. Report could be saved at the user end and they can use it for the future reference. Thus the goal of the system is to provide the customized travel with personalization in choosing POIs and to find the frequent places visited with desired amenities.
