Mining Dataset Research Articles

RACGAP1 is transcriptionally regulated by E2F3, and its depletion leads to mitotic catastrophe in esophageal squamous cell carcinoma.

BackgroundRACGAP1 has significant involvement in tumorigenesis of cancers, including liver cancer, stomach cancer, and colon cancer. However, the role and the exact mechanism of RACGAP1 in esophageal squamous cell carcinoma (ESCC) has not been explored.MethodsQPCR and Western blots analysis was performed to analyze the expression of RACGAP1 in ESCC. MTT assays and colony formation assays were performed to explore the functional role of RACGAP1 in ESCC. Cell cycle analysis and immunofluorescence assays were used to investigate the function of RACGAP1 involvement in mitotic catastrophe. At last, we conducted the public datasets mining to explore the expression status and prognosis value of RACGAP1 as well as the correlation between RACGAP1 and E2F3 in various cancers.ResultsThe high abnormal expression of RACGAP1 is observed in ESCC and associated with worse clinical outcomes of patients with ESCC. RACGAP1, a novel cell cycle associated gene regulated by E2F3, acts as an oncogenic driver in ESCC cell lines. Notably, for the first time, RACGAP1 depletion induced severe mitotic catastrophe, followed by massive cell death.ConclusionsOur findings showed the essential role of RACGAP1 in ESCC cancer cell survival and the therapeutic potential of RACGAP1 as a molecular target for ESCC.

Discovering computer networks intrusion using data analytics and machine intelligence

In this era of a digital revolution, the use of the Internet for information storage, access, and dissemination has increased astronomically. Also, the advent of the Internet of Things (IoT) technologies has removed the digital barrier and accentuate the seamless exchange of data and information among many ubiquitous systems. Therefore, the challenge of information theft, privacy, and confidentiality of data and information over the internet has become a major quandary for many users of several online platforms. Network intrusion detection systems are one of the viable approaches to curb the menace of information theft and other data security threats over the internet. In this paper, we show a comparison between two intrusion detection systems–one that uses the association rule data mining approach–Apriori and the other that adapts the use of a machine learning technique–Support Vector Machine (SVM). The performance of the two systems was compared using the Network Security Laboratory Knowledge Discovery and Data Mining (NSL-KDD) dataset and the University of New South Wales–NB 2015 (UNSW-NB15) dataset. Evaluation results show that SVM performs better than Apriori in terms of accuracy, while Apriori gives a better performance in terms of testing speed.

Evolutionary History of Endogenous Human Herpesvirus 6 Reflects Human Migration out of Africa

Human herpesvirus 6A and 6B (HHV-6) can integrate into the germline, and as a result, ∼70 million people harbor the genome of one of these viruses in every cell of their body. Until now, it has been largely unknown if 1) these integrations are ancient, 2) if they still occur, and 3) whether circulating virus strains differ from integrated ones. Here, we used next-generation sequencing and mining of public human genome data sets to generate the largest and most diverse collection of circulating and integrated HHV-6 genomes studied to date. In genomes of geographically dispersed, only distantly related people, we identified clades of integrated viruses that originated from a single ancestral event, confirming this with fluorescent in situ hybridization to directly observe the integration locus. In contrast to HHV-6B, circulating and integrated HHV-6A sequences form distinct clades, arguing against ongoing integration of circulating HHV-6A or “reactivation” of integrated HHV-6A. Taken together, our study provides the first comprehensive picture of the evolution of HHV-6, and reveals that integration of heritable HHV-6 has occurred since the time of, if not before, human migrations out of Africa.

M1CR0B1AL1Z3R—a user-friendly web server for the analysis of large-scale microbial genomics data

Large-scale mining and analysis of bacterial datasets contribute to the comprehensive characterization of complex microbial dynamics within a microbiome and among different bacterial strains, e.g., during disease outbreaks. The study of large-scale bacterial evolutionary dynamics poses many challenges. These include data-mining steps, such as gene annotation, ortholog detection, sequence alignment, and phylogeny reconstruction. These steps require the use of multiple bioinformatics tools and ad-hoc programming scripts, making the entire process cumbersome, tedious and error-prone due to manual handling. This motivated us to develop the M1CR0B1AL1Z3R web server, a ‘one-stop shop’ for conducting microbial genomics data analyses via a simple graphical user interface (Avram, et al., Nucleic Acids Res., 2019). Some of the features implemented in M1CR0B1AL1Z3R are: (i) extracting putative open reading frames and comparative genomics analysis of gene content; (ii) extracting orthologous sets and analyzing their size distribution; (iii) analyzing gene presence-absence patterns; (iv) reconstructing a phylogenetic tree based on the extracted orthologous set; (v) inferring GC-content variation among lineages. M1CR0B1AL1Z3R facilitates the mining and analysis of dozens of bacterial genomes using advanced techniques, with the click of a button. M1CR0B1AL1Z3R is freely available at https://microbializer.tau.ac.il/ [https://microbializer.tau.ac.il/].

Study of Various Parallel Implementations of Association Rule Mining Algorithm

In data mining, Apriori technique is generally used for frequent itemsets mining and association rule learning over transactional databases. The frequent itemsets generated by the Apriori technique provides association rules which are used for finding trends in the database. As the size of the database increases, sequential implementation of Apriori technique will take a lot of time and at one point of time the system may crash. To overcome this problem, several algorithms for parallel implementation of Apriori technique have been proposed. This paper gives a comparative study on various parallel implementation of Apriori technique .It also focuses on the advantages of using the Map Reduce technology, the latest technology used in parallelization of large dataset mining.

Towards Geostatistical Learning for the Geosciences: A Case Study in Improving the Spatial Awareness of Spectral Clustering

The particularities of geosystems and geoscience data must be understood before any development or implementation of statistical learning algorithms. Without such knowledge, the predictions and inferences may not be accurate and physically consistent. Accuracy, transparency and interpretability, credibility, and physical realism are minimum criteria for statistical learning algorithms when applied to the geosciences. This study briefly reviews several characteristics of geoscience data and challenges for novel statistical learning algorithms. A novel spatial spectral clustering approach is introduced to illustrate how statistical learners can be adapted for modelling geoscience data. The spatial awareness and physical realism of the spectral clustering are improved by utilising a dissimilarity matrix based on nonparametric higher-order spatial statistics. The proposed model-free technique can identify meaningful spatial clusters (i.e. meaningful geographical subregions) from multivariate spatial data at different scales without the need to define a model of co-dependence. Several mixed (e.g. continuous and categorical) variables can be used as inputs to the proposed clustering technique. The proposed technique is illustrated using synthetic and real mining datasets. The results of the case studies confirm the usefulness of the proposed method for modelling spatial data.

AB0050 EXTENDED POLYDIMENSIONAL IMMUNOME CHARACTERISATION (EPIC) PLATFORM AS A TOOL FOR TRANSLATIONAL RESEARCH

Background:We created a high dimensionality healthy human Immunome atlas by interrogating the peripheral blood mononuclear cells (PBMC) of >200 healthy subjects (cord blood to adult) with 63 unique mechanistic and phenotypic markers per cell by mass cytometry (CyTOF). This database is built with an open source, web-based bioinformatics toolkit, enabling its mining and uploading of datasets for comparison with the EPIC healthy database.Objectives:Here, we demonstrate the platform’s ability to identify the immunological differences of mechanistically important cell subsets in the uploaded data in comparison with EPIC.Methods:CyTOF data from 37 healthy elderly (>60 years old) was uploaded onto the EPIC Discovery tool where down-sampling, normalising and FlowSOM (Flow analysis with Self-Organising Maps) clustering were done with the EPIC database for comparison. Online visualisation outputs include cluster frequency boxplots, correspondence analysis (CA) plot and markers expression heat-map. The CA 2-dimensional plot depicts the global differences in immune cells composition between subjects with proximity between points (subjects) denoting similarity. Kruskal-Wallis test was done to identify age groups differences.Results:Increasing distances on the CA plot with age were observed with the elderly being farthest from the new-borns. Notably, we observed significant changes in naive CD4+IL8+T cells (p<1×10-20), memory CD4+IL17A+T cells (p<1×10-20) and type 2 innate lymphoid cells (ILC2) (Lin-CD7+CD25+CD127+CD161+, p<1×10-17) with increasing age. The naive CD4+IL8+T cells (median: 0.68%, interquartile range: 0.415 to 1.055% of CD45+ PBMC) and ILC2 (0.09%, 0.065 to 0.12%) were lowest and memory IL17A+T cells (0.58%, 0.41 to 0.905%) highest in the elderly. Significantly, the memory IL17A+T cells and ILC2 have been implicated in the pathogenesis of auto-immune conditions1,2.Conclusion:With EPIC, we have created an online tool enabling data uploading for comparison to a healthy database, allowing the holistic characterisation of immunological changes in different clinical scenarios. Using it, we were able to identify mechanistically important differences in immune cells composition in a distinct clinical cohort (elderly) compared to the younger ages. Translationally, the EPIC platform can be utilised similarly to catalyse the discovery process in auto-immune diseases interrogated with the EPIC antibody panels.

Network Big Data Mining Algorithm Based on Association Rules of Computer Technology

With the development of cloud computing, it has provided a platform for big data presentation and sharing. These data often contain artificially added uncertain factors to prevent privacy leaks. How to perform mining of these big data is a problem to be solved urgently in big data sharing. In the big data for sharing, it is implemented through the generalization process of precise data, with the feature of uniform distribution, which is not conducive to exact query, but it can provide convenience for the mining of association rules. Firstly, based on the possible intersection or inclusion relationship, hierarchical clustering is performed on the generalized values. To save the essential information related to big dataset mining, we proposed an algorithm for building an uncertain frequent pattern tree. An improved algorithm of network big data mining based on association rules of computer technology is proposed in the big data environment, and its application in landslide monitoring and early warning is discussed.

A comparative study of Distributed Large Scale Data Mining Algorithms

Essentially, data mining concerns the computation of data and the identification of patterns and trends in the information so that we might decide or judge. Data mining concepts have been in use for years, but with the emergence of big data, they are even more common. In particular, the scalable mining of such large data sets is a difficult issue that has attached several recent findings. A few of these recent works use the MapReduce methodology to construct data mining models across the data set. In this article, we examine current approaches to large-scale data mining and compare their output to the MapReduce model. Based on our research, a system for data mining that combines MapReduce and sampling is implemented and addressed

Human Activity Recognition from Multiple Sensors Data Using Multi-fusion Representations and CNNs

With the emerging interest in the ubiquitous sensing field, it has become possible to build assistive technologies for persons during their daily life activities to provide personalized feedback and services. For instance, it is possible to detect an individual’s behavioral pattern (e.g., physical activity, location, and mood) by using sensors embedded in smart-watches and smartphones. The multi-sensor environments also come with some challenges, such as how to fuse and combine different sources of data. In this article, we explore several methods of fusion for multi-representations of data from sensors. Furthermore, multiple representations of sensor data were generated and then fused using data-level, feature-level , and decision-level fusions . The presented methods were evaluated using three publicly available human activity recognition (HAR) datasets. The presented approaches utilize Deep Convolutional Neural Networks (CNNs). A generic architecture for fusion of different sensors is proposed. The proposed method shows promising performance, with the best results reaching an overall accuracy of 98.4% for the Context-Awareness via Wrist-Worn Motion Sensors (HANDY) dataset and 98.7% for the Wireless Sensor Data Mining (WISDM version 1.1) dataset. Both results outperform previous approaches.

Distributed elephant herding optimization for grid-based privacy association rule mining

PurposeAssociation rule mining generates the patterns and correlations from the database, which requires large scanning time, and the cost of computation associated with the generation of the rules is quite high. On the other hand, the candidate rules generated using the traditional association rules mining face a huge challenge in terms of time and space, and the process is lengthy. In order to tackle the issues of the existing methods and to render the privacy rules, the paper proposes the grid-based privacy association rule mining.Design/methodology/approachThe primary intention of the research is to design and develop a distributed elephant herding optimization (EHO) for grid-based privacy association rule mining from the database. The proposed method of rule generation is processed as two steps: in the first step, the rules are generated using apriori algorithm, which is the effective association rule mining algorithm. In general, the extraction of the association rules from the input database is based on confidence and support that is replaced with new terms, such as probability-based confidence and holo-entropy. Thus, in the proposed model, the extraction of the association rules is based on probability-based confidence and holo-entropy. In the second step, the generated rules are given to the grid-based privacy rule mining, which produces privacy-dependent rules based on a novel optimization algorithm and grid-based fitness. The novel optimization algorithm is developed by integrating the distributed concept in EHO algorithm.FindingsThe experimentation of the method using the databases taken from the Frequent Itemset Mining Dataset Repository to prove the effectiveness of the distributed grid-based privacy association rule mining includes the retail, chess, T10I4D100K and T40I10D100K databases. The proposed method outperformed the existing methods through offering a higher degree of privacy and utility, and moreover, it is noted that the distributed nature of the association rule mining facilitates the parallel processing and generates the privacy rules without much computational burden. The rate of hiding capacity, the rate of information preservation and rate of the false rules generated for the proposed method are found to be 0.4468, 0.4488 and 0.0654, respectively, which is better compared with the existing rule mining methods.Originality/valueData mining is performed in a distributed manner through the grids that subdivide the input data, and the rules are framed using the apriori-based association mining, which is the modification of the standard apriori with the holo-entropy and probability-based confidence replacing the support and confidence in the standard apriori algorithm. The mined rules do not assure the privacy, and hence, the grid-based privacy rules are employed that utilize the adaptive elephant herding optimization (AEHO) for generating the privacy rules. The AEHO inherits the adaptive nature in the standard EHO, which renders the global optimal solution.

GERAKAN OPINI DIGITAL #TRUEBEAUTY PADA TWITTER UNTUK PEMERAN FILM ADAPTASI KOMIK WEBTOON

Digital Movement of Opinion (DMO) using hashtag #TrueBeauty on Twitter was conducted by the fans of True Beauty as one of the most popular Webtoon’s comic. The fans gave their opinion about the perfect cast for the movie adaption of the comic. The objective of this research is to analyse the network that was formed by the DMO of #TrueBeauty. The method that had been used was social network analysis and the datasets mining was done using Netlytic. This research indicates that fans as the actors of the DMO were spreaded widely across the network and not centralized into certain dominant actors. The actors was divided into different clusters and every cluster has its own characteristics based on different locations and cultures. Therefore the role of influencers or dominant actors in every cluster is very important to deliver opinions with a style that suits every community. Moreover, text analysis found that film industry should pay attention to social media opinion, because many of the opinion were reflects the original desires of every fan without any intermediaton from any parties.

Coverage-based query subtopic diversification leveraging semantic relevance

Generally, users are reserved in describing their search intention when submitting queries into the search engine. Therefore, a large number of search queries are usually short, ambiguous and tend to have multiple interpretations. With the gigantic size of the web, ignoring the information needs underlying such queries can misguide the search engine. To mitigate these issues, an effective approach is to diversify the search results considering the query subtopics with diverse intents. The task of identifying possible subtopics with diverse intents underlying a query is known as subtopic mining. This paper is aimed at mining and diversifying subtopics underlying a query. Our method first exacts noun phrases containing the query terms from the top-retrieved web documents. We also extract query suggestions and completions from commercial search engines. The extracted candidates highly related to the query are then selected as subtopics. We introduce a new relatedness score function to estimate the degree of relatedness between the query and the candidate. To estimate the relevancy between the query and the subtopic, this paper introduces a semantic relevance measure using a locally trained sentence embedding model. Finally, we propose a novel coverage-based diversification technique to rank the subtopics combining their relevancy and the coverage estimated by the web documents. The experimental results on two NTCIR English subtopic mining datasets demonstrate that our proposed method achieves new state-of-the-art performance and significantly outperforms some known related methods in terms of relevance (D-nDCG) and diversity (D#-nDCG) metric at cut of 10.

Proteomic and Transcriptomic Approaches for Studying Bone Regeneration in Health and Systemically Compromised Conditions.

Bone regeneration is a complex biological process, where the molecular mechanisms are only partially understood. In an ageing population, where the prevalence of chronic diseases with an impact on bone metabolism is increasing, it becomes crucial to identify new strategies that would improve regenerative outcomes also in medically compromised patients. In this context, omics are demonstrating a great potential, as they offer new insights on the molecular mechanisms regulating physiologic/pathologic bone healing and, at the same time, allow the identification of new diagnostic and therapeutic targets. This review provides an overview on the current evidence on the use of transcriptomic and proteomic approaches in bone regeneration research, particularly in relation to type 1 diabetes and osteoporosis, and discusses future scenarios and potential benefits and limitations on the integration of multi-omics. It is suggested that future research will leverage the synergy of omics with statistical modeling and bioinformatics to prompt the understanding of the biology underpinning bone formation in health and medically compromised conditions. With an eye toward personalized medicine, new strategies combining the mining of large datasets and bioinformatic data with a detailed characterization of relevant phenotypes will need to be pursued to further the understanding of disease mechanisms.

Lipidr: A Software Tool for Data Mining and Analysis of Lipidomics Datasets.

The rapid evolution of mass spectrometry (MS)-based lipidomics has enabled the simultaneous measurement of numerous lipid classes. With lipidomics datasets becoming increasingly available, lipidomic-focused software tools are required to facilitate data analysis as well as mining of public datasets, integrating lipidomics-unique molecular information such as lipid class, chain length, and unsaturation. To address this need, we developed lipidr, an open-source R/Bioconductor package for data mining and analysis of lipidomics datasets. lipidr implements a comprehensive lipidomic-focused analysis workflow for targeted and untargeted lipidomics. lipidr imports numerical matrices, Skyline exports, and Metabolomics Workbench files directly into R, automatically inferring lipid class and chain information from lipid names. Through integration with the Metabolomics Workbench API, users can search, download, and reanalyze public lipidomics datasets seamlessly. lipidr allows thorough data inspection, normalization, and uni- and multivariate analyses, displaying results as interactive visualizations. To enable interpretation of lipid class, chain length, and total unsaturation data, we also developed and implemented a novel lipid set enrichment analysis. A companion online guide with two live example datasets is presented at https://www.lipidr.org/. We expect that the ease of use and innovative features of lipidr will allow the lipidomics research community to gain novel detailed insights from lipidomics data.

Optimization of Mining–Mineral Processing Integration Using Unsupervised Machine Learning Algorithms

Traditional ore-waste discrimination schemes often do not take into consideration the impact of fluctuations of the head grade, which have on the performance of mineral processing facilities. This research introduces the use of target grades for processing destinations as an alternative to cut off grade-based methods and models, in each processing destination, the losses due to deviation from targets via the Taguchi loss function. Three unsupervised learning algorithms, k-means clustering, CLARA and k-mean-based approximate spectral clustering, are presented to group mine planning blocks into clusters of similar grades with different processing destinations. In addition, a technique considering uncertainties associated with block grades is proposed to generate new sequences that reduce variation in processing capacities across the life of mine (LoM). The case study in this paper involves the treatment of a realistically large mining dataset. The results showed that clustering methods outperform cutoff grade-based method when divergence from target grades is penalized and that reclassification of blocks based on data from geostatistical simulations could achieve smoother capacities for processing streams across the LoM.

I-Eclat: performance enhancement of eclat via incremental approach in frequent itemset mining

One example of the state-of-the-art vertical rule mining technique is called equivalence class transformation (Eclat) algorithm. Neither horizontal nor vertical data format, both are still suffering from the huge memory consumption. In response to the promising results of mining in a higher volume of data from a vertical format, and taking consideration of dynamic transaction of data in a database, the research proposes a performance enhancement of Eclat algorithm that relies on incremental approach called an Incremental-Eclat (i-Eclat) algorithm. Motivated from the fast intersection in Eclat, this algorithm of performance enhancement adopts via my structured query language (MySQL) database management system (DBMS) as its platform. It serves as the association rule mining database engine in testing benchmark frequent itemset mining (FIMI) datasets from online repository. The MySQL DBMS is chosen in order to reduce the preprocessing stages of datasets. The experimental results indicate that the proposed algorithm outperforms the traditional Eclat with 17% both in chess and T10I4D100K, 69% in mushroom, 5% and 8% in pumsb_star and retail datasets. Thus, among five (5) dense and sparse datasets, the average performance of i-Eclat is concluded to be 23% better than Eclat.

A Novel Approach for Predicting the Height of the Water-Flow Fracture Zone in Undersea Safety Mining

The height of the water-flow fracture zone (WFZ) is an important reference for designing the size of a waterproof crown pillar. Once the WFZ is connected with the sea, there will be catastrophic consequences, especially for undersea mining. This study suggests using a rotating forest (RoF) model to predict the height of the WFZ for the evaluation of the size of a waterproof crown pillar. To train and test the RoF model, five indicators with major influencing factors on undersea safety mining were determined, 107 field-measured mining datasets were collected, 75 (70%) datasets were used for training, and 32 (30%) datasets were used for model testing. At the same time, the random forest ensemble algorithm (RFR) and support vector machine (SVM) models were introduced for comparison and verification; in the end, the tested results were evaluated by RMSE (root-mean-square error) and R2. The comparison shows that the predicted results from the RoF model are significantly better than those from the RFR and SVM models. An importance analysis of the impact indicators shows that the mining height and depth have significant impacts on the prediction results. The development height of the WFZ in undersea safety mining was predicted via the RoF model. The predicted results via the RoF model were verified by field observations using panoramic borehole televiewers. The RoF prediction results are consistent with the observation results at all depths. Compared with the other two models, the RoF model has the smallest average absolute error at 2.87%. The results show that the RoF model can be applied to predict the height of the WFZ in undersea mining, which could be an effective way of minimizing the mineral resource waste in the study area and in other similar areas in the world under the premise of mine safety.

Mining genes associated with furanocoumarin biosynthesis in an endangered medicinal plant, Glehnia littoralis

The endangered medicinal plant Glehnia littoralis is one of the important natural source of furanocoumarin, which has been used as mucolytic, antitussive, antitumour and antibacterial. However, the genetic information of furanocoumarin biosynthesis in G. littoralis is scarce at present. The objective of this study was to mine the putative candidate genes involved in the biosynthesis pathwayof furanocoumarin and provide references for gene identification, and functional genomics of G. littoralis. We carried out the transcriptome analysis of leaves and roots in G. littoralis, which provided a dataset for gene mining. Psoralen, imperatorin and isoimperatorin were detected in G. littoralis by high performance liquid chromatography analysis. Candidate key genes were mined based on the annotations and local BLAST with homologous sequences using BioEdit software. The relative expression of genes was analysed using quantitative real-time polymerase chain reaction. Further, the CYP450 genes were mined using phylogenetic analyses using MEGA 6.0 software. Atotal of 156,949 unigenes were generated, of which 9021 were differentially-expressed between leaves and roots. A total of 82 unigenes encoding eight enzymes in furanocoumarin biosynthetic pathway were first obtained. Seven genes that encoded key enzymes in the downstream furanocoumarin biosynthetic pathway and expressed more in roots than leaves were screened. Twenty-six candidate CYP450 unigenes expressed abundantly in roots and were chiefly concentrated in CYP71, CYP85 and CYP72 clans. Finally, we filtered 102 differentially expressed transcription factors (TFs) unigenes. The transcriptome of G. littoralis was characterized which would help to elucidate the furanocoumarin biosynthetic pathway in G. littoralis and provide an invaluable resource for further study of furanocoumarin.

Human action recognition with deep learning and structural optimization using a hybrid heuristic algorithm

Human action recognition (HAR) is a popular subject for academic society and other stakeholders. Nowadays it has a wide-spread use for lots of practical applications such as for health, assistive living, elderly care, and so on. Both visual and sensor-based data can be used for HAR. Visual data includes video images, still images, skeleton images, etc., whilst sensor-based data is acquired as numerical data from devices such as accelerometer, gyroscope, and so on. Employed classification methods and data types are of crucial importance on HAR performance. In this paper, sensor data based activity recognition is performed using stacked autoencoders (SAE). Finding near optimal accuracy results with SAEs is a challenging process if structural optimization is left to user experience. The purpose of this study is to improve the accuracy of HAR classifying methods such as SAEs using heuristic optimization algorithms. Hence the structural parameters of SAEs have been optimized using artificial bee colony optimization algorithm (ABC), genetic algorithm, differential evolution algorithm, particle swarm optimization algorithm (PSO) and an afresh developed hybrid algorithm (hABCPSO) which includes PSO and ABC in its internal structure. Leave-one-out cross-validation (LOOCV) test method is used for validating the results. Each algorithm is performed for 30 runs and the results of these runs are analyzed by statistical methods in detail. According to experimental results, hABCPSO supported SAE gives the minimum error and is the most robustness algorithm among the others. Obtained success rates show that the proposed SAE achieved the best accuracy rate ever on UCI human activity recognition dataset and a close result to best on wireless sensor data mining dataset with regard to LOOCV test technique.