Algorithms Implemented for Cancer Gene Searching and Classifications

Understanding the gene expression is an important factor to cancer diagnosis. One target of this understanding is implementing cancer gene search and classification methods. However, cancer gene search and classification is a challenge in that there is no an obvious exact algorithm that can be implemented individually for various cancer cells. In this paper a research is con-ducted through the most common top ranked algorithms implemented for cancer gene search and classification, and how they are implemented to reach a better performance. The paper will distinguish algorithms implemented for Bio image analysis for cancer cells and algorithms implemented based on DNA array data. The main purpose of this paper is to explore a road map towards presenting the most current algorithms implemented for cancer gene search and classification

Texture analysis of aggressive and nonaggressive lung tumor CE CT images

This paper presents the potential for fractal analysis of time sequence contrast-enhanced (CE) computed tomography (CT) images to differentiate between aggressive and nonaggressive malignant lung tumors (i.e., high and low metabolic tumors). The aim is to enhance CT tumor staging prediction accuracy through identifying malignant aggressiveness of lung tumors. As branching of blood vessels can be considered a fractal process, the research examines vascularized tumor regions that exhibit strong fractal characteristics. The analysis is performed after injecting 15 patients with a contrast agent and transforming at least 11 time sequence CE CT images from each patient to the fractal dimension and determining corresponding lacunarity. The fractal texture features were averaged over the tumor region and quantitative classification showed up to 83.3% accuracy in distinction between advanced (aggressive) and early-stage (nonaggressive) malignant tumors. Also, it showed strong correlation with corresponding lung tumor stage and standardized tumor uptake value of fluoro deoxyglucose as determined by positron emission tomography. These results indicate that fractal analysis of time sequence CE CT images of malignant lung tumors could provide additional information about likely tumor aggression that could potentially impact on clinical management decisions in choosing the appropriate treatment procedure

Boosting the concordance index for survival data - a unified framework to derive and evaluate biomarker combinations

The development of molecular signatures for the prediction of time-to-event outcomes is a methodologically challenging task in bioinformatics and biostatistics. Although there are numerous approaches for the derivation of marker combinations and their evaluation, the underlying methodology often suffers from the problem that different optimization criteria are mixed during the feature selection, estimation and evaluation steps. This might result in marker combinations that are only suboptimal regarding the evaluation criterion of interest. To address this issue, we propose a unified framework to derive and evaluate biomarker combinations. Our approach is based on the concordance index for time-to-event data, which is a non-parametric measure to quantify the discrimatory power of a prediction rule. Specifically, we propose a component-wise boosting algorithm that results in linear biomarker combinations that are optimal with respect to a smoothed version of the concordance index. We investigate the performance of our algorithm in a large-scale simulation study and in two molecular data sets for the prediction of survival in breast cancer patients. Our numerical results show that the new approach is not only methodologically sound but can also lead to a higher discriminatory power than traditional approaches for the derivation of gene signatures.Comment: revised manuscript - added simulation study, additional result

Study of microRNAs-21/221 as potential breast cancer biomarkers in Egyptian women

microRNAs (miRNAs) play an important role in cancer prognosis. They are small molecules, approximately 17–25 nucleotides in length, and their high stability in human serum supports their use as novel diagnostic biomarkers of cancer and other pathological conditions. In this study, we analyzed the expression patterns of miR-21 and miR-221 in the serum from a total of 100 Egyptian female subjects with breast cancer, fibroadenoma, and healthy control subjects. Using microarray-based expression profiling followed by real-time polymerase chain reaction validation, we compared the levels of the two circulating miRNAs in the serum of patients with breast cancer (n = 50), fibroadenoma (n = 25), and healthy controls (n = 25). The miRNA SNORD68 was chosen as the housekeeping endogenous control. We found that the serum levels of miR-21 and miR-221 were significantly overexpressed in breast cancer patients compared to normal controls and fibroadenoma patients. Receiver Operating Characteristic (ROC) curve analysis revealed that miR-21 has greater potential in discriminating between breast cancer patients and the control group, while miR-221 has greater potential in discriminating between breast cancer and fibroadenoma patients. Classification models using k-Nearest Neighbor (kNN), Naïve Bayes (NB), and Random Forests (RF) were developed using expression levels of both miR-21 and miR-221. Best classification performance was achieved by NB Classification models, reaching 91% of correct classification. Furthermore, relative miR-221 expression was associated with histological tumor grades. Therefore, it may be concluded that both miR-21 and miR-221 can be used to differentiate between breast cancer patients and healthy controls, but that the diagnostic accuracy of serum miR-21 is superior to miR-221 for breast cancer prediction. miR-221 has more diagnostic power in discriminating between breast cancer and fibroadenoma patients. The overexpression of miR-221 has been associated with the breast cancer grade. We also demonstrated that the combined expression of miR-21 and miR-221can be successfully applied as breast cancer biomarkers

Data mining analyses for precision medicine in acromegaly: a proof of concept

Predicting which acromegaly patients could benefit from somatostatin receptor ligands (SRL) is a must for personalized medicine. Although many biomarkers linked to SRL response have been identified, there is no consensus criterion on how to assign this pharmacologic treatment according to biomarker levels. Our aim is to provide better predictive tools for an accurate acromegaly patient stratification regarding the ability to respond to SRL. We took advantage of a multicenter study of 71 acromegaly patients and we used advanced mathematical modelling to predict SRL response combining molecular and clinical information. Different models of patient stratification were obtained, with a much higher accuracy when the studied cohort is fragmented according to relevant clinical characteristics. Considering all the models, a patient stratification based on the extrasellar growth of the tumor, sex, age and the expression of E-cadherin, GHRL, IN1-GHRL, DRD2, SSTR5 and PEBP1 is proposed, with accuracies that stand between 71 to 95%. In conclusion, the use of data mining could be very useful for implementation of personalized medicine in acromegaly through an interdisciplinary work between computer science, mathematics, biology and medicine. This new methodology opens a door to more precise and personalized medicine for acromegaly patients

Energy Efficiency Prediction using Artificial Neural Network

Buildings energy consumption is growing gradually and put away around 40% of total energy use. Predicting heating and cooling loads of a building in the initial phase of the design to find out optimal solutions amongst different designs is very important, as ell as in the operating phase after the building has been finished for efficient energy. In this study, an artificial neural network model was designed and developed for predicting heating and cooling loads of a building based on a dataset for building energy performance. The main factors for input variables are: relative compactness, roof area, overall height, surface area, glazing are a, wall area, glazing area distribution of a building, orientation, and the output variables: heating and cooling loads of the building. The dataset used for training are the data published in the literature for various 768 residential buildings. The model was trained and validated, most important factors affecting heating load and cooling load are identified, and the accuracy for the validation was 99.60%