APPLYING „IN SILICO“ TOXICOGENOMIC DATA MINING TO PREDICT MOLECULAR MECHANISMS AND PATHWAYS AGAINST CARCINOMA: IMMUNOMODULATOR SULFORAPHANE AS A CASE STUDY

2021 ◽  
Author(s):  
Dragica Božić ◽  
◽  
Katarina Živančević ◽  
Katarina , Baralić ◽  
Dragana Javorac ◽  
...  
Keyword(s):  
Data Mining ◽  
Tumor Cells ◽  
In Silico ◽  
Molecular Mechanisms ◽  
Antioxidative Activity ◽  
Biological Processes ◽  
Comparative Toxicogenomics Database ◽  
Oxidoreductase Activity ◽  
Different Types

The aim of this study was to predict the molecular mechanisms and pathways of immunomodulator sulforaphane (SFN) against carcinoma using in silico toxicogenomic data mining. Three key tools applied in our analysis were Comparative Toxicogenomics Database (CTD; http://CTD.mdibl. org), ToppGene Suite portal (https://toppgene.cchmc.org) and Reactome Knowledgebase (https://reactome.org). Sulforaphane interacted with a total of 1896, among which NFE2L2, NQO1, HMOX1, GCLC, TXNRD1, IL1B, IFNG, AGT, KEAP1, and CASP3 had the highest number of interactions. In the CTD, there were direct evidences that SFN interacts with a total of 169 genes to express a therapeutic effect against different types of cancer such as: hepatocellular carcinoma (113), colorectal neoplasms (67), uterine cervical neoplasms (10), and adenomatous polyposis coli (4). This set of genes was further uploaded into the Gene Mania software, ToppGene Suite portal, and Reactome Knowledgebase, which confirmed that molecular functions, biological processes and pathways of SFN-affected genes were mostly related to oxidoreductase activity, regulation of immune system, and apoptosis. In conclusion, we may suggest that SFN interacts with host immunity to enhance the eradication of tumor cells mainly by inducing immune-response and stimulating apoptotic process of tumor cells. Moreover, its antioxidative activity could contribute to better anti-cancerogenic effects.

ARSENIC ACCUMULATION IN PLANTS – OUTLINING STRATEGIES FOR DEVELOPING IMPROVED VARIETY OF CROPS FOR AVOIDING ARSENIC TOXICITY IN FOODS

2010 ◽  
Vol 18 (01) ◽  
pp. 223-241 ◽  
Author(s):  
DAN LUNDH ◽  
DENNIS LARSSON ◽  
NOOR NAHAR ◽  
ABUL MANDAL
Keyword(s):  
Data Mining ◽  
In Silico ◽  
System Biology ◽  
Cellular Localization ◽  
Molecular Mechanisms ◽  
Efflux Pump ◽  
Arsenate Reductase ◽  
Potential Health ◽  
System Biology Model ◽  
In Silico Studies

Contamination of food with arsenics is a potential health risk for both humans and animals in many regions of the world, especially in Asia. Arsenics can be accumulated in humans, animals and plants for a longer period and a long-term exposure of humans to arsenics results in severe damage of kidney, lever, heart etc. and many other vascular diseases. Arsenic contamination in human may also lead to development of cancer. In this paper we report our results on data mining approach (an in silico analysis based on searching of the existing genomic databases) for identification and characterization of genes that might be responsible for uptake, accumulation or metabolism of arsenics. For these in silico analyses we have involved the model plant Arabidopsis thaliana in our investigation. By employing a system biology model (a kinetic model) we have studied the molecular mechanisms of these processes in this plant. This model contains equations for uptake, metabolism and sequestration of different types of arsenic; As(V), As(III), MMAA and DMAA. The model was then implemented in the software XPP. The model was also validated against the data existing in the literatures. Based on the results of these in silico studies we have developed some strategies that can be used for reducing arsenic contents in different parts of the plant. Data mining experiments resulted in identification of two candidate genes (ACR2, arsenate reductase 2 and PCS1, phytochelatin synthase 1) that are involved either in uptake, transport or cellular localization of arsenic in A. thaliana. However, our system biology model revealed that by increasing the level of arsenate reductase together with an increased rate of arsenite sequestration in the vacuoles (by involving an arsenite efflux pump MRP1/2), it is possible to reduce the amount of arsenics in the shoots of A. thaliana to 11–12%.

ACCESSING DATA MINING RULES THROUGH EXPERT SYSTEMS

2002 ◽  
Vol 01 (04) ◽  
pp. 657-672 ◽  
Author(s):  
BASILIS BOUTSINAS
Keyword(s):  
Data Mining ◽  
Expert System ◽  
Expert Systems ◽  
Knowledge Discovery ◽  
Domain Knowledge ◽  
Research Area ◽  
General Purpose ◽  
Related Data ◽  
Different Types

Data mining is an emerging research area that develops techniques for knowledge discovery in huge volumes of data. Usually, data mining rules can be used either to classify data into predefined classes, or to partition a set of patterns into disjoint and homogeneous clusters, or to reveal frequent dependencies among data. The discovery of data mining rules would not be very useful unless there are mechanisms to help analysts access them in a meaningful way. Actually, documenting and reporting the extracted knowledge is of considerable importance for the successful application of data mining in practice. In this paper, we propose a methodology for accessing data mining rules, which is based on using an expert system. We present how the different types of data mining rules can be transformed into the domain knowledge of any general-purpose expert system. Then, we present how certain attribute values given by the user as facts and/or goals can determine, through a forward and/or backward chaining, the related data mining rules. In this paper, we also present a case study that demonstrates the applicability of the proposed methodology.

scholarly journals Integrating Multiple Microarray Data for Cancer Pathway Analysis Using Bootstrapping K-S Test

2009 ◽  
Vol 2009 ◽  
pp. 1-11 ◽  
Author(s):  
Bing Han ◽  
Xue-Wen Chen ◽  
Xinkun Wang ◽  
Elias K. Michaelis
Keyword(s):  
Microarray Data ◽  
Molecular Mechanisms ◽  
Large Set ◽  
Biological Processes ◽  
Different Types ◽  
Kolmogorov Smirnov ◽  
Integrative Method ◽  
The Relationship ◽  
Key Pathways ◽  
Smirnov Test

Previous applications of microarray technology for cancer research have mostly focused on identifying genes that are differentially expressed between a particular cancer and normal cells. In a biological system, genes perform different molecular functions and regulate various biological processes via interactions with other genes thus forming a variety of complex networks. Therefore, it is critical to understand the relationship (e.g., interactions) between genes across different types of cancer in order to gain insights into the molecular mechanisms of cancer. Here we propose an integrative method based on the bootstrapping Kolmogorov-Smirnov test and a large set of microarray data produced with various types of cancer to discover common molecular changes in cells from normal state to cancerous state. We evaluate our method using three key pathways related to cancer and demonstrate that it is capable of finding meaningful alterations in gene relations.

Elucidating the influence of environmentally relevant toxic metal mixture on molecular mechanisms involved in the development of neurodegenerative diseases: In silico toxicogenomic data-mining

2021 ◽  
Vol 194 ◽  
pp. 110727
Author(s):  
Katarina Živančević ◽  
Katarina Baralić ◽  
Dragica Jorgovanović ◽  
Aleksandra Buha Djordjević ◽  
Marijana Ćurčić ◽  
...  
Keyword(s):  
Data Mining ◽  
Neurodegenerative Diseases ◽  
In Silico ◽  
Molecular Mechanisms ◽  
Toxic Metal ◽  
Metal Mixture

In vivo data mining and in silico metabolic profiling to predict diverse hepatotoxic phenotypes: Case study of piperonyl butoxide

2015 ◽  
Vol 238 (2) ◽  
pp. S173 ◽  
Author(s):  
V. Vitcheva ◽  
A. Mostrag-Szlichtyng ◽  
O. Sacher ◽  
B. Bienfait ◽  
C.H. Schwab ◽  
...  
Keyword(s):  
Data Mining ◽  
Metabolic Profiling ◽  
In Silico ◽  
Piperonyl Butoxide

scholarly journals Exosomal Non-coding RNAs-Mediated Crosstalk in the Tumor Microenvironment

2021 ◽  
Vol 9 ◽  
Author(s):  
Qi Chen ◽  
Yuefeng Li ◽  
Yueqin Liu ◽  
Wenlin Xu ◽  
Xiaolan Zhu
Keyword(s):  
Tumor Microenvironment ◽  
Tumor Cells ◽  
Stromal Cells ◽  
Early Stage ◽  
Circular Rnas ◽  
Biological Processes ◽  
Tumor Cell Growth ◽  
Different Types ◽  
Non Coding Rnas ◽  
Regulatory Functions

Exosomes are secreted by different types of cells in tumor microenvironment (TME) and participate in multiple biological processes of tumors. Non-coding RNAs (ncRNAs) enveloped in exosomes and released to the TME are shown to be involved in tumorigenesis and development, as well as act as important intracellular communication mediators. However, the understanding on the exact regulatory functions and substrates of exosomal RNA is still at an early stage. In this review, we provided an overview on recent studies on exosomes mediating the modulation of both tumor cells and immune cells, then summarized the exosomal ncRNAs [such as microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs)] secreted by tumor cells and stromal cells that exhibited potential capabilities to regulate tumor cell growth, progression, metastasis, drug resistance, and immune response. Our review may hopefully inspire a deeper understanding on the ncRNAs’ function as useful biomarkers for the diagnosis, prognosis, and as novel targets therapy for cancer.

Participatory Mapping in Browns Canyon National Monument, Colorado (USA)

2017 ◽  
Vol 1 (1) ◽  
pp. 1-16
Author(s):  
John Harner ◽  
Lee Cerveny ◽  
Rebecca Gronewold
Keyword(s):  
Land Management ◽  
Planning Process ◽  
Management Plan ◽  
Bureau Of Land Management ◽  
National Monument ◽  
Resource Managers ◽  
Different Types ◽  
Us Forest Service ◽  
Natural Resource Managers

Natural resource managers need up-to-date information about how people interact with public lands and the meanings these places hold for use in planning and decision-making. This case study explains the use of public participatory Geographic Information System (GIS) to generate and analyze spatial patterns of the uses and values people hold for the Browns Canyon National Monument in Colorado. Participants drew on maps and answered questions at both live community meetings and online sessions to develop a series of maps showing detailed responses to different types of resource uses and landscape values. Results can be disaggregated by interaction types, different meaningful values, respondent characteristics, seasonality, or frequency of visit. The study was a test for the Bureau of Land Management and US Forest Service, who jointly manage the monument as they prepare their land management plan. If the information generated is as helpful throughout the entire planning process as initial responses seem, this protocol could become a component of the Bureau’s planning tool kit.

Penerapan Metode Klasifikasi Decision Tree dan Algoritma C4.5 dalam Memprediksi Kriteria Nasabah Kredit Mega Auto Finance

2020 ◽  
Vol 7 (2) ◽  
pp. 200
Author(s):  
Puji Santoso ◽  
Rudy Setiawan
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Microsoft Excel ◽  
Customer Data ◽  
Data Mining Techniques ◽  
C4.5 Algorithm ◽  
Marketing Costs ◽  
Excel Format ◽  
Data Mining Application

One of the tasks in the field of marketing finance is to analyze customer data to find out which customers have the potential to do credit again. The method used to analyze customer data is by classifying all customers who have completed their credit installments into marketing targets, so this method causes high operational marketing costs. Therefore this research was conducted to help solve the above problems by designing a data mining application that serves to predict the criteria of credit customers with the potential to lend (credit) to Mega Auto Finance. The Mega Auto finance Fund Section located in Kotim Regency is a place chosen by researchers as a case study, assuming the Mega Auto finance Fund Section has experienced the same problems as described above. Data mining techniques that are applied to the application built is a classification while the classification method used is the Decision Tree (decision tree). While the algorithm used as a decision tree forming algorithm is the C4.5 Algorithm. The data processed in this study is the installment data of Mega Auto finance loan customers in July 2018 in Microsoft Excel format. The results of this study are an application that can facilitate the Mega Auto finance Funds Section in obtaining credit marketing targets in the future

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