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A new machine learning technique for an accurate diagnosis of coronary artery disease

Abdar M, Książek W, Acharya UR, Tan RS, Makarenkov V, Pławiak P

Comput Methods Programs Biomed, 2019 Oct;179:104992.
PMID: 31443858 DOI: 10.1016/j.cmpb.2019.104992

BACKGROUND AND OBJECTIVE: Coronary artery disease (CAD) is one of the commonest diseases around the world. An early and accurate diagnosis of CAD allows a timely administration of appropriate treatment and helps to reduce the mortality. Herein, we describe an innovative machine learning methodology that enables an accurate detection of CAD and apply it to data collected from Iranian patients.
METHODS: We first tested ten traditional machine learning algorithms, and then the three-best performing algorithms (three types of SVM) were used in the rest of the study. To improve the performance of these algorithms, a data preprocessing with normalization was carried out. Moreover, a genetic algorithm and particle swarm optimization, coupled with stratified 10-fold cross-validation, were used twice: for optimization of classifier parameters and for parallel selection of features.
RESULTS: The presented approach enhanced the performance of all traditional machine learning algorithms used in this study. We also introduced a new optimization technique called N2Genetic optimizer (a new genetic training). Our experiments demonstrated that N2Genetic-nuSVM provided the accuracy of 93.08% and F1-score of 91.51% when predicting CAD outcomes among the patients included in a well-known Z-Alizadeh Sani dataset. These results are competitive and comparable to the best results in the field.
CONCLUSIONS: We showed that machine-learning techniques optimized by the proposed approach, can lead to highly accurate models intended for both clinical and research use.

Matched MeSH terms: Data Mining/statistics & numerical data
IAPSO-AIRS: A novel improved machine learning-based system for wart disease treatment

Abdar M, Wijayaningrum VN, Hussain S, Alizadehsani R, Plawiak P, Acharya UR, et al.

J Med Syst, 2019 Jun 07;43(7):220.
PMID: 31175462 DOI: 10.1007/s10916-019-1343-0

Wart disease (WD) is a skin illness on the human body which is caused by the human papillomavirus (HPV). This study mainly concentrates on common and plantar warts. There are various treatment methods for this disease, including the popular immunotherapy and cryotherapy methods. Manual evaluation of the WD treatment response is challenging. Furthermore, traditional machine learning methods are not robust enough in WD classification as they cannot deal effectively with small number of attributes. This study proposes a new evolutionary-based computer-aided diagnosis (CAD) system using machine learning to classify the WD treatment response. The main architecture of our CAD system is based on the combination of improved adaptive particle swarm optimization (IAPSO) algorithm and artificial immune recognition system (AIRS). The cross-validation protocol was applied to test our machine learning-based classification system, including five different partition protocols (K2, K3, K4, K5 and K10). Our database consisted of 180 records taken from immunotherapy and cryotherapy databases. The best results were obtained using the K10 protocol that provided the precision, recall, F-measure and accuracy values of 0.8908, 0.8943, 0.8916 and 90%, respectively. Our IAPSO system showed the reliability of 98.68%. It was implemented in Java, while integrated development environment (IDE) was implemented using NetBeans. Our encouraging results suggest that the proposed IAPSO-AIRS system can be employed for the WD management in clinical environment.

Matched MeSH terms: Data Mining
Fulltext Development and mining of a volatile organic compound database

Abdullah AA, Altaf-Ul-Amin M, Ono N, Sato T, Sugiura T, Morita AH, et al.

Biomed Res Int, 2015;2015:139254.
PMID: 26495281 DOI: 10.1155/2015/139254

Volatile organic compounds (VOCs) are small molecules that exhibit high vapor pressure under ambient conditions and have low boiling points. Although VOCs contribute only a small proportion of the total metabolites produced by living organisms, they play an important role in chemical ecology specifically in the biological interactions between organisms and ecosystems. VOCs are also important in the health care field as they are presently used as a biomarker to detect various human diseases. Information on VOCs is scattered in the literature until now; however, there is still no available database describing VOCs and their biological activities. To attain this purpose, we have developed KNApSAcK Metabolite Ecology Database, which contains the information on the relationships between VOCs and their emitting organisms. The KNApSAcK Metabolite Ecology is also linked with the KNApSAcK Core and KNApSAcK Metabolite Activity Database to provide further information on the metabolites and their biological activities. The VOC database can be accessed online.

Matched MeSH terms: Data Mining/methods*
Fulltext Intelligent bar chart plagiarism detection in documents

Al-Dabbagh MM, Salim N, Rehman A, Alkawaz MH, Saba T, Al-Rodhaan M, et al.

ScientificWorldJournal, 2014;2014:612787.
PMID: 25309952 DOI: 10.1155/2014/612787

This paper presents a novel features mining approach from documents that could not be mined via optical character recognition (OCR). By identifying the intimate relationship between the text and graphical components, the proposed technique pulls out the Start, End, and Exact values for each bar. Furthermore, the word 2-gram and Euclidean distance methods are used to accurately detect and determine plagiarism in bar charts.

Matched MeSH terms: Data Mining/methods*
Fulltext Diabetes disease prediction system using HNB classifier based on discretization method

Al-Hameli BA, Alsewari AA, Basurra SS, Bhogal J, Ali MAH

J Integr Bioinform, 2023 Mar 01;20(1).
PMID: 36810102 DOI: 10.1515/jib-2021-0037

Diagnosing diabetes early is critical as it helps patients live with the disease in a healthy way - through healthy eating, taking appropriate medical doses, and making patients more vigilant in their movements/activities to avoid wounds that are difficult to heal for diabetic patients. Data mining techniques are typically used to detect diabetes with high confidence to avoid misdiagnoses with other chronic diseases whose symptoms are similar to diabetes. Hidden Naïve Bayes is one of the algorithms for classification, which works under a data-mining model based on the assumption of conditional independence of the traditional Naïve Bayes. The results from this research study, which was conducted on the Pima Indian Diabetes (PID) dataset collection, show that the prediction accuracy of the HNB classifier achieved 82%. As a result, the discretization method increases the performance and accuracy of the HNB classifier.

Matched MeSH terms: Data Mining
Fulltext Role of biological Data Mining and Machine Learning Techniques in Detecting and Diagnosing the Novel Coronavirus (COVID-19): A Systematic Review

Albahri AS, Hamid RA, Alwan JK, Al-Qays ZT, Zaidan AA, Zaidan BB, et al.

J Med Syst, 2020 May 25;44(7):122.
PMID: 32451808 DOI: 10.1007/s10916-020-01582-x

Coronaviruses (CoVs) are a large family of viruses that are common in many animal species, including camels, cattle, cats and bats. Animal CoVs, such as Middle East respiratory syndrome-CoV, severe acute respiratory syndrome (SARS)-CoV, and the new virus named SARS-CoV-2, rarely infect and spread among humans. On January 30, 2020, the International Health Regulations Emergency Committee of the World Health Organisation declared the outbreak of the resulting disease from this new CoV called 'COVID-19', as a 'public health emergency of international concern'. This global pandemic has affected almost the whole planet and caused the death of more than 315,131 patients as of the date of this article. In this context, publishers, journals and researchers are urged to research different domains and stop the spread of this deadly virus. The increasing interest in developing artificial intelligence (AI) applications has addressed several medical problems. However, such applications remain insufficient given the high potential threat posed by this virus to global public health. This systematic review addresses automated AI applications based on data mining and machine learning (ML) algorithms for detecting and diagnosing COVID-19. We aimed to obtain an overview of this critical virus, address the limitations of utilising data mining and ML algorithms, and provide the health sector with the benefits of this technique. We used five databases, namely, IEEE Xplore, Web of Science, PubMed, ScienceDirect and Scopus and performed three sequences of search queries between 2010 and 2020. Accurate exclusion criteria and selection strategy were applied to screen the obtained 1305 articles. Only eight articles were fully evaluated and included in this review, and this number only emphasised the insufficiency of research in this important area. After analysing all included studies, the results were distributed following the year of publication and the commonly used data mining and ML algorithms. The results found in all papers were discussed to find the gaps in all reviewed papers. Characteristics, such as motivations, challenges, limitations, recommendations, case studies, and features and classes used, were analysed in detail. This study reviewed the state-of-the-art techniques for CoV prediction algorithms based on data mining and ML assessment. The reliability and acceptability of extracted information and datasets from implemented technologies in the literature were considered. Findings showed that researchers must proceed with insights they gain, focus on identifying solutions for CoV problems, and introduce new improvements. The growing emphasis on data mining and ML techniques in medical fields can provide the right environment for change and improvement.

Matched MeSH terms: Data Mining/methods*
Machine learning-based coronary artery disease diagnosis: A comprehensive review

Alizadehsani R, Abdar M, Roshanzamir M, Khosravi A, Kebria PM, Khozeimeh F, et al.

Comput Biol Med, 2019 08;111:103346.
PMID: 31288140 DOI: 10.1016/j.compbiomed.2019.103346

Coronary artery disease (CAD) is the most common cardiovascular disease (CVD) and often leads to a heart attack. It annually causes millions of deaths and billions of dollars in financial losses worldwide. Angiography, which is invasive and risky, is the standard procedure for diagnosing CAD. Alternatively, machine learning (ML) techniques have been widely used in the literature as fast, affordable, and noninvasive approaches for CAD detection. The results that have been published on ML-based CAD diagnosis differ substantially in terms of the analyzed datasets, sample sizes, features, location of data collection, performance metrics, and applied ML techniques. Due to these fundamental differences, achievements in the literature cannot be generalized. This paper conducts a comprehensive and multifaceted review of all relevant studies that were published between 1992 and 2019 for ML-based CAD diagnosis. The impacts of various factors, such as dataset characteristics (geographical location, sample size, features, and the stenosis of each coronary artery) and applied ML techniques (feature selection, performance metrics, and method) are investigated in detail. Finally, the important challenges and shortcomings of ML-based CAD diagnosis are discussed.

Matched MeSH terms: Data Mining
Fulltext A novel association rule mining approach using TID intermediate itemset

Aqra I, Herawan T, Abdul Ghani N, Akhunzada A, Ali A, Bin Razali R, et al.

PLoS One, 2018;13(1):e0179703.
PMID: 29351287 DOI: 10.1371/journal.pone.0179703

Designing an efficient association rule mining (ARM) algorithm for multilevel knowledge-based transactional databases that is appropriate for real-world deployments is of paramount concern. However, dynamic decision making that needs to modify the threshold either to minimize or maximize the output knowledge certainly necessitates the extant state-of-the-art algorithms to rescan the entire database. Subsequently, the process incurs heavy computation cost and is not feasible for real-time applications. The paper addresses efficiently the problem of threshold dynamic updation for a given purpose. The paper contributes by presenting a novel ARM approach that creates an intermediate itemset and applies a threshold to extract categorical frequent itemsets with diverse threshold values. Thus, improving the overall efficiency as we no longer needs to scan the whole database. After the entire itemset is built, we are able to obtain real support without the need of rebuilding the itemset (e.g. Itemset list is intersected to obtain the actual support). Moreover, the algorithm supports to extract many frequent itemsets according to a pre-determined minimum support with an independent purpose. Additionally, the experimental results of our proposed approach demonstrate the capability to be deployed in any mining system in a fully parallel mode; consequently, increasing the efficiency of the real-time association rules discovery process. The proposed approach outperforms the extant state-of-the-art and shows promising results that reduce computation cost, increase accuracy, and produce all possible itemsets.

Matched MeSH terms: Data Mining/methods*
Allele Mining Strategies: Principles and Utilisation for Blast Resistance Genes in Rice (Oryza sativa L.)

Ashkani S, Yusop MR, Shabanimofrad M, Azady A, Ghasemzadeh A, Azizi P, et al.

Curr Issues Mol Biol, 2015;17:57-73.
PMID: 25706446

Allele mining is a promising way to dissect naturally occurring allelic variants of candidate genes with essential agronomic qualities. With the identification, isolation and characterisation of blast resistance genes in rice, it is now possible to dissect the actual allelic variants of these genes within an array of rice cultivars via allele mining. Multiple alleles from the complex locus serve as a reservoir of variation to generate functional genes. The routine sequence exchange is one of the main mechanisms of R gene evolution and development. Allele mining for resistance genes can be an important method to identify additional resistance alleles and new haplotypes along with the development of allele-specific markers for use in marker-assisted selection. Allele mining can be visualised as a vital link between effective utilisation of genetic and genomic resources in genomics-driven modern plant breeding. This review studies the actual concepts and potential of mining approaches for the discovery of alleles and their utilisation for blast resistance genes in rice. The details provided here will be important to provide the rice breeder with a worthwhile introduction to allele mining and its methodology for breakthrough discovery of fresh alleles hidden in hereditary diversity, which is vital for crop improvement.

Matched MeSH terms: Data Mining/methods*
Fulltext Order batching in warehouses by minimizing total tardiness: a hybrid approach of weighted association rule mining and genetic algorithms

Azadnia AH, Taheri S, Ghadimi P, Saman MZ, Wong KY

ScientificWorldJournal, 2013;2013:246578.
PMID: 23864823 DOI: 10.1155/2013/246578

One of the cost-intensive issues in managing warehouses is the order picking problem which deals with the retrieval of items from their storage locations in order to meet customer requests. Many solution approaches have been proposed in order to minimize traveling distance in the process of order picking. However, in practice, customer orders have to be completed by certain due dates in order to avoid tardiness which is neglected in most of the related scientific papers. Consequently, we proposed a novel solution approach in order to minimize tardiness which consists of four phases. First of all, weighted association rule mining has been used to calculate associations between orders with respect to their due date. Next, a batching model based on binary integer programming has been formulated to maximize the associations between orders within each batch. Subsequently, the order picking phase will come up which used a Genetic Algorithm integrated with the Traveling Salesman Problem in order to identify the most suitable travel path. Finally, the Genetic Algorithm has been applied for sequencing the constructed batches in order to minimize tardiness. Illustrative examples and comparisons are presented to demonstrate the proficiency and solution quality of the proposed approach.

Matched MeSH terms: Data Mining/methods*
Modelling gully-erosion susceptibility in a semi-arid region, Iran: Investigation of applicability of certainty factor and maximum entropy models

Azareh A, Rahmati O, Rafiei-Sardooi E, Sankey JB, Lee S, Shahabi H, et al.

Sci Total Environ, 2019 Mar 10;655:684-696.
PMID: 30476849 DOI: 10.1016/j.scitotenv.2018.11.235

Gully erosion susceptibility mapping is a fundamental tool for land-use planning aimed at mitigating land degradation. However, the capabilities of some state-of-the-art data-mining models for developing accurate maps of gully erosion susceptibility have not yet been fully investigated. This study assessed and compared the performance of two different types of data-mining models for accurately mapping gully erosion susceptibility at a regional scale in Chavar, Ilam, Iran. The two methods evaluated were: Certainty Factor (CF), a bivariate statistical model; and Maximum Entropy (ME), an advanced machine learning model. Several geographic and environmental factors that can contribute to gully erosion were considered as predictor variables of gully erosion susceptibility. Based on an existing differential GPS survey inventory of gully erosion, a total of 63 eroded gullies were spatially randomly split in a 70:30 ratio for use in model calibration and validation, respectively. Accuracy assessments completed with the receiver operating characteristic curve method showed that the ME-based regional gully susceptibility map has an area under the curve (AUC) value of 88.6% whereas the CF-based map has an AUC of 81.8%. According to jackknife tests that were used to investigate the relative importance of predictor variables, aspect, distance to river, lithology and land use are the most influential factors for the spatial distribution of gully erosion susceptibility in this region of Iran. The gully erosion susceptibility maps produced in this study could be useful tools for land managers and engineers tasked with road development, urbanization and other future development.

Matched MeSH terms: Data Mining
Fulltext Bioactive Molecule Prediction Using Extreme Gradient Boosting

Babajide Mustapha I, Saeed F

Molecules, 2016 Jul 28;21(8).
PMID: 27483216 DOI: 10.3390/molecules21080983

Following the explosive growth in chemical and biological data, the shift from traditional methods of drug discovery to computer-aided means has made data mining and machine learning methods integral parts of today's drug discovery process. In this paper, extreme gradient boosting (Xgboost), which is an ensemble of Classification and Regression Tree (CART) and a variant of the Gradient Boosting Machine, was investigated for the prediction of biological activity based on quantitative description of the compound's molecular structure. Seven datasets, well known in the literature were used in this paper and experimental results show that Xgboost can outperform machine learning algorithms like Random Forest (RF), Support Vector Machines (LSVM), Radial Basis Function Neural Network (RBFN) and Naïve Bayes (NB) for the prediction of biological activities. In addition to its ability to detect minority activity classes in highly imbalanced datasets, it showed remarkable performance on both high and low diversity datasets.

Matched MeSH terms: Data Mining/methods*
Enhancing nitrate and strontium concentration prediction in groundwater by using new data mining algorithm

Bui DT, Khosravi K, Karimi M, Busico G, Khozani ZS, Nguyen H, et al.

Sci Total Environ, 2020 May 01;715:136836.
PMID: 32007881 DOI: 10.1016/j.scitotenv.2020.136836

Groundwater resources constitute the main source of clean fresh water for domestic use and it is essential for food production in the agricultural sector. Groundwater has a vital role for water supply in the Campanian Plain in Italy and hence a future sustainability of the resource is essential for the region. In the current paper novel data mining algorithms including Gaussian Process (GP) were used in a large groundwater quality database to predict nitrate (contaminant) and strontium (potential future increasing) concentrations in groundwater. The results were compared with M5P, random forest (RF) and random tree (RT) algorithms as a benchmark to test the robustness of the modeling process. The dataset includes 246 groundwater quality samples originating from different wells, municipals and agricultural. It was divided for the modeling process into two subgroups by using the 10-fold cross validation technique including 173 samples for model building (training dataset) and 73 samples for model validation (testing dataset). Different water quality variables including T, pH, EC, HCO3-, F-, Cl-, SO42-, Na+, K+, Mg2+, and Ca2+ have been used as an input to the models. At first stage, different input combinations have been constructed based on correlation coefficient and thus the optimal combination was chosen for the modeling phase. Different quantitative criteria alongside with visual comparison approach have been used for evaluating the modeling capability. Results revealed that to obtain reliable results also variables with low correlation should be considered as an input to the models together with those variables showing high correlation coefficients. According to the model evaluation criteria, GP algorithm outperforms all the other models in predicting both nitrate and strontium concentrations followed by RF, M5P and RT, respectively. Result also revealed that model's structure together with the accuracy and structure of the data can have a relevant impact on the model's results.

Matched MeSH terms: Data Mining
Natural Sirtuin Modulators in Drug Discovery: A Review (2010 -2020)

Chang Y, Yeong KY

Curr Med Chem, 2021 Mar 29.
PMID: 33781187 DOI: 10.2174/0929867328666210329124415

There have been intense research interests in sirtuins since the establishment of their regulatory roles in a myriad of pathological processes. In the last two decades, much research efforts have been dedicated to the development of sirtuin modulators. Although synthetic sirtuin modulators are the focus, natural modulators remain an integral part to be further explored in this area as they are found to possess therapeutic potential in various diseases including cancers, neurodegenerative diseases, and metabolic disorders. Owing to the importance of this cluster of compounds, this review gives a current stand on the naturally occurring sirtuin modulators, , associated molecular mechanisms and their therapeutic benefits.. Furthermore, comprehensive data mining resulted in detailed statistical data analyses pertaining to the development trend of sirtuin modulators from 2010-2020. Lastly, the challenges and future prospect of natural sirtuin modulators in drug discovery will also be discussed.

Matched MeSH terms: Data Mining
Fulltext Statistical estimators as an alternative to standard deviation in weighted euclidean distance cluster analysis

Dalatu, Paul Inuwa, Habshah Midi

Pertanika Journal of Science & Technology, 2018;26(4):1823-1836.
MyJurnal

Clustering is basically one of the major sources of primary data mining tools. It makes
researchers understand the natural grouping of attributes in datasets. Clustering is an
unsupervised classification method with the major aim of partitioning, where objects in the
same cluster are similar, and objects which belong to different clusters vary significantly,
with respect to their attributes. However, the classical Standardized Euclidean distance,
which uses standard deviation to down weight maximum points of the ith features on the
distance clusters, has been criticized by many scholars that the method produces outliers,
lack robustness, and has 0% breakdown points. It also has low efficiency in normal
distribution. Therefore, to remedy the problem, we suggest two statistical estimators
which have 50% breakdown points namely the Sn and Qn estimators, with 58% and 82%
efficiency, respectively. The proposed methods evidently outperformed the existing methods
in down weighting the maximum points of the ith features in distance-based clustering
analysis.

Matched MeSH terms: Data Mining
Fulltext Pooling and expanding registries of familial hypercholesterolaemia to assess gaps in care and improve disease management and outcomes: Rationale and design of the global EAS Familial Hypercholesterolaemia Studies Collaboration

EAS Familial Hypercholesterolaemia Studies Collaboration, Vallejo-Vaz AJ, Akram A, Kondapally Seshasai SR, Cole D, Watts GF, et al.

Atheroscler Suppl, 2016 Dec;22:1-32.
PMID: 27939304 DOI: 10.1016/j.atherosclerosissup.2016.10.001

The potential for global collaborations to better inform public health policy regarding major non-communicable diseases has been successfully demonstrated by several large-scale international consortia. However, the true public health impact of familial hypercholesterolaemia (FH), a common genetic disorder associated with premature cardiovascular disease, is yet to be reliably ascertained using similar approaches. The European Atherosclerosis Society FH Studies Collaboration (EAS FHSC) is a new initiative of international stakeholders which will help establish a global FH registry to generate large-scale, robust data on the burden of FH worldwide.

Matched MeSH terms: Data Mining
Fulltext Chemical named entities recognition: a review on approaches and applications

Eltyeb S, Salim N

J Cheminform, 2014;6:17.
PMID: 24834132 DOI: 10.1186/1758-2946-6-17

The rapid increase in the flow rate of published digital information in all disciplines has resulted in a pressing need for techniques that can simplify the use of this information. The chemistry literature is very rich with information about chemical entities. Extracting molecules and their related properties and activities from the scientific literature to "text mine" these extracted data and determine contextual relationships helps research scientists, particularly those in drug development. One of the most important challenges in chemical text mining is the recognition of chemical entities mentioned in the texts. In this review, the authors briefly introduce the fundamental concepts of chemical literature mining, the textual contents of chemical documents, and the methods of naming chemicals in documents. We sketch out dictionary-based, rule-based and machine learning, as well as hybrid chemical named entity recognition approaches with their applied solutions. We end with an outlook on the pros and cons of these approaches and the types of chemical entities extracted.

Matched MeSH terms: Data Mining
Characterization of spatial patterns in river water quality using chemometric pattern recognition techniques

Gazzaz NM, Yusoff MK, Ramli MF, Aris AZ, Juahir H

Mar Pollut Bull, 2012 Apr;64(4):688-98.
PMID: 22330076 DOI: 10.1016/j.marpolbul.2012.01.032

This study employed three chemometric data mining techniques (factor analysis (FA), cluster analysis (CA), and discriminant analysis (DA)) to identify the latent structure of a water quality (WQ) dataset pertaining to Kinta River (Malaysia) and to classify eight WQ monitoring stations along the river into groups of similar WQ characteristics. FA identified the WQ parameters responsible for variations in Kinta River's WQ and accentuated the roles of weathering and surface runoff in determining the river's WQ. CA grouped the monitoring locations into a cluster of low levels of water pollution (the two uppermost monitoring stations) and another of relatively high levels of river pollution (the mid-, and down-stream stations). DA confirmed these clusters and produced a discriminant function which can predict the cluster membership of new and/or unknown samples. These chemometric techniques highlight the potential for reasonably reducing the number of WQVs and monitoring stations for long-term monitoring purposes.

Matched MeSH terms: Data Mining
Gaining Insights on Nasopharyngeal Carcinoma Treatment Outcome Using Clinical Data Mining Techniques

Ghaibeh AA, Kasem A, Ng XJ, Nair HLK, Hirose J, Thiruchelvam V

Stud Health Technol Inform, 2018;247:386-390.
PMID: 29677988

The analysis of Electronic Health Records (EHRs) is attracting a lot of research attention in the medical informatics domain. Hospitals and medical institutes started to use data mining techniques to gain new insights from the massive amounts of data that can be made available through EHRs. Researchers in the medical field have often used descriptive statistics and classical statistical methods to prove assumed medical hypotheses. However, discovering new insights from large amounts of data solely based on experts' observations is difficult. Using data mining techniques and visualizations, practitioners can find hidden knowledge, identify interesting patterns, or formulate new hypotheses to be further investigated. This paper describes a work in progress on using data mining methods to analyze clinical data of Nasopharyngeal Carcinoma (NPC) cancer patients. NPC is the fifth most common cancer among Malaysians, and the data analyzed in this study was collected from three states in Malaysia (Kuala Lumpur, Sabah and Sarawak), and is considered to be the largest up-to-date dataset of its kind. This research is addressing the issue of cancer recurrence after the completion of radiotherapy and chemotherapy treatment. We describe the procedure, problems, and insights gained during the process.

Matched MeSH terms: Data Mining*
Fulltext Application of an Effective Statistical Technique for an Accurate and Powerful Mining of Quantitative Trait Loci for Rice Aroma Trait

Golestan Hashemi FS, Rafii MY, Ismail MR, Mohamed MT, Rahim HA, Latif MA, et al.

PLoS One, 2015;10(6):e0129069.
PMID: 26061689 DOI: 10.1371/journal.pone.0129069

When a phenotype of interest is associated with an external/internal covariate, covariate inclusion in quantitative trait loci (QTL) analyses can diminish residual variation and subsequently enhance the ability of QTL detection. In the in vitro synthesis of 2-acetyl-1-pyrroline (2AP), the main fragrance compound in rice, the thermal processing during the Maillard-type reaction between proline and carbohydrate reduction produces a roasted, popcorn-like aroma. Hence, for the first time, we included the proline amino acid, an important precursor of 2AP, as a covariate in our QTL mapping analyses to precisely explore the genetic factors affecting natural variation for rice scent. Consequently, two QTLs were traced on chromosomes 4 and 8. They explained from 20% to 49% of the total aroma phenotypic variance. Additionally, by saturating the interval harboring the major QTL using gene-based primers, a putative allele of fgr (major genetic determinant of fragrance) was mapped in the QTL on the 8th chromosome in the interval RM223-SCU015RM (1.63 cM). These loci supported previous studies of different accessions. Such QTLs can be widely used by breeders in crop improvement programs and for further fine mapping. Moreover, no previous studies and findings were found on simultaneous assessment of the relationship among 2AP, proline and fragrance QTLs. Therefore, our findings can help further our understanding of the metabolomic and genetic basis of 2AP biosynthesis in aromatic rice.

Matched MeSH terms: Data Mining

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