Application of the Naïve Bayesian Classifier to optimize treatment decisions

Vertebral Column Pathology Diagnosis Using Ensemble Strategies Based on Supervised Machine Learning Techniques

One expanding area of bioinformatics is medical diagnosis through the categorization of biomedical characteristics. Automatic medical strategies to boost the diagnostic through machine learning (ML) methods are challenging. They require a formal examination of their performance to identify the best conditions that enhance the ML method. This work proposes variants of the Voting and Stacking (VC and SC) ensemble strategies based on diverse auto-tuning supervised machine learning techniques to increase the efficacy of traditional baseline classifiers for the automatic diagnosis of vertebral column orthopedic illnesses. The ensemble strategies are created by first combining a complete set of auto-tuned baseline classifiers based on different processes, such as geometric, probabilistic, logic, and optimization. Next, the three most promising classifiers are selected among k-Nearest Neighbors (kNN), Naïve Bayes (NB), Logistic Regression (LR), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Support Vector Machine (SVM), Artificial Neural Networks (ANN), and Decision Tree (DT). The grid-search K-Fold cross-validation strategy is applied to auto-tune the baseline classifier hyperparameters. The performances of the proposed ensemble strategies are independently compared with the auto-tuned baseline classifiers. A concise analysis evaluates accuracy, precision, recall, F1-score, and ROC-ACU metrics. The analysis also examines the misclassified disease elements to find the most and least reliable classifiers for this specific medical problem. The results show that the VC ensemble strategy provides an improvement comparable to that of the best baseline classifier (the kNN). Meanwhile, when all baseline classifiers are included in the SC ensemble, this strategy surpasses 95% in all the evaluated metrics, standing out as the most suitable option for classifying vertebral column diseases.

Probabilistic classification of the severity classes of unhealthy air pollution events

Air pollution events can be categorized as extreme or non-extreme on the basis of their magnitude of severity. High-risk extreme air pollution events will exert a disastrous effect on the environment. Therefore, public health and policy-making authorities must be able to determine the characteristics of these events. This study proposes a probabilistic machine learning technique for predicting the classification of extreme and non-extreme events on the basis of data features to address the above issue. The use of the naïve Bayes model in the prediction of air pollution classes is proposed to leverage its simplicity as well as high accuracy and efficiency. A case study was conducted on the air pollution index data of Klang, Malaysia, for the period of January 01, 1997, to August 31, 2020. The trained naïve Bayes model achieves high accuracy, sensitivity, and specificity on the training and test datasets. Therefore, the naïve Bayes model can be easily applied in air pollution analysis while providing a promising solution for the accurate and efficient prediction of extreme or non-extreme air pollution events. The findings of this study provide reliable information to public authorities for monitoring and managing sustainable air quality over time.

Chemotherapy and Abdominal Wall Closure Technique Increase the Probability of Postoperative Ventral Incisional Hernia in Patients With Colon Cancer

BACKGROUND

Chemotherapy is associated with postoperative ventral incisional hernia (PVIH) after right hemicolectomy (RHC) for colon cancer, and abdominal wall closure technique may affect PVIH. We sought to identify clinical predictors of PVIH.

METHODS

We retrospectively analyzed patients who underwent RHC for colon cancer from 2008-2018 and later developed PVIH. Time to PVIH was analyzed with Kaplan-Meier analysis, clinical predictors were identified with multivariable Cox proportional hazards modeling, and the probability of PVIH given chemotherapy and the suture technique was estimated with Bayesian analysis.

RESULTS

We identified 399 patients (209 no adjuvant chemotherapy and 190 adjuvant chemotherapy), with an overall PVIH rate of 38%. The 5-year PVIH rate was 55% for adjuvant chemotherapy, compared with 38% for none (log-rank P < .05). Adjuvant chemotherapy (hazard ratio [HR] 1.65, 95% confidence interval [CI] 1.18-2.31, P < .01), age (HR .99, 95% CI .97-1.00, P < .01), body mass index (HR 1.02, 95% CI 1.00-1.04, P < .01), and neoadjuvant chemotherapy (HR 1.92, 95% CI 1.21-3.00, P < .01) were independently associated with PVIH. Postoperative ventral incisional hernia was more common overall in patients who received adjuvant chemotherapy (46% compared with 30%, P < .01). In patients who received adjuvant chemotherapy, the probability of PVIH for incision closure with #1 running looped polydioxanone was 42%, compared with 59% for incision closure with #0 single interrupted polyglactin 910.

DISCUSSION

Exposure to chemotherapy increases the probability of PVIH after RHC, and non-short stitch incision closure further increases this probability, more so than age or body mass index. The suture technique deserves further study as a modifiable factor in this high-risk population.

Predictive effect of Bayes discrimination in the level of serum protein factors and cognitive dysfunction in schizophrenia

Finding molecular biomarkers that can be related to the degree of cognitive dysfunction in schizophrenia remains a challenge. The levels of 6 Serum Protein Factors (NGF, BDNF, IL-6, TNF-α, S100β, GFAP) in peripheral blood of patients with schizophrenia were measured. The cognitive function of patients with schizophrenia was assessed by MATRICS Consensus Cognitive Battery (MCCB), a systematic assessment tool of international gold standard for cognitive function assessment of schizophrenia. To explore the correlation between these 6 biomarkers and the degree of cognitive dysfunction in schizophrenia,78 schizophrenic patients and 71 healthy controls were included in the study. The serum concentrations of BDNF and GFAP were lower in the patient group, but the concentrations of IL-6, TNF-α and S100β were higher. The speed of information processing, word learning, reasoning and problem solving, visual learning T-score of the patient group were lower than the control group. Bayes discriminant function model has a high correct discriminant rate for the severity of cognitive dysfunction in schizophrenia. The level of serum protein factor and clinical symptom score of schizophrenia may forecast the degree of cognitive dysfunction, which is expected to be a potential biomarker to identify the degree of cognitive dysfunction of schizophrenia, and provide objective basis for the clinical diagnosis and treatment of patients with schizophrenia.

Validating machine learning models for the prediction of labour induction intervention using routine data: a registry-based retrospective cohort study at a tertiary hospital in northern Tanzania

OBJECTIVES

We aimed at identifying the important variables for labour induction intervention and assessing the predictive performance of machine learning algorithms.

SETTING

We analysed the birth registry data from a referral hospital in northern Tanzania. Since July 2000, every birth at this facility has been recorded in a specific database.

PARTICIPANTS

21 578 deliveries between 2000 and 2015 were included. Deliveries that lacked information regarding the labour induction status were excluded.

PRIMARY OUTCOME

Deliveries involving labour induction intervention.

RESULTS

Parity, maternal age, body mass index, gestational age and birth weight were all found to be important predictors of labour induction. Boosting method demonstrated the best discriminative performance (area under curve, AUC=0.75: 95% CI (0.73 to 0.76)) while logistic regression presented the least (AUC=0.71: 95% CI (0.70 to 0.73)). Random forest and boosting algorithms showed the highest net-benefits as per the decision curve analysis.

CONCLUSION

All of the machine learning algorithms performed well in predicting the likelihood of labour induction intervention. Further optimisation of these classifiers through hyperparameter tuning may result in an improved performance. Extensive research into the performance of other classifier algorithms is warranted.

Enhancing ontology-driven diagnostic reasoning with a symptom-dependency-aware Naïve Bayes classifier

Background

Ontology has attracted substantial attention from both academia and industry. Handling uncertainty reasoning is important in researching ontology. For example, when a patient is suffering from cirrhosis, the appearance of abdominal vein varices is four times more likely than the presence of bitter taste. Such medical knowledge is crucial for decision-making in various medical applications but is missing from existing medical ontologies. In this paper, we aim to discover medical knowledge probabilities from electronic medical record (EMR) texts to enrich ontologies. First, we build an ontology by identifying meaningful entity mentions from EMRs. Then, we propose a symptom-dependency-aware naïve Bayes classifier (SDNB) that is based on the assumption that there is a level of dependency among symptoms. To ensure the accuracy of the diagnostic classification, we incorporate the probability of a disease into the ontology via innovative approaches.

Results

We conduct a series of experiments to evaluate whether the proposed method can discover meaningful and accurate probabilities for medical knowledge. Based on over 30,000 deidentified medical records, we explore 336 abdominal diseases and 81 related symptoms. Among these 336 gastrointestinal diseases, the probabilities of 31 diseases are obtained via our method. These 31 probabilities of diseases and 189 conditional probabilities between diseases and the symptoms are added into the generated ontology.

Conclusion

In this paper, we propose a medical knowledge probability discovery method that is based on the analysis and extraction of EMR text data for enriching a medical ontology with probability information. The experimental results demonstrate that the proposed method can effectively identify accurate medical knowledge probability information from EMR data. In addition, the proposed method can efficiently and accurately calculate the probability of a patient suffering from a specified disease, thereby demonstrating the advantage of combining an ontology and a symptom-dependency-aware naïve Bayes classifier.

Assessing the performance of GIS- based machine learning models with different accuracy measures for determining susceptibility to gully erosion

The main purpose was to compare discrimination and reliability of four machine learning models to create gully erosion susceptibility map (GESM) in a part of Ekbatan Dam Basin, Hamedan, western Iran. Extensive field surveys using GPS, and the visual interpretation of satellite images, used to prepare a digital map of the spatial distribution of gullies. 130 locations were sampled to elucidate the spatial distribution of the soil surface properties. Topographic attributes were provided from digital elevation model (DEM). The land use and normalized difference vegetation index (NDVI) maps were created by satellite imagery. The functional relationships between gully erosion and controlling factors were calculated using the random forest (RF), support vector machine (SVM), Naïve Bayes (NB), and generalized additive model (GAM) models. The performance of models was evaluated by 10-fold cross-validation based on efficiency, Kappa coefficient, receiver operating characteristic curve (ROC), mean absolute error (MAE), and root mean square error (RMSE). The results showed that the RF model had the highest amount of efficiency, Kappa coefficient, and AUC and the lowest amounts of MAE and RMSE compared with SVM, NB, and GAM. The RF model showed the highest predictive performance (mean AUC = 92.4%), followed by SVM (mean AUC = 90.9%), GAM (mean AUC = 89.9%), and NB (mean AUC = 87.2%) models. Overall accuracy of the models ranged from excellent (NB, GAM) to outstanding (RF, SVM) classes. The capacity of all models for creating GESM was quite stable when the calibration and validation samples were changed through10-fold cross-validation technique. According to variable importance analysis performed by RF model, the most important variables are distance from rivers, calcium carbonate equivalent (CCE), and topographic position index (TPI). The obtained maps can help identifying areas at risk of gully erosion and facilitate the implementation of plans for soil conservation and sustainable management.

Natural language processing and recurrent network models for identifying genomic mutation-associated cancer treatment change from patient progress notes

Objectives

Natural language processing (NLP) and machine learning approaches were used to build classifiers to identify genomic-related treatment changes in the free-text visit progress notes of cancer patients.

Methods

We obtained 5889 deidentified progress reports (2439 words on average) for 755 cancer patients who have undergone a clinical next generation sequencing (NGS) testing in Wake Forest Baptist Comprehensive Cancer Center for our data analyses. An NLP system was implemented to process the free-text data and extract NGS-related information. Three types of recurrent neural network (RNN) namely, gated recurrent unit, long short-term memory (LSTM), and bidirectional LSTM (LSTM_Bi) were applied to classify documents to the treatment-change and no-treatment-change groups. Further, we compared the performances of RNNs to 5 machine learning algorithms including Naive Bayes, K-nearest Neighbor, Support Vector Machine for classification, Random forest, and Logistic Regression.

Results

Our results suggested that, overall, RNNs outperformed traditional machine learning algorithms, and LSTM_Bi showed the best performance among the RNNs in terms of accuracy, precision, recall, and F1 score. In addition, pretrained word embedding can improve the accuracy of LSTM by 3.4% and reduce the training time by more than 60%.

Discussion and Conclusion

NLP and RNN-based text mining solutions have demonstrated advantages in information retrieval and document classification tasks for unstructured clinical progress notes.

A Review of Data Mining Techniques and Applications

Data mining is the analytics and knowledge discovery process of analyzing large volumes of data from various sources and transforming the data into useful information. Various disciplines have contributed to its development and is becoming increasingly important in the scientific and industrial world. This article presents a review of data mining techniques and applications from 1996 to 2016. Techniques are divided into two main categories: predictive methods and descriptive methods. Due to the huge number of publications available on this topic, only a selected number are used in this review to highlight the developments of the past 20 years. Applications are included to provide some insights into how each data mining technique has evolved over the last two decades. Recent research trends focus more on large data sets and big data. Recently there have also been more applications in area of health informatics with the advent of newer algorithms.

Applying Naive Bayesian Networks to Disease Prediction: a Systematic Review

INTRODUCTION

Naive Bayesian networks (NBNs) are one of the most effective and simplest Bayesian networks for prediction.

OBJECTIVE

This paper aims to review published evidence about the application of NBNs in predicting disease and it tries to show NBNs as the fundamental algorithm for the best performance in comparison with other algorithms.

METHODS

PubMed was electronically checked for articles published between 2005 and 2015. For characterizing eligible articles, a comprehensive electronic searching method was conducted. Inclusion criteria were determined based on NBN and its effects on disease prediction. A total of 99 articles were found. After excluding the duplicates (n= 5), the titles and abstracts of 94 articles were skimmed according to the inclusion criteria. Finally, 38 articles remained. They were reviewed in full text and 15 articles were excluded. Eventually, 23 articles were selected which met our eligibility criteria and were included in this study.

RESULT

In this article, the use of NBN in predicting diseases was described. Finally, the results were reported in terms of Accuracy, Sensitivity, Specificity and Area under ROC curve (AUC). The last column in Table 2 shows the differences between NBNs and other algorithms.

DISCUSSION

This systematic review (23 studies, 53,725 patients) indicates that predicting diseases based on a NBN had the best performance in most diseases in comparison with the other algorithms. Finally in most cases NBN works better than other algorithms based on the reported accuracy.

CONCLUSION

The method, termed NBNs is proposed and can efficiently construct a prediction model for disease.

Identifying spatial imaging biomarkers of glioblastoma multiforme for survival group prediction

PURPOSE

Glioblastoma multiforme (GBM) is the most common malignant brain tumor in adults. Most GBMs exhibit extensive regional heterogeneity at tissue, cellular, and molecular scales, but the clinical relevance of the observed spatial imaging characteristics remains unknown. We investigated pretreatment magnetic resonance imaging (MRI) scans of GBMs to identify tumor subregions and quantify their image-based spatial characteristics that are associated with survival time.

MATERIALS AND METHODS

We quantified tumor subregions (termed habitats) in GBMs, which are hypothesized to capture intratumoral characteristics using multiple MRI sequences. For proof-of-concept, we developed a computational framework that used intratumoral grouping and spatial mapping to identify GBM tumor subregions and yield habitat-based features. Using a feature selector and three classifiers, experimental results from two datasets are reported, including Dataset1 with 32 GBM patients (594 tumor slices) and Dataset2 with 22 GBM patients, who did not undergo resection (261 tumor slices) for survival group prediction.

RESULTS

In both datasets, we show that habitat-based features achieved 87.50% and 86.36% accuracies for survival group prediction, respectively, using leave-one-out cross-validation. Experimental results revealed that spatially correlated features between signal-enhanced subregions were effective for predicting survival groups (P < 0.05 for all three machine-learning classifiers).

CONCLUSION

The quantitative spatial-correlated features derived from MRI-defined tumor subregions in GBM could be effectively used to predict the survival time of patients.

LEVEL OF EVIDENCE

2 J. MAGN. RESON. IMAGING 2017;46:115-123.

Predicting distant failure in early stage NSCLC treated with SBRT using clinical parameters

PURPOSE/OBJECTIVE

The aim of this study is to predict early distant failure in early stage non-small cell lung cancer (NSCLC) treated with stereotactic body radiation therapy (SBRT) using clinical parameters by machine learning algorithms.

MATERIALS/METHODS

The dataset used in this work includes 81 early stage NSCLC patients with at least 6months of follow-up who underwent SBRT between 2006 and 2012 at a single institution. The clinical parameters (n=18) for each patient include demographic parameters, tumor characteristics, treatment fraction schemes, and pretreatment medications. Three predictive models were constructed based on different machine learning algorithms: (1) artificial neural network (ANN), (2) logistic regression (LR) and (3) support vector machine (SVM). Furthermore, to select an optimal clinical parameter set for the model construction, three strategies were adopted: (1) clonal selection algorithm (CSA) based selection strategy; (2) sequential forward selection (SFS) method; and (3) statistical analysis (SA) based strategy. 5-cross-validation is used to validate the performance of each predictive model. The accuracy was assessed by area under the receiver operating characteristic (ROC) curve (AUC), sensitivity and specificity of the system was also evaluated.

RESULTS

The AUCs for ANN, LR and SVM were 0.75, 0.73, and 0.80, respectively. The sensitivity values for ANN, LR and SVM were 71.2%, 72.9% and 83.1%, while the specificity values for ANN, LR and SVM were 59.1%, 63.6% and 63.6%, respectively. Meanwhile, the CSA based strategy outperformed SFS and SA in terms of AUC, sensitivity and specificity.

CONCLUSIONS

Based on clinical parameters, the SVM with the CSA optimal parameter set selection strategy achieves better performance than other strategies for predicting distant failure in lung SBRT patients.

Fast spectroscopic multiple analysis (FASMA) for brain tumor classification: a clinical decision support system utilizing multi-parametric 3T MR data

INTRODUCTION

A clinical decision support system (CDSS) for brain tumor classification can be used to assist in the diagnosis and grading of brain tumors. A Fast Spectroscopic Multiple Analysis (FASMA) system that uses combinations of multiparametric MRI data sets was developed as a CDSS for brain tumor classification.

METHODS

MRI metabolic ratios and spectra, from long and short TE, respectively, as well as diffusion and perfusion data were acquired from the intratumoral and peritumoral area of 126 patients with untreated intracranial tumors. These data were categorized based on the pathology, and different machine learning methods were evaluated regarding their classification performance for glioma grading and differentiation of infiltrating versus non-infiltrating lesions. Additional databases were embedded to the system, including updated literature values of the related MR parameters and typical tumor characteristics (imaging and histological), for further comparisons. Custom Graphical User Interface (GUI) layouts were developed to facilitate classification of the unknown cases based on the user's available MR data.

RESULTS

The highest classification performance was achieved with a support vector machine (SVM) using the combination of all MR features. FASMA correctly classified 89 and 79% in the intratumoral and peritumoral area, respectively, for cases from an independent test set. FASMA produced the correct diagnosis, even in the misclassified cases, since discrimination between infiltrative versus non-infiltrative cases was possible.

CONCLUSIONS

FASMA is a prototype CDSS, which integrates complex quantitative MR data for brain tumor characterization. FASMA was developed as a diagnostic assistant that provides fast analysis, representation and classification for a set of MR parameters. This software may serve as a teaching tool on advanced MRI techniques, as it incorporates additional information regarding typical tumor characteristics derived from the literature.

Classification methods for the differentiation of atypical meningiomas using diffusion and perfusion techniques at 3-T MRI

The purpose was to investigate the contribution of machine learning algorithms using diffusion and perfusion techniques in the differentiation of atypical meningiomas from glioblastomas and metastases. Apparent diffusion coefficient, fractional anisotropy, and relative cerebral blood volume were measured in different tumor regions. Naive Bayes, k-Nearest Neighbor, and Support Vector Machine classifiers were used in the classification procedure. The application of classification methods adds incremental differential diagnostic value. Differentiation is mainly achieved using diffusion metrics, while perfusion measurements may provide significant information for the peritumoral regions.

Automated differentiation of glioblastomas from intracranial metastases using 3T MR spectroscopic and perfusion data

PURPOSE

Differentiation of glioblastomas from metastases is clinical important, but may be difficult even for expert observers. To investigate the contribution of machine learning algorithms in the differentiation of glioblastomas multiforme (GB) from metastases, we developed and tested a pattern recognition system based on 3T magnetic resonance (MR) data.

MATERIALS AND METHODS

Single and multi-voxel proton magnetic resonance spectroscopy (1H-MRS) and dynamic susceptibility contrast (DSC) MRI scans were performed on 49 patients with solitary brain tumors (35 glioblastoma multiforme and 14 metastases). Metabolic (NAA/Cr, Cho/Cr, (Lip [Formula: see text] Lac)/Cr) and perfusion (rCBV) parameters were measured in both intratumoral and peritumoral regions. The statistical significance of these parameters was evaluated. For the classification procedure, three datasets were created to find the optimum combination of parameters that provides maximum differentiation. Three machine learning methods were utilized: Naïve-Bayes, Support Vector Machine (SVM) and [Formula: see text]-nearest neighbor (KNN). The discrimination ability of each classifier was evaluated with quantitative performance metrics.

RESULTS

Glioblastoma and metastases were differentiable only in the peritumoral region of these lesions ([Formula: see text]). SVM achieved the highest overall performance (accuracy 98%) for both the intratumoral and peritumoral areas. Naïve-Bayes and KNN presented greater variations in performance. The proper selection of datasets plays a very significant role as they are closely correlated to the underlying pathophysiology.

CONCLUSION

The application of pattern recognition techniques using 3T MR-based perfusion and metabolic features may provide incremental diagnostic value in the differentiation of common intraaxial brain tumors, such as glioblastoma versus metastasis.

B-Spline Registration Based on New Concept of an Intelligent Masking Procedure and GPU Computations for the Head and Neck Adaptive Tomotherapy

The deformable image registration (DIR) procedure has been optimized for helical tomotherapy. The data on registration shifts obtained on matching planning image with pre-treatment megavoltage CT are used in our software for acceleration of the first step (rigid registration) of the DIR procedure and for implementation of the B-Spline algorithm with intelligent masking. Priorities of the masks were automatically calculated based on disagreement detected during rigid registration. Evaluation tasks included: (a) comparison of accuracy and rate for schemes of pre-registered and non-registered images; (b) qualification of the effectiveness of the intelligent masking process, and (c) determination of acceleration of achievable with GPU computing. A specially designed head and neck phantom used for evaluation included structures with controlled changes of position, volume, density, and shape. Re-contouring procedures were performed with an Adaptive Planning software (Tomotherapy Inc.). No statistical difference was observed in accuracy of DIR based on structure position match on the tomotherapy unit and non pre-registered images (p > 0.7). Using pre-registered data reduces the total time required for execution of the elastic registration procedure by 5%. These data are also necessary for intelligent masking procedure during B-Spine registration. Intelligent masking procedure increases accuracy of the registration for a masked structure (p < 0.04) without decreasing the accuracy in non-masked tissues and additionally reduces the total time by 13%. GPU computations speed up procedure 30 times. GPU computing of the DIR in current status of our investigation could be realized in a relatively short time after pre-treatment imaging. The proposed approach can be used in the routine assessment of anatomic changes occurring in healthy tissue during the course of radiotherapy. Further developments will be concentrated on the full integration of DIR computations in the imaging and treatment process of helical tomotherapy.

Gene expression analysis predicts insect venom anaphylaxis in indolent systemic mastocytosis

BACKGROUND

Anaphylaxis to insect venom (Hymenoptera) is most severe in patients with mastocytosis and may even lead to death. However, not all patients with mastocytosis suffer from anaphylaxis. The aim of the study was to analyze differences in gene expression between patients with indolent systemic mastocytosis (ISM) and a history of insect venom anaphylaxis (IVA) compared to those patients without a history of anaphylaxis, and to determine the predictive use of gene expression profiling.

METHODS

Whole-genome gene expression analysis was performed in peripheral blood cells.

RESULTS

Twenty-two adults with ISM were included: 12 with a history of IVA and 10 without a history of anaphylaxis of any kind. Significant differences in single gene expression corrected for multiple testing were found for 104 transcripts (P < 0.05). Gene ontology analysis revealed that the differentially expressed genes were involved in pathways responsible for the development of cancer and focal and cell adhesion suggesting that the expression of genes related to the differentiation state of cells is higher in patients with a history of anaphylaxis. Based on the gene expression profiles, a naïve Bayes prediction model was built identifying patients with IVA.

CONCLUSIONS

In ISM, gene expression profiles are different between patients with a history of IVA and those without. These findings might reflect a more pronounced mast cells dysfunction in patients without a history of anaphylaxis. Gene expression profiling might be a useful tool to predict the risk of anaphylaxis on insect venom in patients with ISM. Prospective studies are needed to substantiate any conclusions.

Gene expression profile, pathways, and transcriptional system regulation in indolent systemic mastocytosis

BACKGROUND

  Mastocytosis is an uncommon disease resulting from proliferation of abnormal mast cells infiltrating skin, bone marrow, liver, and other tissues. The aim of this study was to find differences in gene expression in peripheral blood cells of patients with indolent systemic mastocytosis compared to healthy controls. The second aim was to define a specific gene expression profile in patients with mastocytosis.

METHODS

  Twenty-two patients with indolent systemic mastocytosis and 43 healthy controls were studied. Whole genome gene expression analysis was performed on RNA samples isolated from the peripheral blood. For amplification and labelling of the RNA, the Illumina TotalPrep 96 RNA Amplification Kit was used. Human HT-12_V3_expression arrays were processed. Data analysis was performed using GeneSpring, Genecodis, and Transcriptional System Regulators.

RESULTS

  Comparison of gene expression between patients and controls revealed a significant difference (P < 0.05 corrected for multiple testing) and the fold change difference >2 in gene expression in 2303 of the 48.794 analysed transcripts. Functional annotation indicated that the main pathways in which the differently expressed genes were involved are ubiquitin-mediated proteolysis, MAPK signalling pathway, pathways in cancer, and Jak-STAT signalling. The expression distributions for both groups did not overlap at all, indicating that many genes are highly differentially expressed in both groups.

CONCLUSION

  We were able to find abnormalities in gene expression in peripheral blood cells of patients with indolent systemic mastocytosis and to construct a gene expression profile which may be useful in clinical practice to predict the presence of mastocytosis and in further research of novel drugs.

An improved brain image classification technique with mining and shape prior segmentation procedure

The shape prior segmentation procedure and pruned association rule with ImageApriori algorithm has been used to develop an improved brain image classification system are presented in this paper. The CT scan brain images have been classified into three categories namely normal, benign and malignant, considering the low-level features extracted from the images and high level knowledge from specialists to enhance the accuracy in decision process. The experimental results on pre-diagnosed brain images showed 97% sensitivity, 91% specificity and 98.5% accuracy. The proposed algorithm is expected to assist the physicians for efficient classification with multiple key features per image.