Integration Of Multiple Data Sources Research Articles

Two-stage multi-datasource machine learning for attack technique and lifecycle detection

Intrusion detection systems (IDS) have increasingly adopted machine learning (ML) techniques to enhance their ability to detect a wide range of attack variants. However, the traditional focus in current research primarily revolves around identifying specific attack types or techniques using a single data source. However, this approach lacks a holistic perspective on attacks, which can result in missed detections. To improve the effectiveness of responding to detected attacks, it is essential to identify them based on their lifecycles and incorporate information from multiple data sources. In this study, we present three distinct approaches for detecting attack lifecycles, each leveraging different ML methodologies: a single-stage ML model, a two-stage ML+ML approach, and ML with sequence matching (ML+SM). Simultaneously, we explore the benefits of utilizing multiple data sources, including network traffic, system logs, and host statistics, to enhance technique detection capabilities. Our evaluation of these methods reveals that on lifecycle detection, the two-stage ML+ML approach outperforms the others, achieving an impressive F1 score of 0.994. In contrast, the single-stage and ML+SM methods yield F1 scores of 0.887 and 0.189, respectively. Furthermore, the integration of multiple data sources proves highly advantageous, with the combination of all three sources yielding the highest F1 score of 0.922 on technique detection.

Integrated machine learning and multimodal data fusion for patho-phenotypic feature recognition in iPSC models of dilated cardiomyopathy.

Integration of multiple data sources presents a challenge for accurate prediction of molecular patho-phenotypic features in automated analysis of data from human model systems. Here, we applied a machine learning-based data integration to distinguish patho-phenotypic features at the subcellular level for dilated cardiomyopathy (DCM). We employed a human induced pluripotent stem cell-derived cardiomyocyte (iPSC-CM) model of a DCM mutation in the sarcomere protein troponin T (TnT), TnT-R141W, compared to isogenic healthy (WT) control iPSC-CMs. We established a multimodal data fusion (MDF)-based analysis to integrate source datasets for Ca2+ transients, force measurements, and contractility recordings. Data were acquired for three additional layer types, single cells, cell monolayers, and 3D spheroid iPSC-CM models. For data analysis, numerical conversion as well as fusion of data from Ca2+ transients, force measurements, and contractility recordings, a non-negative blind deconvolution (NNBD)-based method was applied. Using an XGBoost algorithm, we found a high prediction accuracy for fused single cell, monolayer, and 3D spheroid iPSC-CM models (≥92±0.08 %), as well as for fused Ca2+ transient, beating force, and contractility models (>96±0.04 %). Integrating MDF and XGBoost provides a highly effective analysis tool for prediction of patho-phenotypic features in complex human disease models such as DCM iPSC-CMs.

Enhanced detection of freeze‒thaw induced landslides in Zhidoi county (Tibetan Plateau, China) with Google Earth Engine and image fusion

Freeze‒thaw induced landslides (FTILs) in grasslands on the Tibetan Plateau are a geological disaster leading to soil erosion. These landslides reduce biodiversity and intensify landscape fragmentation, which in turn are strengthen by the persistent climate change and increased anthropogenic activities. However, conventional techniques for mapping FTILs on a regional scale are impractical due to their labor-intensive, costly, and time-consuming nature. This study focuses on improving FTILs detection by implementing image fusion-based Google Earth Engine (GEE) and a random forest algorithm. Integration of multiple data sources, including texture features, index features, spectral features, slope, and vertical‒vertical polarization data, allow automatic detection of the spatial distribution characteristics of FTILs in Zhidoi county, which is located within the Qinghai‒Tibet Engineering Corridor (QTEC). We employed statistical techniques to elucidate the mechanisms influencing FTILs occurrence. The enhanced method identifies two schemes that achieve high accuracy using a smaller training sample (scheme A: 94.1%; scheme D: 94.5%) compared to other methods (scheme B: 50.0%; scheme C: 95.8%). This methodology is effective in generating accurate results using only ∼10% of the training sample size necessitated by other methods. The spatial distribution patterns of FTILs generated for 2021 are similar to those obtained using various other training sample sources, with a primary concentration observed along the central region traversed by the QTEC. The results highlight the slope as the most crucial feature in the fusion images, accounting for 93% of FTILs occurring on gentle slopes ranging from 0° to 14°. This study provides a theoretical framework and technological reference for the identification, monitoring, prevention and control of FTILs in grasslands. Such developments hold the potential to benefit the management of grassland ecosystem, reduce economic losses, and promote grassland sustainability.

MARITIME DOMAIN AWARENESS IN SOUTH AFRICA: A MULTISOURCE APPROACH USING REMOTE SENSING AND AIS DATA

Abstract. Maritime Domain Awareness (MDA) is a key factor in ensuring the security, safety, economy, and environment of the maritime domain. Due to the large size of the EEZ, vessel tracking is essential for security, economy, tourism, and safety. However, the failure of one of the two Copernicus Sentinel-1 satellites has posed a significant challenge, hindering the effectiveness of monitoring dark targets. To address this limitation, this study presents the integration of multiple data sources, allowing the mapping of both vessels and dark targets while providing comprehensive analytics to inform decision makers and improve South Africa’s maritime domain awareness efforts. The results demonstrated the potential of combining SAR, optical and AIS data to effectively monitor and manage vessel activities in the South African EEZ. Using these diverse data sources, decision makers can gain real-time insights into maritime traffic, identify potential risks or illicit activities, and make informed decisions to protect national interests and promote sustainable practises within the maritime domain.

Enterprise data and semantic modelling: conceptual model of information technology incident management

Knowledge management methods and their efficient implementation across the organization determine sound and resilient management of processes. This paper studies the semantic integration of enterprise data sources essential to service management processes. Implementing a semantic layer within the enterprise architecture uses various tools, methods, and techniques. The semantic conceptual model unifies and implements intelligent integration of multiple data sources across the enterprise, achieving consistency and more accessible interpretation. Specifically, we draw our attention to incident and problem management within enterprises. We propose an ontology - a conceptual model for the incident management process. The incident ontology presented as an intelligent data integration layer component aims to achieve operational excellence. Besides, this ontology is a fundamental part of the proactive process in problem management. An ontology as a logic-based system supports integrity validation. It infers new, no explicitly modeled facts in the problem domain, thus helping experts better analyze and understand the problem. We discuss the conducted experiment results with the proposed in this article conceptual model using the enterprise knowledge graph platform. It can be perceived as a framework for a query-answering system with components, including ontology schema, data mapping, and classification methods for data graph enrichment.

Prediction model for multiblock data with or without intermediate blocks. Estimation by PLS Path Modeling

An important issue in modern science remains the integration of multiple data sources, considered as datasets divided into blocks of variables measured on the same set of observations. When analyzing these blocks, the path modeling approach aims to take into account a specific pattern of directed relations between them. This structure, usually set up from prior knowledge, leads to a path diagram in which the block’s components are linked to one another. Until path modeling has gained popularity in recent decades, the issue of prediction has been surprisingly neglected in the literature. To fill this gap, we propose a prediction framework dedicated to the path modeling approach. Indeed, prediction for path modeling methods is of paramount interest for: (i) selecting the optimal model with cross-validation (e.g., optimal model specification and dimension), (ii) comparing the model with other relevant ones, (iii) or predicting new observations. Here, we propose an explicit formulation of a prediction model in the context of a path modeling structure. This generic prediction model is estimated using the PLS Path Modeling algorithm, denoted PMM-PLSPM (=Prediction Model for Multiblock data estimated by PLS Path Modeling). Nevertheless, its versatility makes it possible to adapt the approach to any other composite-based methods. Two estimation strategies (PLSPM-like and PLSR-like) are proposed for prediction, both being viewed as the structural model at the variable level. At the same time, the components of multiple blocks – obtained by a relevant deflation strategy – are handled to take advantage of the full multidimensional potential of blocks. The proposed prediction strategies are applied and evaluated on real data sets.

Integration of Sentinel–1 and Landsat–8 data for identifying deformation risk of hydraulic engineering in seasonally frozen salinization regions

Hydraulic engineering has positive effects on agricultural production and soil desalination. However, freeze–thaw cycles can cause instability in canal slopes, emphasizing the need for effective deformation monitoring. Using Sentinel-1B data acquired from November 2017 to August 2019 and applying the small baseline subset (SBAS) InSAR method, two unstable canal segments with vertical deformation rates exceeding –40 mm/year were identified in Songyuan, Northeast China. The canal slopes uplifted in winter and experienced accelerated subsidence in spring, with the shady slope experiencing a deformation rate approximately 10 mm/year faster than the sunny slope. Further investigation, integrating Landsat–8 data collected from November 2017 to August 2019, revealed that the shady slope received less thermal radiation than the sunny slope, with an average annual temperature difference on the land surface of approximately 1 ℃. In winter, the saline–alkali soil particles formed a thick unfrozen water film, which, driven by temperature and soil–water gradients, migrated towards the freezing front, froze, and caused soil frost heave. The shady slope experienced longer freezing periods and deeper frozen depths, up to 240 cm, causing more unfrozen water migration and greater uplift deformation than the sunny slope. In spring, as temperature rose, the soil thawed from the surface downward. Notably, the shady slope thawed at a slower rate than the sunny slope, forming a frozen lens near a depth of 100 cm. Frozen lens acted as a barrier for meltwater infiltration, impeding the dissipation of pore water pressure, making the shady slope prone to surface landslides. The integration of multiple data sources is beneficial for identifying deformation risk in hydraulic engineering.

Quantitative Characterization of Coastal Cliff Retreat and Landslide Processes at Portonovo–Trave Cliffs (Conero, Ancona, Italy) Using Multi-Source Remote Sensing Data

The integration of multiple data sources, including satellite imagery, aerial photography, and ground-based measurements, represents an important development in the study of landslide processes. The combination of different data sources can be very important in improving our understanding of geological phenomena, especially in cases of inaccessible areas. In this context, the study of coastal areas represents a real challenge for the research community, both for the inaccessibility of coastal slopes and for the numerous drivers that can control coastal processes (subaerial, marine, or endogenic). In this work, we present a case study of the Conero Regional Park (Northern Adriatic Sea, Ancona, Italy) cliff-top retreat, characterized by Neogenic soft rocks (flysch, molasse). In particular, the study is focused in the area between the beach of Portonovo and Trave (south of Ancona), which has been studied using aerial orthophoto acquired between 1978 and 2021, Unmanned Aerial Vehicle (UAV) photographs (and extracted photogrammetric model) surveyed in September 2021 and 2012 LiDAR data. Aerial orthophotos were analyzed through the United States Geological Survey’s (USGS) tool Digital Shoreline Analysis System (DSAS) to identify and estimate the top-cliff erosion. The results were supported by the analysis of wave data and rainfall from the correspondent period. It has been found that for the northernmost sector (Trave), in the examined period of 40 years, an erosion up to 40 m occurred. Furthermore, a Digital Elevation Model (DEM) of Difference (DoD) between a 2012 Digital Terrain Model (DTM) and a UAV Digital Surface Model (DSM) was implemented to corroborate the DSAS results, revealing a good agreement between the retreat areas, identified by DSAS, and the section of coast characterized by a high value of DoD.

Predicting the disease severity in male individuals with ornithine transcarbamylase deficiency.

Ornithine transcarbamylase deficiency (OTC-D) is an X-linked metabolic disease and the most common urea cycle disorder. Due to high phenotypic heterogeneity, ranging from lethal neonatal hyperammonemic events to moderate symptoms and even asymptomatic individuals, the prediction of the disease course at an early disease stage is very important to individually adjust therapies such as medical treatment or liver transplantation. In this translational study, we developed a severity-adjusted classification system based on invitro residual enzymatic OTC activity. Applying a cell-based expression system, residual enzymatic OTC activities of 71 pathogenic OTC variants were spectrophotometrically determined and subsequently correlated with clinical and biochemical outcome parameters of 119 male individuals with OTC-D (mOTC-D) as reported in the UCDC and E-IMD registries. Integration of multiple data sources enabled the establishment of a robust disease prediction model for mOTC-D. Residual enzymatic OTC activity not only correlates with age at first symptoms, initial peak plasma ammonium concentration and frequency of metabolic decompensations but also predicts mortality. The critical threshold of 4.3% residual enzymatic activity distinguishes a severe from an attenuated phenotype. Residual enzymatic OTC activity reliably predicts the disease severity in mOTC-D and could thus serve as a tool for severity-adjusted evaluation of therapeutic strategies and counselling patients and parents.

Linking animal migration and ecosystem processes: Data-driven simulation of propagule dispersal by migratory herbivores.

Animal migration is a key process underlying active subsidies and species dispersal over long distances, which affects the connectivity and functioning of ecosystems. Despite much research describing patterns of where animals migrate, we still lack a framework for quantifying and predicting how animal migration affects ecosystem processes. In this study, we aim to integrate animal movement behavior and ecosystem functioning by developing a predictive modeling framework that can inform ecosystem management and conservation.We propose a framework to model individual‐level migration trajectories between populations' seasonal ranges as well as the resulting dispersal and fate of propagules carried by the migratory animals, which can be calibrated using empirical data at every step of the modeling process. As a case study, we applied our framework to model the spread of guava seeds, Psidium guajava, by a population of migratory Galapagos tortoises, Chelonoidis porteri, across Santa Cruz Island. Galapagos tortoises are large herbivores that transport seeds and nutrients across the island, while Guava is one of the most problematic invasive species in the Galapagos archipelago.Our model can predict the pattern of spread of guava seeds alongside tortoises' downslope migration range, and it identified areas most likely to see establishment success. Our results show that Galapagos tortoises' seed dispersal may particularly contribute to guava range expansion on Santa Cruz Island, due to both long gut retention time and tortoise's long‐distance migration across vegetation zones. In particular, we predict that tortoises are dispersing a significant amount of guava seeds into the Galapagos National Park, which has important consequences for the native flora.The flexibility and modularity of our framework allow for the integration of multiple data sources. It also allows for a wide range of applications to investigate how migratory animals affect ecosystem processes, including propagule dispersal but also other processes such as nutrient transport across ecosystems. Our framework is also a valuable tool for predicting how animal‐mediated propagule dispersal can be affected by environmental change. These different applications can have important conservation implications for the management of ecosystems that include migratory animals.

Geospatial Artificial Intelligence (GeoAI) in the Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends

This paper reviews the current GeoAI and machine learning applications in hydrological and hydraulic modeling, hydrological optimization problems, water quality modeling, and fluvial geomorphic and morphodynamic mapping. GeoAI effectively harnesses the vast amount of spatial and non-spatial data collected with the new automatic technologies. The fast development of GeoAI provides multiple methods and techniques, although it also makes comparisons between different methods challenging. Overall, selecting a particular GeoAI method depends on the application’s objective, data availability, and user expertise. GeoAI has shown advantages in non-linear modeling, computational efficiency, integration of multiple data sources, high accurate prediction capability, and the unraveling of new hydrological patterns and processes. A major drawback in most GeoAI models is the adequate model setting and low physical interpretability, explainability, and model generalization. The most recent research on hydrological GeoAI has focused on integrating the physical-based models’ principles with the GeoAI methods and on the progress towards autonomous prediction and forecasting systems.

Reliably quantifying the evolving worldwide dynamic state of the COVID-19 outbreak from death records, clinical parametrization, and demographic data

The dynamic characterization of the COVID-19 outbreak is critical to implement effective actions for its control and eradication but the information available at a global scale is not sufficiently reliable to be used directly. Here, we develop a quantitative approach to reliably quantify its temporal evolution and controllability through the integration of multiple data sources, including death records, clinical parametrization of the disease, and demographic data, and we explicitly apply it to countries worldwide, covering 97.4% of the human population, and to states within the United States (US). The validation of the approach shows that it can accurately reproduce the available prevalence data and that it can precisely infer the timing of nonpharmaceutical interventions. The results of the analysis identified general patterns of recession, stabilization, and resurgence. The diversity of dynamic behaviors of the outbreak across countries is paralleled by those of states and territories in the US, converging to remarkably similar global states in both cases. Our results offer precise insights into the dynamics of the outbreak and an efficient avenue for the estimation of the prevalence rates over time.

Particular matter prediction using synergy of multiple source urban big data in smart cities

At present, the issue of air quality in populated urban areas is recognized as an environmental crisis. Air pollution affects the sustainability of the city. In controlling air pollution and protecting its hazards from humans, air quality data are very important. However, the costs of constructing and maintaining air quality registration infrastructure are very expensive and high, and air quality data recording at one point will not be generalizable to even a few kilometers. Some of the gains come from the integration of multiple data sources, which can never be achieved through independent single-source processing. Urban organizations in each city independently produce and record data relevant to the organization’s goals and objectives. These issues create separate data silos associated with an urban system. These data are varied in model and structure, and the integration of such data provides an appropriate opportunity to discover knowledge that can be useful in urban planning and decision making. This paper aims to show the generality of our previous research, which proposed a novel model to predict Particulate Matter (PM) as the main factor of air quality in the regions of the cities where air quality sensors are not available through urban big data resources integration, by extending the model and experiments with various configuration for different settings in smart cities. This work extends the evaluation scenarios of the model with the extended dataset of city of Aarhus, in Denmark, and compare the model performance against various specified baselines. Details of removing the heterogeneity of multiple data sources in the Multiple Data Set Aggregator & Heterogeneity Remover (MDA&HR) and improving the operation of Train Data Splitter (TDS) part of the model by focusing on the finding more similar pattern of air quality also are presented in this paper. The acceptable accuracy of the results shows the generality of the model.

Abstract 165: Enhanced processing of genomic sequencing data for pediatric cancers: GPUs and machine learning techniques for variant detection

Abstract Introduction: The evolution of precision oncology will demand the integration of multi-omics, clinical data, demographics and outcomes, which is a complex and computationally intensive endeavor. Accurate and robust molecular characterization of pediatric leukemias require a comprehensive approach involving longitudinal sample collection across a large cohort. Genomic alterations, both germline and somatic, need to be efficiently captured and annotated to identify clinically meaningful trends. Algorithms developed for conducting genomic analysis typically leverage a central processing unit (CPU) environment for data processing and traditional statistical approach, such as Bayes' theorem, for variant detection. More recently, algorithms have been developed that can leverage a graphics processing unit (GPU) architecture and machine learning (ML) technology. In this study we benchmarked these techniques and applied them sequencing data generated from pediatric AML subjects enrolled on the Children's Oncology Group AAML1031 trial (951 samples). Methods: Whole genome sequencing was conducted using the Illumina platform (2X150) and sequenced between 30x-60x coverage. Genome alignment files were generated and subjected to germline and somatic variant calling (NVIDIA's Selene DGXA100 cluster; Parabricks Pipelines). Germline variants were called using Haplotype caller and DeepVariant (Parabricks v3.0). HG002 genome in a bottle sample was used as a positive control to determine precision, recall and F1 score. Somatic variant calling was completed using somatic sniper, VarScan2, Muse, MuTect2, and Strelka (Parabricks v3.5) and SEQCII data was used to assess somatic variant calling performance. Results: Control sample HG002 was used for benchmarking the algorithms tested. At all depths analyzed, 50X, 30X, 15X, and 5X, GoogleDeep Variant had the highest F1 score, precision, and recall compared to Haplotyper for SNPs and InDels. For somatic SNV detection using SEQCII dataset, Strelka2 had the highest F1 score, precision, and recall (0.9435, 0.9445, 0.9425), followed by MuTect2, MUSE, VarScan2, and SomaticSniper. For somatic InDel detection, MuTect2 and Strelka2 performed similar with MuTect2 higher precision, but Strelka2 had higher recall. These pipelines were utilized to analyze 951 pediatric AML samples, and demonstrated superior processing time and identification of variants, both germline and somatic. Discussion: ML algorithms integrated into a GPU computing environment demonstrated greater precision and accuracy for germline variant detection compared to traditional statistical approaches for variant detection. Ongoing efforts are focused at integration of multiple data sources correlated with outcomes and analyzed via ML tools in order to improve future risk stratification and therapeutic selection for superior outcomes. Citation Format: Erin L. Crowgey, Pankaj Vats, Karl Franke, Gary Burnett, Ankit Sethia, Timothy Harkins, Todd E. Druley. Enhanced processing of genomic sequencing data for pediatric cancers: GPUs and machine learning techniques for variant detection [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 165.

The Road to Operationalization of Effective Tropical Forest Monitoring Systems

The urgency to preserve tropical forest remnants has encouraged the development of remote sensing tools and techniques to monitor diverse forest attributes for management and conservation. State-of-the-art methodologies for mapping and tracking these attributes usually achieve accuracies greater than 0.8 for forest cover monitoring; r-square values of ~0.5–0.7 for plant diversity, vegetation structure, and plant functional trait mapping, and overall accuracies of ~0.8 for categorical maps of forest attributes. Nonetheless, existing operational tropical forest monitoring systems only track single attributes at national to global scales. For the design and implementation of effective and integrated tropical forest monitoring systems, we recommend the integration of multiple data sources and techniques for monitoring structural, functional, and compositional attributes. We also recommend its decentralized implementation for adjusting methods to local climatic and ecological characteristics and for proper end-user engagement. The operationalization of the system should be based on all open-source computing platforms, leveraging international support in research and development and ensuring direct and constant user engagement. We recommend continuing the efforts to address these multiple challenges for effective monitoring.

Enriching context descriptions for enhanced LA scalability: a case study

Learning analytics (LA) is a field that examines data about learners and their context, for understanding and optimizing learning and the environments in which it occurs. Integration of multiple data sources, an important dimension of scalability, has the potential to provide rich insights within LA. Using a common standard such as the Experience API (xAPI) to describe learning activity data across multiple sources can alleviate obstacles for data integration. Despite their potential, however, research indicates that standards are seldom used for integration of multiple sources in LA. Our research aims to understand and address the challenges of using current learning activity data standards for describing learning context with regard to interoperability and data integration. In this paper, we present the results of an exploratory case study involving in-depth interviews with stakeholders having used xAPI in a real-world project. Based on the subsequent thematic analysis of interviews, and examination of xAPI, we identified challenges and limitations in describing learning context data, and developed recommendations (provided in this paper in summarized form) for enriching context descriptions and enhancing the expressibility of xAPI. By situating the research in a real-world setting, our research also contributes to bridge the gap between the academic community and practitioners in learning activity data standards and scalability, focusing on description of learning context.

Spatial prediction of sparse events using a discrete global grid system; a case study of hate crimes in the USA

ABSTRACT Spatial prediction of any geographic phenomenon can be an intractable problem. Predicting sparse and uncertain spatial events related to many influencing factors necessitates the integration of multiple data sources. We present an innovative approach that combines data in a Discrete Global Grid System (DGGS) and uses machine learning for analysis. A DGGS provides a structured input for multiple types of spatial data, consistent over multiple scales. This data framework facilitates the training of an Artificial Neural Network (ANN) to map and predict a phenomenon. Spatial lag regression models (SLRM) are used to evaluate and rank the outputs of the ANN. In our case study, we predict hate crimes in the USA. Hate crimes get attention from mass media and the scientific community, but data on such events is sparse. We trained the ANN with data ingested in the DGGS based on a 50% sample of hate crimes as identified by the Southern Poverty Law Center (SPLC). Our spatial prediction is up to 78% accurate and verified at the state level against the independent FBI hate crime statistics with a fit of 80%. The derived risk maps are a guide to action for policy makers and law enforcement.

Knowledge Transfer for Entity Resolution with Siamese Neural Networks

The integration of multiple data sources is a common problem in a large variety of applications. Traditionally, handcrafted similarity measures are used to discover, merge, and integrate multiple representations of the same entity—duplicates—into a large homogeneous collection of data. Often, these similarity measures do not cope well with the heterogeneity of the underlying dataset. In addition, domain experts are needed to manually design and configure such measures, which is both time-consuming and requires extensive domain expertise. We propose a deep Siamese neural network, capable of learning a similarity measure that is tailored to the characteristics of a particular dataset. With the properties of deep learning methods, we are able to eliminate the manual feature engineering process and thus considerably reduce the effort required for model construction. In addition, we show that it is possible to transfer knowledge acquired during the deduplication of one dataset to another, and thus significantly reduce the amount of data required to train a similarity measure. We evaluated our method on multiple datasets and compare our approach to state-of-the-art deduplication methods. Our approach outperforms competitors by up to +26 percent F-measure, depending on task and dataset. In addition, we show that knowledge transfer is not only feasible, but in our experiments led to an improvement in F-measure of up to +4.7 percent.

Drug repurposing improvement using a novel data integration framework based on the drug side effect

The most significant drawback of experimental methods in drug development and discovery is that they are time-consuming and costly. Researches have indicated that designing a new drug from primary stages to its delivery to the consumer market lasts between 10 and 15 years. Moreover, this process costs about 0.8–1.5 billion dollars. Drug repurposing refers to seeking new indications for approved drugs. Recently, some methods have attempted to repurpose drugs based on incorporating computational approaches. In the present research, a method has been proposed for drug repurposing with the aim of integrating diverse and heterogeneous data sources, called DRSE. The proposed method can predict drug-disease associations based on the integration of multiple data sources through a matrix factorization algorithm considering side effect features of the drugs. The experimental results confirmed that the proposed method can improve accuracy of the drug repurposing task. In addition, the AUC and AUPR criteria have been improved by 1.13 and 14.23%, respectively, compared to the state-of-the-art methods. • A framework for drug repurposing by integration of diverse and heterogeneous data sources is proposed. • It predicts drug-disease associations by a matrix factorization algorithm based on side-effect features of drugs. • It uses a Random Walk with Restart (RWR) and feature compacting technique.

GALLO: An R package for genomic annotation and integration of multiple data sources in livestock for positional candidate loci.

BackgroundThe development of high-throughput sequencing and genotyping methodologies has enabled the identification of thousands of genomic regions associated with several complex traits. The integration of multiple sources of biological information is a crucial step required to better understand patterns regulating the development of these traits.FindingsGenomic Annotation in Livestock for positional candidate LOci (GALLO) is an R package developed for the accurate annotation of genes and quantitative trait loci (QTLs) located in regions identified in common genomic analyses performed in livestock, such as genome-wide association studies and transcriptomics using RNA sequencing. Moreover, GALLO allows the graphical visualization of gene and QTL annotation results, data comparison among different grouping factors (e.g., methods, breeds, tissues, statistical models, studies), and QTL enrichment in different livestock species such as cattle, pigs, sheep, and chickens.ConclusionsConsequently, GALLO is a useful package for annotation, identification of hidden patterns across datasets, and data mining previously reported associations, as well as the efficient examination of the genetic architecture of complex traits in livestock.