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1 |
Decoding and targeting the molecular basis of MACC1-driven metastatic spread: Lessons from big data mining and clinical-experimental approaches
رمزگشایی و هدف قرار دادن اساس مولکولی گسترش متاستاتیک MACC1 محور: درسهایی از کاوش داده های بزرگ و رویکردهای بالینی-تجربی-2019 Metastasis remains the key issue impacting cancer patient survival and failure or success of cancer therapies.
Metastatic spread is a complex process including dissemination of single cells or collective cell migration, penetration
of the blood or lymphatic vessels and seeding at a distant organ site. Hundreds of genes involved in
metastasis have been identified in studies across numerous cancer types. Here, we analyzed how the metastasisassociated
gene MACC1 cooperates with other genes in metastatic spread and how these coactions could be
exploited by combination therapies: We performed (i) a MACC1 correlation analysis across 33 cancer types in the
mRNA expression data of TCGA and (ii) a comprehensive literature search on reported MACC1 combinations and
regulation mechanisms. The key genes MET, HGF and MMP7 reported together with MACC1 showed significant
positive correlations with MACC1 in more than half of the cancer types included in the big data analysis.
However, ten other genes also reported together with MACC1 in the literature showed significant positive
correlations with MACC1 in only a minority of 5 to 15 cancer types. To uncover transcriptional regulation mechanisms that are activated simultaneously with MACC1, we isolated pan-cancer consensus lists of 1306
positively and 590 negatively MACC1-correlating genes from the TCGA data and analyzed each of these lists for
sharing transcription factor binding motifs in the promotor region. In these lists, binding sites for the transcription
factors TELF1, ETS2, ETV4, TEAD1, FOXO4, NFE2L1, ELK1, SP1 and NFE2L2 were significantly enriched,
but none of them except SP1 was reported in combination with MACC1 in the literature. Thus, while
some of the results of the big data analysis were in line with the reported experimental results, hypotheses on
new genes involved in MACC1-driven metastasis formation could be generated and warrant experimental validation.
Furthermore, the results of the big data analysis can help to prioritize cancer types for experimental
studies and testing of combination therapies. Keywords: MACC1 | Big data analyses | Cancer prognosis and prediction | Biomarker combination | Combinatorial therapy |
مقاله انگلیسی |
2 |
A disease diagnosis and treatment recommendation system based on big data mining and cloud computing
سیستم تشخیص بیماری و درمان مبتنی بر کاوش داده های بزرگ و محاسبات ابری-2018 It is crucial to provide compatible treatment schemes for a disease according to various
symptoms at different stages. However, most classification methods might be ineffective in
accurately classifying a disease that holds the characteristics of multiple treatment stages,
various symptoms, and multi-pathogenesis. Moreover, there are limited exchanges and co
operative actions in disease diagnoses and treatments between different departments and
hospitals. Thus, when new diseases occur with atypical symptoms, inexperienced doctors
might have difficulty in identifying them promptly and accurately. Therefore, to maximize
the utilization of the advanced medical technology of developed hospitals and the rich
medical knowledge of experienced doctors, a Disease Diagnosis and Treatment Recommen
dation System (DDTRS) is proposed in this paper. First, to effectively identify disease symp
toms more accurately, a Density-Peaked Clustering Analysis (DPCA) algorithm is introduced
for disease-symptom clustering. In addition, association analyses on Disease-Diagnosis (D
D) rules and Disease-Treatment (D-T) rules are conducted by the Apriori algorithm sep
arately. The appropriate diagnosis and treatment schemes are recommended for patients
and inexperienced doctors, even if they are in a limited therapeutic environment. More
over, to reach the goals of high performance and low latency response, we implement
a parallel solution for DDTRS using the Apache Spark cloud platform. Extensive experi
mental results demonstrate that the proposed DDTRS realizes disease-symptom clustering
effectively and derives disease treatment recommendations intelligently and accurately.
Keywords: Big data mining ، Cloud computing ، Disease diagnosis and treatment ، Recommendation system |
مقاله انگلیسی |
3 |
Analyze the energy consumption characteristics and affecting factors of Taiwan’s convenience stores-using the big data mining approach
ویژگی های مصرف انرژی و عوامل موثر را تحلیل کنیداز فروشگاه های راحت تایوان - با استفاده از روش کاوش داده های بزرگ-2018 This study applies big data mining, machine learning analysis technique and uses the Waikato Environ
ment for Knowledge Analysis (WEKA) as a tool to discuss the convenience stores energy consumption
performance in Taiwan which consists of (a). Influential factors of architectural space environment and
geographical conditions; (b). Influential factors of management type; (c). Influential factors of business
equipment; (d). Influential factors of local climatic conditions; (e). Influential factors of service area so
cioeconomic conditions. The survey data of 1,052 chain convenience stores belong to 7-Eleven, Family
Mart and Hi-Life groups by Taiwan Architecture and Building Center (TABC) in 2014. The implicit knowl
edge will be explored in order to improve the traditional analysis technique which is unlikely to build a
model for complex, inexact and uncertain dynamic energy consumption system for convenience stores.
The analysis process comprises of (a). Problem definition and objective setting; (b). Data source selection;
(c). Data collection; (d). Data preprocessing/preparation; (e). Data attributes selection; (f). Data mining
and model construction; (g). Results analysis and evaluation; (h). Knowledge discovery and dissemination.
The key factors influencing the convenience stores energy consumption and the influence intensity order
can be explored by data attributes selection. The numerical prediction model for energy consumption is
built by applying regression analysis and classification techniques. The optimization thresholds of various
influential factors are obtained. The different cluster data are compared by using clustering analysis to
verify the correlation between the factors influencing the convenience stores energy consumption char
acteristic. The implicit knowledge of energy consumption characteristic obtained by the aforesaid analysis
can be used to (a). Provide the owners with accurate predicted energy consumption performance to opti
mize architectural space, business equipment and operations management mode; (b). The design planners
can obtain the optimum design proposal of Cost Performance Ratio (C/P) by planning the thresholds of
various key factors and the validation of prediction model; (c). Provide decision support for government
energy and environment departments, to make energy saving and carbon emission reduction policies, in
order to estimate and set the energy consumption scenarios of convenience store industry.
Keywords: Convenience store ، Data mining ، Machine learning ، Energy consumption characteristics ، Energy consumption affecting factor |
مقاله انگلیسی |
4 |
Privacy preserving big data mining: association rule hiding using fuzzy logic approach
حفظ حریم خصوصی کاوش داده های بزرگ :پنهان سازی قوانین انجمنی با استفاده از رویکرد منطق فازی-2018 Recently, privacy preserving data mining has been studied widely. Association rule mining can cause potential threat toward privacy of data. So, association rule hiding techniques are employed to avoid the risk of sensitive knowledge leakage. Many researches have been done on association rule hiding, but most of them focus on proposing algorithms with least side effect for static databases (with no new data entrance), while now the authors confront with streaming data which are continuous data. Furthermore, in the age of big data, it is necessary to optimise existing methods to be executable for large volume of data. In this study, data anonymisation is used to fit the proposed model for big data mining. Besides, special features of big data such as velocity make it necessary to consider each rule as a sensitive association rule with an appropriate membership degree. Furthermore, parallelisation techniques which are embedded in the proposed model, can help to speed up data mining process.
Index Terms: authorisation, Big Data, data mining, data protection, fuzzy logic |
مقاله انگلیسی |
5 |
Mining big data using parsimonious factor, machine learning, variable selection and shrinkage methods
کاوش داده های بزرگ با استفاده از عامل پارسیمون، یادگیری ماشین، انتخاب متغیر و روش های انقباض-2018 A number of recent studies in the economics literature have focused on the usefulness
of factor models in the context of prediction using ‘‘big data’’ (see Bai and Ng, 2008;
Dufour and Stevanovic, 2010; Forni, Hallin, Lippi, & Reichlin, 2000; Forni et al., 2005;
Kim and Swanson, 2014a; Stock and Watson, 2002b, 2006, 2012, and the references
cited therein). We add to this literature by analyzing whether ‘‘big data’’ are useful for
modelling low frequency macroeconomic variables, such as unemployment, inflation and
GDP. In particular, we analyze the predictive benefits associated with the use of principal
component analysis (PCA), independent component analysis (ICA), and sparse principal
component analysis (SPCA). We also evaluate machine learning, variable selection and
shrinkage methods, including bagging, boosting, ridge regression, least angle regression,
the elastic net, and the non-negative garotte. Our approach is to carry out a forecasting
‘‘horse-race’’ using prediction models that are constructed based on a variety of model
specification approaches, factor estimation methods, and data windowing methods, in
the context of predicting 11 macroeconomic variables that are relevant to monetary
policy assessment. In many instances, we find that various of our benchmark models,
including autoregressive (AR) models, AR models with exogenous variables, and (Bayesian)
model averaging, do not dominate specifications based on factor-type dimension reduction
combined with various machine learning, variable selection, and shrinkage methods (called
‘‘combination’’ models). We find that forecast combination methods are mean square
forecast error (MSFE) ‘‘best’’ for only three variables out of 11 for a forecast horizon of
h = 1, and for four variables when h = 3 or 12. In addition, non-PCA type factor estimation
methods yield MSFE-best predictions for nine variables out of 11 for h = 1, although
PCA dominates at longer horizons. Interestingly, we also find evidence of the usefulness of
combination models for approximately half of our variables when h > 1. Most importantly,
we present strong new evidence of the usefulness of factor-based dimension reduction
when utilizing ‘‘big data’’ for macroeconometric forecasting.
Keywords: Prediction ، Independent component analysis ، Sparse principal component analysis ، Bagging ، Boosting ، Bayesian model averaging ، Ridge regression ، Least angle regression ، Elastic net and non-negative garotte |
مقاله انگلیسی |
6 |
Big Data Mining of Users Energy Consumption Patterns in the Wireless Smart Grid
کاوش داده های بزرگ الگوهای مصرف انرژی کاربران در شبکه هوشمند بی سیم-2018 A growing number of utility companies are starting to use cellular wireless networks to transmit data in the smart grid. Consequently, millions of users daily energy consumption data are sent by wireless smart meters. However, the broadcast transfer manner of wireless communication makes it naturally vulnerable to cyber attacks. Since smart meter readings can easily be leaked, users energy patterns could be inferred. Hence, users privacy at home is under serious threat. This article begins by introducing the existing work on stealing data from wireless communication networks. Then three types of big data mining schemes for analyzing stolen data are represented. Finally, we discuss several ongoing defense strategies in the era of the wireless smart grid.
Keywords: Big Data, cellular radio, data mining, data privacy, energy consumption, power engineering computing, power system security, security of data,smart meters, smart power grids |
مقاله انگلیسی |
7 |
Analyze the energy consumption characteristics and affecting factors of Taiwan’s convenience stores-using the big data mining approach
تجزیه و تحلیل ویژگی های مصرف انرژی و عوامل موثر در فروشگاه های راحتی تایوان با استفاده از روش کاوش داده های بزرگ-2018 This study applies big data mining, machine learning analysis technique and uses the Waikato Environ
ment for Knowledge Analysis (WEKA) as a tool to discuss the convenience stores energy consumption
performance in Taiwan which consists of (a). Influential factors of architectural space environment and
geographical conditions; (b). Influential factors of management type; (c). Influential factors of business
equipment; (d). Influential factors of local climatic conditions; (e). Influential factors of service area so
cioeconomic conditions. The survey data of 1,052 chain convenience stores belong to 7-Eleven, Family
Mart and Hi-Life groups by Taiwan Architecture and Building Center (TABC) in 2014. The implicit knowl
edge will be explored in order to improve the traditional analysis technique which is unlikely to build a
model for complex, inexact and uncertain dynamic energy consumption system for convenience stores.
The analysis process comprises of (a). Problem definition and objective setting; (b). Data source selection;
(c). Data collection; (d). Data preprocessing/preparation; (e). Data attributes selection; (f). Data mining
and model construction; (g). Results analysis and evaluation; (h). Knowledge discovery and dissemination.
The key factors influencing the convenience stores energy consumption and the influence intensity order
can be explored by data attributes selection. The numerical prediction model for energy consumption is
built by applying regression analysis and classification techniques. The optimization thresholds of various
influential factors are obtained. The different cluster data are compared by using clustering analysis to
verify the correlation between the factors influencing the convenience stores energy consumption char
acteristic. The implicit knowledge of energy consumption characteristic obtained by the aforesaid analysis
can be used to (a). Provide the owners with accurate predicted energy consumption performance to opti
mize architectural space, business equipment and operations management mode; (b). The design planners
can obtain the optimum design proposal of Cost Performance Ratio (C/P) by planning the thresholds of
various key factors and the validation of prediction model; (c). Provide decision support for government
energy and environment departments, to make energy saving and carbon emission reduction policies, in
order to estimate and set the energy consumption scenarios of convenience store industry.
Keywords: Convenience store ، Data mining ، Machine learning ، Energy consumption characteristics ، Energy consumption affecting factor |
مقاله انگلیسی |
8 |
Weather and cycling: Mining big data to have an in-depth understanding of the association of weather variability with cycling on an off-road trail and an on-road bike lane
آب و هوا و دوچرخه سواری: کاوش داده های بزرگ برای درک عمیق از ارتباط تغییرات آب و هوایی با دوچرخه سواری در یک مسیر بدون درز و مسیر دوچرخه در جاده-2018 Although cycling is an easy and popular form of physical activity and urban travel, barriers exist.
In particular, cycling is more likely and more severely to be affected by inclement weather than
the motorized modes as the cyclists are entirely exposed to outdoor environment. Understanding
the weather-cycling relationship is of great importance to academics and practitioners for cycling
activity analysis and promotion. This study contributes to an in-depth understanding of how the
changes in weather conditions affect cycling on an off-road trail and an on-road (bridge) bike
lane at both daily and hourly scales across four seasons. The paper compares the weather-cycling
relationship based on day of week and time of day combinations. The autocorrelation effect of
cycling itself and the lagging effect of weather elements are also examined. The findings indicate
that cycling is significantly self-dependent especially at the finer temporal scales. Weather have a
very different influence on bicycle usage of off-road trails versus on-road bike lanes. When it rains
its negative impact not only continues but also significantly affects the cycling within previous
one hour. At the daily level, weekend cycling on the trail is less likely to be affected by weather as
compared to cycling on the bike lane, whilst inverse is true for weekday cycling. Cycling is most
likely to be affected by weather conditions in spring and least likely to be affected in winter.
Cycling pattern which is more unrelated to weather at the aggregated level tends to be more
flexibly adjusted according to the real-time weather conditions at the disaggregated level.
Cyclists on weekends especially during the weekend peak hours (11 AM–4 PM) tend to have more
flexibility to adjust their cycling schedule before or after the adverse weather conditions than on
weekdays. In addition, cyclists with utilitarian purposes are more likely to shift from cycling to
other modes (e.g., transit) due to real-time bad weather conditions in weekdays than in week
ends, especially during weekday peak hours (7–9 AM and 4–6 PM). The results provide weather
officials, transport agencies and research institutions with valuable information for cycling ac
tivity analysis and promotion by considering the effects of weather events especially rainfall.
Keywords: Weather ، Cycling ، Off-road trail ، On-road (bridge) bike lane ، Lagging effect ، Rainfall |
مقاله انگلیسی |
9 |
ClowdFlows: Online workflows for distributed big data mining
ClowdFlows: گردش کار آنلاین برای کاوش داده های بزرگ توزیع شده-2017 The paper presents a platform for distributed computing, developed using the latest software technologies
and computing paradigms to enable big data mining. The platform, called ClowdFlows, is implemented
as a cloud-based web application with a graphical user interface which supports the construction
and execution of data mining workflows, including web services used as workflow components. As
a web application, the ClowdFlows platform poses no software requirements and can be used from
any modern browser, including mobile devices. The constructed workflows can be declared either as
private or public, which enables sharing the developed solutions, data and results on the web and in
scientific publications. The server-side software of ClowdFlows can be multiplied and distributed to
any number of computing nodes. From a developer’s perspective the platform is easy to extend and
supports distributed development with packages. The paper focuses on big data processing in the batch
and real-time processing mode. Big data analytics is provided through several algorithms, including
novel ensemble techniques, implemented using the map-reduce paradigm and a special stream mining
module for continuous parallel workflow execution. The batch mode and real-time processing mode are
demonstrated with practical use cases. Performance analysis shows the benefit of using all available data
for learning in distributed mode compared to using only subsets of data in non-distributed mode. The
ability of ClowdFlows to handle big data sets and its nearly perfect linear speedup is demonstrated.
Keywords:Data mining platform|Cloud computing|Scientific workflows|Batch processing|Map-reduce|Big data |
مقاله انگلیسی |
10 |
Analysis on spatial-temporal features of taxis emissions from big data informed travel patterns: a case of Shanghai, China
تجزیه و تحلیل در مورد ویژگی های زمانی- مکانی حرکت تاکسی ها از الگوهای سفر آگاه داده های بزرگ : یک مورد از شانگهای، چین-2017 Air pollutions from transportation sector have become a serious urban environmental problem, espe
cially in developing countries with expending urbanization. Cleaner technologies advancement and
optimal regulation on the transporting behaviors and related design in infrastructures is critical to
address above issue. To understand the spatial and temporal emissions pattern within transportation lays
the foundation for design on better infrastructures and guidance on low-carbon transportation behav
iors. The feasibility of Global Positioning System (GPS) and emerging big data analysis technique enable
the in-depth analysis on this topic, while to date, applications had been rather few. With this circum
stance, this paper analyzed the taxis energy consumption and emissions and their spatial-temporal
distribution in Shanghai, one of the most famous mega cities in China, applying big data analysis on
GPS data of taxies. Spatial and temporal features of energy consumptions and pollutants emissions were
further mapped with geographical information system (GIS). Results highlighted that, spatially, the
energy consumption and emission presented a distribution of dual-core cyclic structure, in which, two
hubs were identified. One was the city center, the other was Hongqiao transport hub, the activities and
emission was more concentrated in the west par of Huangpu River. Temporally, the highest activity and
emission moment was 9e10AM, the second peak occurred in 7e8PM, which were both the traffic rush
period. The lowest activity/emission moment was 3e4AM. Causal mechanism for such distribution was
further investigated, so as to improve the driving behaviors. Through the exploration of spatial and
temporal emissions distribution of taxis via big dada technique, this paper provided enlightening in
sights to policy makers for better understanding on the travel patterns and related environmental im
plications in Shanghai metropolis, so as to support better planning on infrastructures system, demand
side management and the promotion on low-carbon life styles.
Keywords:GPS|Big data mining|Spatial-temporal emissions distribution|Taxi travel pattern|Shanghai |
مقاله انگلیسی |