A Knowledge-Based Data Mining System for Diagnosing Malaria Related Cases in Healthcare Management

Data mining a process for assembling and analyzing data into useful information can be applied as rapid measures for malaria diagnosis. In this research work we implemented (knowledge-base) inference engine that will help in mining sample patient records to discover interesting relationships in malaria related cases. The computer programming language employed was the C#.NET programming language and Microsoft SQL Server 2005 served as the Relational Database Management System (RDBMS). The results obtained showed that knowledge-based data mining system was able to successfully mine out and diagnose possible diseases corresponding to the selected symptoms entered as query. With this finding, we believe the development of a Knowledge-based data mining system will not only be beneficial towards the diagnosis of malaria related cases in a more cost effective means but will assist in crucial decision making and new policy formulation in the malaria endemic regions

Computational Predictive Framework towards the Control and Reduction of Malaria incidences in Africa

Malaria persists as a problematic disease in Africa. It is the main cause of morbidity and mortality of children and efforts are currently being pooled to increase the control measures within endemic countries. With this in mind, we developed and applied a malaria control strategy from a computational perspective, to analyze, predict and offer appropriate recommendations and control measures of malaria data obtained from WHO ten Sub Saharan countries malaria report of 2008 . The analytical tool used is based on the C# programming language embedded artificial neural network intelligence system. From the outcome obtained, the system demonstrated some level of intelligence and showed the effects and impacts of some controllable factors on future malaria occurrence. The system at 90% prediction intensity showed malaria infection course to decline sharply by 2014 in all the study countries, ranging from 15.71% in Madagascar, 35.46% in Malawi, 38.44% in Nigeria, 38.98% in Sudan , 39.05% in Ethiopia 39.09% in Zambia, 40,08% in Ghana, 42.61% in Kenya, 45.21% in Uganda and 46.63% Mozambique respectively. Therefore, more future prevention, control and management interventions are needed in Madagascar and Mozambique by 2014 as compared to the rest of the countries studied. In conclusion, the tool can be used to produce sensible and logical results which can be applied to achieve reduction of possible future malaria occurrences by governmental, NGOs and other relevant health agencies for proper public health planning

Cooperation between expert knowledge and data mining discovered knowledge: Lessons learned

Expert systems are built from knowledge traditionally elicited from the human expert. It is precisely knowledge elicitation from the expert that is the bottleneck in expert system construction. On the other hand, a data mining system, which automatically extracts knowledge, needs expert guidance on the successive decisions to be made in each of the system phases. In this context, expert knowledge and data mining discovered knowledge can cooperate, maximizing their individual capabilities: data mining discovered knowledge can be used as a complementary source of knowledge for the expert system, whereas expert knowledge can be used to guide the data mining process. This article summarizes different examples of systems where there is cooperation between expert knowledge and data mining discovered knowledge and reports our experience of such cooperation gathered from a medical diagnosis project called Intelligent Interpretation of Isokinetics Data, which we developed. From that experience, a series of lessons were learned throughout project development. Some of these lessons are generally applicable and others pertain exclusively to certain project types

Zebrafish Whole-Adult-Organism Chemogenomics for Large-Scale Predictive and Discovery Chemical Biology

The ability to perform large-scale, expression-based chemogenomics on whole adult organisms, as in invertebrate models (worm and fly), is highly desirable for a vertebrate model but its feasibility and potential has not been demonstrated. We performed expression-based chemogenomics on the whole adult organism of a vertebrate model, the zebrafish, and demonstrated its potential for large-scale predictive and discovery chemical biology. Focusing on two classes of compounds with wide implications to human health, polycyclic (halogenated) aromatic hydrocarbons [P(H)AHs] and estrogenic compounds (ECs), we generated robust prediction models that can discriminate compounds of the same class from those of different classes in two large independent experiments. The robust expression signatures led to the identification of biomarkers for potent aryl hydrocarbon receptor (AHR) and estrogen receptor (ER) agonists, respectively, and were validated in multiple targeted tissues. Knowledge-based data mining of human homologs of zebrafish genes revealed highly conserved chemical-induced biological responses/effects, health risks, and novel biological insights associated with AHR and ER that could be inferred to humans. Thus, our study presents an effective, high-throughput strategy of capturing molecular snapshots of chemical-induced biological states of a whole adult vertebrate that provides information on biomarkers of effects, deregulated signaling pathways, and possible affected biological functions, perturbed physiological systems, and increased health risks. These findings place zebrafish in a strategic position to bridge the wide gap between cell-based and rodent models in chemogenomics research and applications, especially in preclinical drug discovery and toxicology

A Cognitive Model of an Epistemic Community: Mapping the Dynamics of Shallow Lake Ecosystems

We used fuzzy cognitive mapping (FCM) to develop a generic shallow lake ecosystem model by augmenting the individual cognitive maps drawn by 8 scientists working in the area of shallow lake ecology. We calculated graph theoretical indices of the individual cognitive maps and the collective cognitive map produced by augmentation. The graph theoretical indices revealed internal cycles showing non-linear dynamics in the shallow lake ecosystem. The ecological processes were organized democratically without a top-down hierarchical structure. The steady state condition of the generic model was a characteristic turbid shallow lake ecosystem since there were no dynamic environmental changes that could cause shifts between a turbid and a clearwater state, and the generic model indicated that only a dynamic disturbance regime could maintain the clearwater state. The model developed herein captured the empirical behavior of shallow lakes, and contained the basic model of the Alternative Stable States Theory. In addition, our model expanded the basic model by quantifying the relative effects of connections and by extending it. In our expanded model we ran 4 simulations: harvesting submerged plants, nutrient reduction, fish removal without nutrient reduction, and biomanipulation. Only biomanipulation, which included fish removal and nutrient reduction, had the potential to shift the turbid state into clearwater state. The structure and relationships in the generic model as well as the outcomes of the management simulations were supported by actual field studies in shallow lake ecosystems. Thus, fuzzy cognitive mapping methodology enabled us to understand the complex structure of shallow lake ecosystems as a whole and obtain a valid generic model based on tacit knowledge of experts in the field.Comment: 24 pages, 5 Figure

Integrating Economic Knowledge in Data Mining Algorithms

The assessment of knowledge derived from databases depends on many factors. Decision makers often need to convince others about the correctness and effectiveness of knowledge induced from data.The current data mining techniques do not contribute much to this process of persuasion.Part of this limitation can be removed by integrating knowledge from experts in the field, encoded in some accessible way, with knowledge derived form patterns stored in the database.In this paper we will in particular discuss methods for implementing monotonicity constraints in economic decision problems.This prior knowledge is combined with data mining algorithms based on decision trees and neural networks.The method is illustrated in a hedonic price model.knowledge;neural network;data mining;decision trees

Toxicogenomic and Phenotypic Analyses of Bisphenol-A Early-Life Exposure Toxicity in Zebrafish

Bisphenol-A is an important environmental contaminant due to the increased early-life exposure that may pose significant health-risks to various organisms including humans. This study aimed to use zebrafish as a toxicogenomic model to capture transcriptomic and phenotypic changes for inference of signaling pathways, biological processes, physiological systems and identify potential biomarker genes that are affected by early-life exposure to bisphenol-A. Phenotypic analysis using wild-type zebrafish larvae revealed BPA early-life exposure toxicity caused cardiac edema, cranio-facial abnormality, failure of swimbladder inflation and poor tactile response. Fluorescent imaging analysis using three transgenic lines revealed suppressed neuron branching from the spinal cord, abnormal development of neuromast cells, and suppressed vascularization in the abdominal region. Using knowledge-based data mining algorithms, transcriptome analysis suggests that several signaling pathways involving ephrin receptor, clathrin-mediated endocytosis, synaptic long-term potentiation, axonal guidance, vascular endothelial growth factor, integrin and tight junction were deregulated. Physiological systems with related disorders associated with the nervous, cardiovascular, skeletal-muscular, blood and reproductive systems were implicated, hence corroborated with the phenotypic analysis. Further analysis identified a common set of BPA-targeted genes and revealed a plausible mechanism involving disruption of endocrine-regulated genes and processes in known susceptible tissue-organs. The expression of 28 genes were validated in a separate experiment using quantitative real-time PCR and 6 genes, ncl1, apoeb, mdm1, mycl1b, sp4, U1SNRNPBP homolog, were found to be sensitive and robust biomarkers for BPA early-life exposure toxicity. The susceptibility of sp4 to BPA perturbation suggests its role in altering brain development, function and subsequently behavior observed in laboratory animals exposed to BPA during early life, which is a health-risk concern of early life exposure in humans. The present study further established zebrafish as a model for toxicogenomic inference of early-life chemical exposure toxicity

Towards knowledge-based gene expression data mining

The field of gene expression data analysis has grown in the past few years from being purely data-centric to integrative, aiming at complementing microarray analysis with data and knowledge from diverse available sources. In this review, we report on the plethora of gene expression data mining techniques and focus on their evolution toward knowledge-based data analysis approaches. In particular, we discuss recent developments in gene expression-based analysis methods used in association and classification studies, phenotyping and reverse engineering of gene networks

User-centered visual analysis using a hybrid reasoning architecture for intensive care units

One problem pertaining to Intensive Care Unit information systems is that, in some cases, a very dense display of data can result. To ensure the overview and readability of the increasing volumes of data, some special features are required (e.g., data prioritization, clustering, and selection mechanisms) with the application of analytical methods (e.g., temporal data abstraction, principal component analysis, and detection of events). This paper addresses the problem of improving the integration of the visual and analytical methods applied to medical monitoring systems. We present a knowledge- and machine learning-based approach to support the knowledge discovery process with appropriate analytical and visual methods. Its potential benefit to the development of user interfaces for intelligent monitors that can assist with the detection and explanation of new, potentially threatening medical events. The proposed hybrid reasoning architecture provides an interactive graphical user interface to adjust the parameters of the analytical methods based on the users' task at hand. The action sequences performed on the graphical user interface by the user are consolidated in a dynamic knowledge base with specific hybrid reasoning that integrates symbolic and connectionist approaches. These sequences of expert knowledge acquisition can be very efficient for making easier knowledge emergence during a similar experience and positively impact the monitoring of critical situations. The provided graphical user interface incorporating a user-centered visual analysis is exploited to facilitate the natural and effective representation of clinical information for patient care

Sox2, a stemness gene, regulates tumor-initiating and drug-resistant properties in CD133-positive glioblastoma stem cells

AbstractBackgroundGlioblastoma multiforme (GBM) is the most lethal type of adult brain cancer and performs outrageous growth and resistance regardless of adjuvant chemotherapies, eventually contributing to tumor recurrence and poor outcomes. Considering the common heterogeneity of cancer cells, the imbalanced regulatory mechanism could be switched on/off and contribute to drug resistance. Moreover, the subpopulation of GBM cells was recently discovered to share similar phenotypes with neural stem cells. These cancer stem cells (CSCs) promote the potency of tumor initiation. As a result, targeting of glioma stem cells has become the dominant way of improving the therapeutic outcome against GBM and extending the life span of patients. Among the biomarkers of CSCs, CD-133 (prominin-1) has been known to effectively isolate CSCs from cancer population, including GBM; however, the underlying mechanism of how stemness genes manipulate CSC-associated phenotypes, such as tumor initiation and relapse, is still unclear.MethodsTumorigenicity, drug resistance and embryonic stem cell markers were examined in primary CD133-positive (CD133+) GBM cells and CD133+ subpopulation. Stemness signature of CD133+ GBM cells was identified using microarray analysis. Stem cell potency, tumorigenicity and drug resistance were also tested in differential expression of SOX2 in GBM cells.ResultsIn this study, high tumorigenic and drug resistance was noticed in primary CD-133+ GBM cells; meanwhile, plenty of embryonic stem cell markers were also elevated in the CD-133+ subpopulation. Using microarray analysis, we identified SOX2 as the most enriched gene among the stemness signature in CD133+ GBM cells. Overexpression of SOX2 consistently enhanced the stem cell potency in the GBM cell lines, whereas knockdown of SOX2 dramatically withdrew CD133 expression in CD133+ GBM cells. Additionally, we silenced SOX2 expression using RNAi system, which abrogated the ability of tumor initiation as well as drug resistance of CD133+ GBM cells, suggesting that SOX2 plays a crucial role in regulating tumorigenicity in CD133+ GBM cells.ConclusionSOX2 plays a crucial role in regulating tumorigenicity in CD133+ GBM cells. Our results not only revealed the genetic plasticity contributing to drug resistance and stemness but also demonstrated the dominant role of SOX2 in maintenance of GBM CSCs, which may provide a novel therapeutic target to overcome the conundrum of poor survival of brain cancers