Types Of Data Research Articles

Attention-Dominated Cognitive Dysfunction May Be a Biological Marker for Distinguishing SA from SI in Adolescents: A Network Analysis Study Based on Adolescent Depression.

Suicidal behavior is strongly correlated with depressive symptoms and the degree of suicidal ideation. Cognitive impairment may have varying degrees of influence on suicidal ideation (SI) and suicidal attempts (SA). The aim of this study was to identify the cognitive biomarkers that distinguish suicidal ideation from suicidal attempts in adolescents. The cross-sectional sample comprised 54 adolescents with major depressive disorder (MDD) and 32 healthy controls (HC). The THINC-it was utilized to assess cognitive function of all the samples. Suicidal ideation was examined by the Positive and Negative Suicide Ideation Scale (PANSI). Based on the type of data, one-way ANOVA or Kruskal-Wallis was performed to investigate group differences. Bonferroni post-hoc analysis was employed for regulating type I error for pairwise comparisons. Network analysis was used to compare the networks associated with suicidal ideation, depression symptoms, and cognitive function between SA and SI. The depression symptoms (HAMD-17) (F=72.515, P<0.001) and suicidal ideation (PANSI) (F=267.952, P<0.001) in the SA were higher than those in the SI. Analysis of between-group differences showed SA performed worse in THINC-it, especially in "Spotter (SP)" (P=0.033), "Objective cognition score (OS)" (P=0.027) and "Composite score (CS)" (P=0.017). Compared with SI, network analysis revealed that SA had a unique network of cognitive function, depressive symptoms, and suicidal ideation. Nevertheless, both networks exhibit comparable performance concerning the node strength of cognitive function. Within their separate networks, the aspects of CS, OS, and SP have emerged as the three most crucial elements. Adolescents with SI or SA exhibit a broad spectrum of cognitive impairments. Attention impairment can be beneficial in discerning between SI and SA. Future interventions for adolescent suicide can center on attention and the comprehensive cognitive ability that it represents.

CME Arrival Time Prediction via Fusion of Physical Parameters and Image Features

Coronal mass ejections (CMEs) are among the most intense phenomena in the Sun–Earth system, often resulting in space environment effects and consequential geomagnetic disturbances. Consequently, quickly and accurately predicting CME arrival time is crucial to minimize the harm caused to the near-Earth space environment. To forecast the arrival time of CMEs, researchers have developed diverse methods over the years. While existing approaches have yielded positive results, they do not fully use the available data, as they solely accept either CME physical parameters or CME images as inputs. To solve this issue, we propose a method that extracts features from both CME physical parameters and CME images and uses the attention mechanism to fuse the two types of data. First, we design a parameter feature extraction module that extracts features from CME physical parameters. After that, we adopt an effective convolutional neural network model as our image feature extraction module for extracting features from CME images. Finally, utilizing the attention mechanism, we present a feature fusion module designed to fuse the features extracted from both parameters and images of CMEs. Therefore, our model can fully utilize and combine physical parameters and image features, which allows it to capture significant and comprehensive information about CMEs.

Design and simulation of remote monitoring of the intelligent automatic control system in the production line

&lt;p&gt;&lt;span lang="EN-US"&gt;In this research, we will introduce implementation requirements of a remote wireless control and monitoring unit of industrial production lines automatically controlled using programmable logic controller (PLC). PLC is capable of collecting different types of data and converting them into electrical signals that can be controlled by the industrial network using supervisory control and data acquisition (SCADA) systems. SCADA will be installed in the main server inside the control unit. The PLC will be used as a decision maker of the received signals for the industrial lines that comes from a group of detectors (sensors/transducers). The output of the PLC processor will trigger the engines, according to a specific industrial process management program. The processed data could be transferred through wireless or wired method. The wireless approach will be shown in this study, along with two other ways to implement it.&lt;/span&gt;&lt;/p&gt;

BASECOL2023 scientific content

Context. The global context of making numerous data produced by researchers available requires collecting and organising the data, assigning meaningful metadata, and presenting the data in a meaningful and homogeneous way. The BASECOL database, which collects inelastic rate coefficients for application to the interstellar medium and to circumstellar and cometary atmospheres, meets those requirements. Aims. We aim to present the scientific content of the BASECOL2023 edition. Methods. While the previous versions relied on finding rate coefficients in the literature, the current version is populated with published results sent by the producers of data. The paper presents the database, the type of data that can be found, the type of metadata that are used, and the Virtual Atomic and Molecular Data Centre (VAMDC) standards that are used for the metadata. Finally, we present the different datasets species by species. Results. As the BASECOL database, interconnected with the VAMDC e-infrastructure, uses the VAMDC standards, the collisional data can be extracted with tools using VAMDC standards and can be associated with spectroscopic data extracted from other VAMDC connected databases such as the Cologne database for molecular spectroscopy (CDMS), the jet propulsion laboratory molecular spectroscopy database (JPL), and the high-resolution transmission molecular absorption database (HITRAN).

Mudslide susceptibility assessment based on a two-channel residual network

In response to the challenges posed by rugged terrain in Yunnan, hindering large-scale mudslide screening efforts, this article introduces a dual-channel Convolutional Neural Network (CNN) constructed using elevation data from historical mudslide-prone valleys (Digital Elevation Model, DEM) and remote sensing imagery. The network is designed to facilitate the comprehensive assessment of potential mudslide hazards in gullies, serving as a crucial tool for early mudslide disaster warning. The model initially employs an enhanced residual structure to extract fundamental features from both types of data. Subsequently, it leverages the SE module and deep separable structure to emphasize the importance of relevant features and expedite model convergence. Finally, the model classifies the gullies under evaluation based on their similarity to gullies where mudslides have previously occurred. Experimental results demonstrate the model’s robust performance in assessing mudflow-prone gullies, achieving an impressive precision rate of up to 81.10% and a recall rate of 82.76%. When applied to evaluate the potential hazard of mudslide gullies across the entirety of Nujiang Prefecture, the model predicts that 87.80% of the mudslide locations are at an extremely high risk. These findings underscore the viability of utilizing image-based gully feature analysis for assessing the hazard levels of mudslide-prone gullies.

Combating COVID-19 Crisis using Artificial Intelligence (AI) Based Approach: Systematic Review.

SARS-CoV-2, the unique coronavirus that causes COVID-19, has wreaked damage around the globe, with victims displaying a wide range of difficulties that have encouraged medical professionals to look for innovative technical solutions and therapeutic approaches. Artificial intelligence-based methods have contributed a significant part in tackling complicated issues, and some institutions have been quick to embrace and tailor these solutions in response to the COVID-19 pandemic's obstacles. Here, in this review article, we have covered a few DL techniques for COVID-19 detection and diagnosis, as well as ML techniques for COVID-19 identification, severity classification, vaccine and drug development, mortality rate prediction, contact tracing, risk assessment, and public distancing. This review illustrates the overall impact of AI/ML tools on tackling and managing the outbreak. The focus of this research was to undertake a thorough evaluation of the literature on the part of Artificial Intelligence (AI) as a complete and efficient solution in the battle against the COVID-19 epidemic in the domains of detection and diagnostics of disease, mortality prediction and vaccine as well as drug development. A comprehensive exploration of PubMed, Web of Science, and Science Direct was conducted using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) regulations to find all possibly suitable papers conducted and made publicly available between December 1, 2019, and August 2023. COVID-19, along with AI-specific words, was used to create the query syntax. During the period covered by the search strategy, 961 articles were published and released online. Out of these, a total of 135 papers were chosen for additional investigation. Mortality rate prediction, early detection and diagnosis, vaccine as well as drug development, and lastly, incorporation of AI for supervising and controlling the COVID-19 pandemic were the four main topics focused entirely on AI applications used to tackle the COVID-19 crisis. Out of 135, 60 research papers focused on the detection and diagnosis of the COVID-19 pandemic. Next, 19 of the 135 studies applied a machine-learning approach for mortality rate prediction. Another 22 research publications emphasized the vaccine as well as drug development. Finally, the remaining studies were concentrated on controlling the COVID-19 pandemic by applying AI AI-based approach to it. We compiled papers from the available COVID-19 literature that used AI-based methodologies to impart insights into various COVID-19 topics in this comprehensive study. Our results suggest crucial characteristics, data types, and COVID-19 tools that can aid in medical and translational research facilitation.

Cost-sensitive learning for imbalanced medical data: a review

Integrating Machine Learning (ML) in medicine has unlocked many opportunities to harness complex medical data, enhancing patient outcomes and advancing the field. However, the inherent imbalanced distribution of medical data poses a significant challenge, resulting in biased ML models that perform poorly on minority classes. Mitigating the impact of class imbalance has prompted researchers to explore various strategies, wherein Cost-Sensitive Learning (CSL) arises as a promising approach to improve the accuracy and reliability of ML models. This paper presents the first review of CSL for imbalanced medical data. A comprehensive exploration of the existing literature encompassed papers published from January 2010 to December 2022 and sourced from five major digital libraries. A total of 173 papers were selected, analysed, and classified based on key criteria, including publication years, channels and sources, research types, empirical types, medical sub-fields, medical tasks, CSL approaches, strengths and weaknesses of CSL, frequently used datasets and data types, evaluation metrics, and development tools. The results indicate a noteworthy publication rise, particularly since 2020, and a strong preference for CSL direct approaches. Data type analysis unveiled diverse modalities, with medical images prevailing. The underutilisation of cost-related metrics and the prevalence of Python as the primary programming tool are highlighted. The strengths and weaknesses analysis covered three aspects: CSL strategy, CSL approaches, and relevant works. This study serves as a valuable resource for researchers seeking to explore the current state of research, identify strengths and gaps in the existing literature and advance CSL’s application for imbalanced medical data.

Landslide potential mapping in Pituruh District of Purworejo by using geographic information system

Located in Purworejo Regency, Pituruh is an area that prone to a landslide. Disaster mapping was a strategy that has been done so far by the government institutions and academia. According to its topography, Purworejo has been dominated by slope with more than 30 degree. The creation of landslide vulnerability map was carried out through several stages, namely, calculating rainfall at 6 stations located in Pituruh district, then producing average rainfall data to be used as a reference to create a rainfall map, then overlay process. Referring to the 4 parameters used, slope became an important parameter to determine the level of landslide vulnerability in Pituruh. The development of Geographic Information Systems can provide various types of spatial data related to the earth’s surface, and has the ability to perform accurate spatial analysis. Mitigation aims to reduce risks that have the potential to become disasters or reduce the effects of disasters. This study represents the level of vulnerability in Pituruh district by classifying the existing level. The results of this study have 5 classifications with different areas, namely highly vulnerable that covers 240.27 ha, moderately vulnerable for about 2237.81 ha, moderate with 3322.68 ha, vulnerable by 3040.14 ha, and not vulnerable area of 24.72 ha.

Microtremor Full-Wavefield Modeling of Effective Phase Velocity and Horizontal-to-Vertical Spectral Ratio at Kyoto Reference Borehole Site: Comparison with Surface-Wavefield Modeling Based on a Velocity Structure with a Cap Layer

ABSTRACT Two types of data commonly used for microtremor exploration are phase-velocity dispersion curves obtained through an array measurement and horizontal-to-vertical spectral ratios (HVSRs) obtainable by a single-station measurement. Phase-velocity dispersion curves obtained by applying the spatial autocorrelation method to the array waveforms have a characteristic peaked shape in some cases. This dispersion curve shape has traditionally been explained as a consequence of the predominance of higher modes over fundamental mode in the Rayleigh waves. In this study, the effects of body waves on phase velocities and HVSRs were investigated based on both field measurements and theoretical calculations of microtremors. We used vertical-component array waveforms and single-station three-component waveforms of microtremors, obtained at and around a site where combined P-wave–S-wave (PS) and density loggings were conducted in the Kyoto basin, Japan (site KD-1), to identify phase velocities and HVSRs at frequencies in the range 0.2–2 Hz. The corresponding theoretical phase velocities and HVSRs were identified using full-wavefield synthetic data, which were generated assuming excitation points randomly distributed over the surface of a horizontally stratified velocity structure model created based on the logging data. The following key results were obtained. The measured phase-velocity dispersion curve exhibits a peaked shape with the value exceeding the S-wave velocity of the Tamba Group (Tb-Group), which is the bedrock (half-space) of the velocity structure model. Theoretical calculations based on the surface-wavefield theory were unable to reproduce this peaked shape; however, theoretical calculations based on the full-wavefield theory reproduced it with extraordinary accuracy. To reproduce the peaked shape based on the surface-wavefield theory, it was necessary to construct a model containing a cap (i.e., high-velocity layer) connected under the Tb-Group. The theoretical calculation based on the full wavefield also accurately reproduced the peak value and peak frequency of the measured HVSRs.

Measuring data drift with the unstable population indicator1

Measuring data drift is essential in machine learning applications where model scoring (evaluation) is done on data samples that differ from those used in training. The Kullback-Leibler divergence is a common measure of shifted probability distributions, for which discretized versions are invented to deal with binned or categorical data. We present the Unstable Population Indicator, a robust, flexible and numerically stable, discretized implementation of Jeffrey’s divergence, along with an implementation in a Python package that can deal with continuous, discrete, ordinal and nominal data in a variety of popular data types. We show the numerical and statistical properties in controlled experiments. It is not advised to employ a common cut-off to distinguish stable from unstable populations, but rather to let that cut-off depend on the use case.

Investigating the overlap of machine learning algorithms in the final results of RNA-seq analysis on gene expression estimation

Advances in computer science in combination with the next-generation sequencing have introduced a new era in biology, enabling advanced state-of-the-art analysis of complex biological data. Bioinformatics is evolving as a union field between computer Science and biology, enabling the representation, storage, management, analysis and exploration of many types of data with a plethora of machine learning algorithms and computing tools. In this study, we used machine learning algorithms to detect differentially expressed genes between different types of cancer and showing the existence overlap to final results from RNA-sequencing analysis. The datasets were obtained from the National Center for Biotechnology Information resource. Specifically, dataset GSE68086 which corresponds to PMID:200,068,086. This dataset consists of 171 blood platelet samples collected from patients with six different tumors and healthy individuals. All steps for RNA-sequencing analysis (preprocessing, read alignment, transcriptome reconstruction, expression quantification and differential expression analysis) were followed. Machine Learning- based Random Forest and Gradient Boosting algorithms were applied to predict significant genes. The Rstudio statistical tool was used for the analysis.

Deep Learning based Smart Image Search Engine

Abstract: This paper introduces a new reverse search engine integration into content-based image retrieval (CBIR) systems that employs convolutional neural networks (CNNs) for feature extraction. It generates global descriptors using pre-trained CNN architectures such as ResNet50, InceptionV3, and InceptionResNetV2. It retrieves visually similar images without depending on linguistic annotations. Comparative analysis against existing methods, such as Gabor Wavelet, CNN-SVM, Metaheuristic Algorithm, etc., has been tested, and it proves the superiority of the proposed algorithm, the Cartoon Texture Algorithm, in CBIR. As the Internet sees an exponential growth of different data types, the importance of CBIR continues to grow. In order to efficiently retrieve images, solely relying on image features while ignoring metadata is exactly what we need. As such, this paper is a reminder of the need for CBIR in this changing world. They showed that CBIR continues to be quite effective in the age of the Internet. Their proposed model for CBIR, which integrates ResNet-50-based feature extraction, a neural network model trained on different image datasets, and clustering techniques to make retrieval fast, provides a significant improvement in accuracy and efficiency for content-dependent image retrieval. This methodology is likely to be very useful as we work with the increasingly huge data of vision and beyond on the Internet. It provides a good basis for an effective image search and retrieval system

Analysis of the Influence of the Tourism Sector on Original Regional Income (PAD) in Minahasa Regency for the 2008-2022 Period

Objective: To find out how many hotels, restaurants and returning visitors contribute to the regional income of Minahasa Regency in 2008–2022 is the aim of this research. The Minahasa Regency Tourism and Culture Office and the Central Statistics Agency provided time series data which is a type of secondary data used in this research. One type of analysis method is multiple linear regression analysis. Research findings show that the initial regional income of Minahasa Regency is greatly influenced and profitable by the number of visitors. In Minahasa Regency, the number of hotels has a negative and very large impact compared to local revenue. In Minahasa Regency, the amount of income in the area is influenced significantly and profitably by restaurants. During 2008–2022, the large number of hotels, restaurants and tourists had a major impact on the local economy in Minahasa Regency.

스포츠 전공 학생-교수 간의 교환관계(LMX)가 학습성과에 미치는 영향

This study examined the exchange relationship between professors who influence sports major students and students who are influenced by both theory and practice, and verified the impact of this relationship on learning outcomes. Accordingly, a total of 72 rows (144 people: students) were collected in the form of couple data (type A, B) according to the research plan from May 11 to October 6, 2023, using APIM (actor and partner interdependent model), which can interpret data. A convenience sample (72 people, 72 professors) was extracted. In addition, keywords such as 'student', 'professor', and 'sports major' were set, and the study was conducted after conducting preliminary interviews with the type A group (10 students) and the type B group (10 major professors). Factors consistent with the purpose were set. Research results: First, the self-effect of student exchange relationships on student learning outcomes was significant. And the counterpart effect was significant, showing that the higher the professor exchange relationship, the higher the student learning performance. Second, the self-effect of the professor exchange relationship had a significant effect on professor and learning outcomes. In addition, it can be seen that the higher the counterpart effect, the higher the teaching and learning performance. And it was found that learning outcomes are not determined by individuals, but that individual interaction methods can be found through exchange relationships (LMX) between members.

GD3N: Adaptive clustering-based detection of selective forwarding attacks in WSNs under variable harsh environments

Wireless sensor networks (WSNs) are susceptible to numerous security threats due to their reliance on open environments and broadcast communication methods. Among these, the selective forwarding attack is notably challenging to detect. This difficulty arises from the ability of malicious nodes to imitate the behavior of normal nodes, and selectively drop data packets, which makes them virtually indistinguishable from normal ones, particularly under conditions of poor channel quality. To address this challenge with harsh environments, we introduce a novel methodology termed GD3N. This approach is underpinned by the design of a unique type of data point that encapsulates both short-term and long-term forwarding behaviors of nodes. It combines a refined version of the Gradient Diffusion Density-based Spatial Clustering of Applications with Noise (GD-DBSCAN) algorithm, with a novel Double-Parameter Neighbor Voting (DP-NV) method based on the data set. These innovations contribute to a significant enhancement in detection accuracy and a reduction in computational complexity when compared to traditional DBSCAN and NV methods. Simulation results show that our GD3N achieves a false detection rate (FDR) of less than 2%, a missed detection rate (MDR) of below 10%, and an overall detection accuracy rate (DAR) of over 95% across various testing scenarios.

Aboveground Biomass Estimation and Time Series Analyses in Mongolian Grasslands Utilizing PlanetScope Imagery

Mongolia, situated in central Asia and bordered by Russia to the north and China to the south, experiences a semi-arid climate across most of its territory. Grasslands are pivotal in Mongolia’s agricultural sustainability and food security, facing rapid changes in the last two decades that underscore the ongoing need for innovative approaches to assess vegetation conditions. This study aims to evaluate grassland biomass measurement and prediction through the analysis of high-resolution satellite data. By conducting a time series assessment of grazing-induced changes in vegetation dynamics at the long-term monitoring sites of the Botanic Garden and Research Institute, Mongolian Academy of Sciences, we seek to refine our understanding. The investigation covers biomass estimation across various Mongolian grassland landscapes, encompassing desert, steppe, and mountain regions. Spanning the grassland growing season from May 2020 to October 2023, the research leveraged diverse ground data types, including surface reflectance measurements, geographic coordinates for satellite data correction, and aboveground dry biomass. These components were instrumental in developing a biomass estimation model reliant on establishing correlations between the satellite-derived Normalized Difference Vegetation Index and biomass. The predicted biomass facilitated the time series map analysis and dynamic analysis. The PlanetScope surface reflectance correlates strongly at 0.97 with field measurements, indicating robust relations. Biomass and the Normalized Difference Vegetation Index show correlations of 0.82 for dry grassland, 0.80 for mountain grassland, and 0.65 for desert grassland, with a combined correlation coefficient of 0.62, revealing distinct characteristics across these grasslands. Time series dynamic analysis reveals rising biomass differences between grazed and ungrazed areas, suggesting potential grassland degradation. Variations in the slope coefficient of biomass differences among grassland types indicate differing degradation patterns, emphasizing the need for effective grazing management practices to sustain and conserve Mongolian grasslands. This highlights the potential of remote sensing in monitoring and managing grassland ecosystems.

Analysis of the Effectiveness and Contribution of Land and Building Taxes to the Original Regional Income of Bekasi Regency

Land and Building Tax (PBB) is one of the regional taxes that has a major contribution to the Regional Original Income (PAD) of Bekasi Regency. This study aims to analyze the level of effectiveness and contribution of land and building tax (PBB) to the regional original income (PAD) of Bekasi Regency. This study used a quantitative descriptive method. The data source used in this study was secondary data with time series data types. Secondary data used are the financial statements of Regional Original Revenue (PAD) revenue and the Bekasi Regency Land and Building Tax (PBB) revenue report for 2017–2019. The results of this study indicate that the effectiveness level of land and building tax (PBB) revenue in Bekasi Regency for three years, namely from 2017 to 2019, shows that the criteria are very effective, with an average achievement of 113%. The contribution rate of land and building tax (PBB) to the regional original income (PAD) of Bekasi Regency for three years, namely from 2017 to 2019, shows the criteria of being deficient with an average of 18%. For the simple regression analysis, the results show that land and building taxes have a positive effect on local revenue

Advanced analytics for predicting traffic collision severity assessment

Accurate prediction of accident risks plays a crucial role in proactively implementing safety measures and allocating resources effectively. This paper introduces an innovative approach aimed at improving accident risk prediction by harnessing unique data sources and extracting insights from diverse yet sparse datasets. Traditional models often face limitations due to a lack of diversity and scope in the available data, which hinders their predictive capabilities. In response to this challenge, our study integrates a broad spectrum of heterogeneous data, encompassing traffic flow, weather conditions, road infrastructure details, and historical accident records. To overcome the difficulties associated with sparse data, we employ advanced data science techniques such as feature engineering, imputation, and machine learning. The paper introduces a novel dataset that amalgamates various data types, establishing a robust foundation for our predictive model. Through meticulous analysis, we derive valuable insights from these diverse sources, significantly enhancing our ability to assess accident risks. The proposed approach offers numerous advantages, including the capacity to predict accidents in areas that were previously underrepresented and under varying conditions. We rigorously evaluate the model's performance through extensive experimentation and validate its accuracy using real-world accident data. Our results indicate substantial improvements in prediction accuracy compared to conventional models. This research contributes significantly to the field of accident risk prediction by highlighting the potential benefits of integrating heterogeneous sparse data and leveraging advanced data science techniques. The study underscores the importance of tapping into novel data sources and extracting concealed patterns and insights to promote safety and optimize resource allocation in accident-prone regions, fostering more secure environments.

Magnetic Field Enhancements in the Solar Wind: Diverse Processes Manifesting a Uniform Observation Type?

AbstractWithin the solar wind throughout the inner heliosphere, observations reveal the presence of magnetic field enhancements accompanied by thin current sheets at varying distances from the sun and across different longitudes and latitudes. Two primary explanations have been proposed to elucidate these phenomena: Solar wind‐dust interaction and interlacing flux ropes. In this study, we employ multi‐fluid Magnetohydrodynamics (MHD) and Hall MHD models to simulate these hypotheses, respectively. Our findings indicate a concurrence between both models and the observed phenomena, suggesting that both processes may result in the same kind of enhancement. Furthermore, both models make predictions pointing to additional types of observational data, occurring at distinct spatial or temporal stages of the interaction. This convergence of model predictions with empirical data underscores the need for further observational and modeling studies to comprehensively test these models. This research enhances our knowledge of the inner heliosphere's dynamics and the influence of the solar wind on the Earth's magnetosphere, thereby shedding light on critical aspects of space weather and its potential impact on our planet.

MOGAT: A Multi-Omics Integration Framework Using Graph Attention Networks for Cancer Subtype Prediction.

Accurate cancer subtype prediction is crucial for personalized medicine. Integrating multi-omics data represents a viable approach to comprehending the intricate pathophysiology of complex diseases like cancer. Conventional machine learning techniques are not ideal for analyzing the complex interrelationships among different categories of omics data. Numerous models have been suggested using graph-based learning to uncover veiled representations and network formations unique to distinct types of omics data to heighten predictions regarding cancers and characterize patients' profiles, amongst other applications aimed at improving disease management in medical research. The existing graph-based state-of-the-art multi-omics integration approaches for cancer subtype prediction, MOGONET, and SUPREME, use a graph convolutional network (GCN), which fails to consider the level of importance of neighboring nodes on a particular node. To address this gap, we hypothesize that paying attention to each neighbor or providing appropriate weights to neighbors based on their importance might improve the cancer subtype prediction. The natural choice to determine the importance of each neighbor of a node in a graph is to explore the graph attention network (GAT). Here, we propose MOGAT, a novel multi-omics integration approach, leveraging GAT models that incorporate graph-based learning with an attention mechanism. MOGAT utilizes a multi-head attention mechanism to extract appropriate information for a specific sample by assigning unique attention coefficients to neighboring samples. Based on our knowledge, our group is the first to explore GAT in multi-omics integration for cancer subtype prediction. To evaluate the performance of MOGAT in predicting cancer subtypes, we explored two sets of breast cancer data from TCGA and METABRIC. Our proposed approach, MOGAT, outperforms MOGONET by 32% to 46% and SUPREME by 2% to 16% in cancer subtype prediction in different scenarios, supporting our hypothesis. Our results also showed that GAT embeddings provide a better prognosis in differentiating the high-risk group from the low-risk group than raw features.