Integration Of Different Data Sets Research Articles

The influence of salt tectonics on the distribution of the Triassic Skagerrak Formation in the Ula Field, Norwegian North Sea

Distribution of the Triassic succession in the North Sea is poorly understood because of structural complexities associated with halokinesis and limited stratigraphic control. This study uses a seismic and well-based dataset to improve understanding of development of the Triassic succession in the Ula Field Area, in the Norwegian North Sea. Core interpretation revealed a fluvial-dominated depositional environment in the Ula Field Area. Palynological studies allowed dating of cored intervals, revealing Ladinian and Carnian sections, time-equivalent to the Julius and Joanne members of the Skagerrak Formation. Well-log interpretation provided insight into the intra-Triassic stratigraphy of the Ula Field Area. A section considered to be equivalent to and extending from the Smith Bank Formation to the Jonathan Member of the Skagerrak Formation was interpreted and correlated across the area. In the proposed correlation, the Julius Member thins towards the Ula Field Area and is replaced by a time equivalent sandstone unit. The Jonathan Member displays a sandier composition compared to the equivalent section in the UK sector. Seismic facies-based interpretation of Triassic stratigraphy within salt minibasins allowed recognition and mapping of intra-Triassic units and showed that mudstone members thin towards the NE. Interpreted internal geometries within minibasins allowed determination of the timing of halokinesis. Integration of different datasets allowed palaeogeographic reconstructions for the Anisian, early Ladinian, Carnian and Norian to be constructed. To conclude, the distribution of stacked fluvial channel deposits indicates that they occur both within minibasins and across salt highs such that ongoing halokinesis had no topographic expression and that channels were free to migrate across the area.

Analysis of the effect of climatic variability and increasing water demand on the Saïss aquifer, Morocco

Climate variability has a significant impact on the water table of the Saïss plain, which relies heavily on groundwater for agricultural irrigation and drinking water supply. The study of the spatio-temporal evolution of the groundwater level requires a global and integrated approach that considers both natural processes and human activities. Satellite imagery has provided information on land use and land cover that influences groundwater recharge. The GIS has facilitated the integration of different datasets, allowing spatial analysis, mapping and identification of vulnerable areas to depletion. Geostatistical techniques are used to analyze collected data, identify trends and quantify the relationship between groundwater levels and various influencing factors. The results of this study have shown that the depth of the water table of the Saïss plain between 2000 and 2020 has generally decreased by several metres in the NW and the centre zone, but has improved by a few metres or remained constant in the other areas. This information is valuable for developing sustainable water management plans that take into account both environmental and societal needs.

Depositional and diagenetic controllers on the sandstone reservoir quality of the late cretaceous sediments, Gulf of Suez Basin

The complex depositional, burial, and diagenetic histories of the Late Cretaceous Nezzazat Group sandstones in Northeastern Africa present the main challenges with regard to reservoir quality. The quality of commercial reservoirs is maintained despite deep burial and the associated high temperature and pressure. The study presents an optimum integration of different data sets to address reservoir quality and reservoir performance controllers. The data set includes measured porosity and permeability, petrographic point-counting data, grain-size analysis, X-ray diffraction data, scanning electron microscopy, and porosity loss by compaction. The depositional controls on the reservoir quality are the facies, whereas the higher quality is found in the channel and upper shoreface settings. The coarse-grained sandstone is associated with better reservoir quality. The large intergranular porosity is the main porosity controlling the fluid flow. The massive and laminated sandstones are the best quality facies. The labile grains (feldspars and mica) control the permeability distribution. Whereas the secondary diagenetic controllers are the carbonate cementation that inhibited the effects of compaction. The siderite cementation has resulted in a micropore dominated and highly tortuous pore system. Total porosity has largely been preserved in the siderite-cemented sample but virtually eliminated in the dolomite cemented. A low volume of illite is associated with better reservoir quality. Whereas the better reservoir quality is associated with the abundant quartz cementation that protected the primary porosity from compaction. Compaction acts as a significant porosity loss factor during diagenesis. Authigenic kaolinite does not significantly affect the reservoir quality. The reservoir sensitivity to formation damage comes from the potential for fines (kaolinite, illitic clays, siderite, and pyrite) migration within the pore system that is readily mobilized by the fluid flow.

Bathymetric data integration approach to study bedforms in the estuary of the Saint‐Lawrence River

The identification of bedforms has an important role in the study of seafloor morphology. The presence of these dynamic structures on the seafloor represents a hazard for navigation. They also influence the hydrodynamic simulation models used in the context, for example, of coastal flooding. Generally, MultiBeam EchoSounders (MBES) are used to survey these bedforms. Unfortunately, the coverage of the MBES is limited to small areas per survey. Therefore, the analysis of large areas of interest (like navigation channels) requires the integration of different datasets acquired over overlapping areas at different times. The presence of spatial and temporal inconsistencies between these datasets may significantly affect the study of bedforms, which are subject to many natural processes (e.g., Tides; flow). This paper proposes a novel approach to integrate multisource bathymetric datasets to study bedforms. The proposed approach is based on consolidating multisource datasets and applying the Empirical Bayesian Kriging interpolation for the creation of a multisource Digital Bathymetric Model (DBM). It has been designed to be adapted for estuarine areas with a high dynamism of the seafloor, characteristic of the fluvio-marine regime of the Estuary of the Saint-Lawrence River. This area is distinguished by a high tidal cycle and the presence of fields of dunes. The study involves MBES data that was acquired daily over a field of dunes in this area over the span of 4 days for the purpose of monitoring the morphology and migration of dunes. The proposed approach performs well with a resulting surface with a reduced error relative to the original data compared to existing approaches and the conservation of the dune shape through the integration of the data sets despite the highly dynamic fluvio-marine environments.

Chemical Imaging – Is an Image Always Worth a Thousand Spectra?

Chemical images can be described as distribution maps that correlate the chemical information of an element or molecule, such as mass-to-charge ratio (m/z) or wavelength, with its intensity and/or concentration in a given sample. These images are usually obtained by mass spectrometry (MS) or optical spectroscopy techniques, where hundreds or thousands of spectra are initially acquired and dedicated image processing software is employed to construct and edit the final pictures, as well as selecting and annotating regions of interest in a sample, performing calibration procedures, etc. Mass spectrometry imaging (preferably abbreviated as MSI, to distinguish it from ion mobility spectrometry – IMS) is currently the most employed chemical imaging strategy, as can be noticed in the most recently published papers. Depending on the selected ionization technique, molecular or elemental images can be acquired. For molecular MSI, the classical matrix-assisted laser desorption/ionization (MALDI) is generally applied for imaging lipids, peptides and proteins, and the ambient ionization technique desorption electrospray ionization (DESI) is commonly applied for visualizing lipid distribution. In terms of elemental MSI, laser ablation inductively coupled plasma (LA-ICP) is undoubtedly the technique of choice, although nano-secondary ion mass spectrometry (nanoSIMS) can also be applied. Considering optical spectroscopy, the main techniques used nowadays are Raman and near-infrared radiation – NIR – spectroscopy for molecular imaging, and Synchrotron radiation X-ray fluorescence – SRXRF – and laser-induced breakdown spectroscopy – LIBS – for elemental imaging. Amongst these techniques, the best spatial resolutions are generally achieved by SRXRF (elemental imaging) and Raman spectroscopy (molecular imaging). Analytical chemistry advances in chemical imaging allow the acquisition of images with high spatial resolution, which is particularly interesting when studying specific regions or cell structures in a biological sample. For instance, in a Parkinson’s disease model, LA-ICP-MS images with good spatial resolution make the distinction of specific mouse brain regions possible and, consequently, the association of metal ion concentrations to each region,1 which is a relevant result considering micro-local metal speciation in neurodegenerative diseases. Nevertheless, there are some drawbacks in chemical imaging that demand further analytical development, such as the long analysis time and the lack of certified reference materials for quantitative analysis and method validation, as well as open-source software with advanced multivariate statistical analysis tools. Another obstacle to overcome concerns the integration of elemental and molecular imaging results. Since 2009, when one of the first review articles regarding the combination of these imaging approaches in a synergistic way was proposed by Becker and Jakubowski,2 until more recently described in reviews from 20203 and 2021,4 it has been possible to realize that there is still much work to be done in this field. This is mostly due to the fact that each imaging technique provides different spatial resolutions, making image superposition difficult, and also the absence of software and algorithms that allow the integration of different data sets in order to obtain trustworthy results and produce relevant study hypotheses. Besides that, the instrumentation for chemical imaging is rather costly and usually research groups are specialized in either molecular or elemental imaging. With these considerations, it is important to emphasize that the community involved in chemical imaging research should focus not only on the quality of the generated images in terms of resolution but also, if they are indeed worth a thousand spectra, on interpretation of the initial questions in a deep and holistic manner. After all, the main objective of chemical imaging is that the images represent how the process in question (disease, treatment, contamination, genetic modification, etc.) locally affects the system (biological, environmental, pharmaceutical sample) under study and, then, provide solutions for solving problems in different areas, such as forensic, environmental and life sciences.

Bdc: A toolkit for standardizing, integrating and cleaning biodiversity data

Abstract The increase in online and openly accessible biodiversity databases provides a vast and invaluable resource to support research and policy. However, without scrutiny, errors in primary species occurrence data can lead to erroneous results and misleading information. Here, we introduce the Biodiversity Data Cleaning (bdc), an R package to address quality issues and improve the fitness‐for‐use of biodiversity datasets. The bdc package brings together several aspects of biodiversity data cleaning in one place. It is organized in thematic modules related to different biodiversity dimensions, including (a) Merge datasets: standardization and integration of different datasets; (b) Pre‐filter: flagging and removal of invalid or non‐interpretable information, followed by data amendments; (c) Taxonomy: cleaning, parsing and harmonization of scientific names from several taxonomic groups against taxonomic databases locally stored through the application of exact and partial matching algorithms; (d) Space: flagging of erroneous, suspect and low‐precision geographic coordinates; and (e) Time: flagging and, whenever possible, correction of inconsistent collection date. In addition, the package contains features to visualize, document and report data quality—which is essential for making data quality assessment transparent and reproducible. The modules illustrated, and functions within, were linked to form a proposed reproducible workflow that can also integrate functions from other R packages. We demonstrated the bdc package's applicability in cleaning more than 30 million occurrence records for terrestrial plant species in Brazil. We found that around one‐fifth of the original datasets hold the standard quality requirements. Compared to other available R packages, the main strengths of the bdc package are that it brings together available tools—and a series of new ones—to assess the quality of different dimensions of biodiversity data into a single and flexible toolkit. The functions can be applied to many taxonomic groups, datasets (including regional or local repositories), countries, or world‐wide. We hope the bdc package can facilitate the data cleaning process and catalyse improvements to allow the wise and efficient use of primary biodiversity data.

Coastal subsidence increases vulnerability to sea level rise over twenty first century in Cartagena, Caribbean Colombia

Cartagena is subsiding at a higher rate compared to that of global climate-driven sea level rise. We investigate the relative sea level rise (RSLR) and the influence of vertical land movements in Cartagena through the integration of different datasets, including tide gauge records, GPS geodetic subsidence data, and Interferometric Synthetic Aperture Radar (InSAR) observations of vertical motions. Results reveal a long-term rate (> 60 years) of RSLR of 5.98 ± 0.01 mm/yr. The last two decades exhibited an even greater rate of RSLR of 7.02 ± 0.06 mm/yr. GPS subsidence rates range between − 5.71 ± 2.18 and − 2.85 ± 0.84 mm/yr. InSAR data for the 2014–2020 period show cumulative subsidence rates of up to 72.3 mm. We find that geologically induced vertical motions represent 41% of the observed changes in RSLR and that subsidence poses a major threat to Cartagena’s preservation. The geodetic subsidence rates found would imply a further additional RSLR of 83 mm by 2050 and 225 mm by 2100. The Colombian government should plan for the future and serve as an example to similar cities across the Caribbean.

Integrating citizen‐science and planned‐survey data improves species distribution estimates

AbstractAimMapping species distributions is a crucial but challenging requirement of wildlife management. The frequent need to sample vast expanses of potential habitat increases the cost of planned surveys and rewards accumulation of opportunistic observations. In this paper, we integrate planned‐survey data from roost counts with opportunistic samples from eBird, WikiAves and Xeno‐canto citizen‐science platforms to map the geographic range of the endangered Vinaceous‐breasted Parrot. We demonstrate the estimation and mapping of species occurrence based on data integration while accounting for specifics of each dataset, including observation technique and uncertainty about the observations.LocationArgentina, Brazil and Paraguay.MethodsOur analysis illustrates (a) the incorporation of sampling effort, spatial autocorrelation and site covariates in a joint‐likelihood, hierarchical, data integration model; (b) the evaluation of the contribution of each dataset, as well as the contribution of effort covariates, spatial autocorrelation and site covariates to the predictive ability of fitted models using a cross‐validation approach; and (c) how spatial representation of the latent occupancy state (i.e. realized occupancy) helps identify areas with high uncertainty that should be prioritized in future fieldwork.ResultsWe estimate a Vinaceous‐breasted Parrot geographic range of 434,670 km2, which is three times larger than the “Extant” area previously reported in the IUCN Red List. The exclusion of one dataset at a time from the analyses always resulted in worse predictions by the models of truncated data than by the Full Model, which included all datasets. Likewise, exclusion of spatial autocorrelation, site covariates or sampling effort resulted in worse predictions.Main conclusionsThe integration of different datasets into one joint‐likelihood model produced a more reliable representation of the species range than any individual dataset taken on its own, improving the use of citizen‐science data in combination with planned‐survey results.

Changing environments and human interaction during the Pleistocene–Early Holocene from the shallow coastal area of Dor, Israel

AbstractThe protected Tel-Dor coastal embayment in the eastern Mediterranean preserves an unusually complete stratigraphic record that reveals human–environmental interactions throughout the Holocene. Interpretation of new seismic profiles collected from shallow marine geophysical transects across the bay show five seismic units were correlated with stratigraphy and age dates obtained from coastal and shallow-marine sediment cores. This stratigraphic framework permits a detailed reconstruction of the coastal system over the last ca. 77 ka as well as an assessment of environmental factors that influenced some dimensions of past coastal societies. The base of the boreholes records lowstand aeolian deposits overlain by wetland sediments that were subsequently flooded by the mid-Holocene transgression. The earliest human settlements are submerged Pottery Neolithic (8.25–7 ka) structures and tools, found immediately above the wetland deposits landward of a submerged aeolianite ridge at the mouth of the bay. The wetland deposits and Pottery Neolithic settlement remains are buried by coastal sand that records a middle Holocene sea-level rise ca. 7.6–6.5 ka. Stratigraphic and geographic relationships suggest that these coastal communities were displaced by sea-level transgression. These findings demonstrate how robust integration of different data sets can be used to reconstruct the geomorphic evolution of coastal settings as well as provide an important addition to the nature of human–landscape interaction and cultural development.

City-EYE a platform for the surveillance and management of built-up area and infrastructures

Abstract Cities and built-up areas are the main environment of human civilization in the 21st century. The quick development of Space industry and processing capabilities allow near-real time monitoring of large target areas in cities. A proof-of-concept of a platform for the surveillance and management of built-up area and infrastructures (City-EYE) has been developed by Detektia and Insar.sk space companies, supported by scientific validation. The potential of City.EYE lies in the two-level analysis of entire cities and in the integration of different datasets from various SAR sensors and data dating from 1992 to present. The processed PSI data is input to the platform, together with relevant monitoring data (climate, urban development…). A two-scale approach allows monitoring the city and built up areas as a whole while simultaneously monitoring in detail critical infrastructures and buildings. City-EYE is part of a new generation of management platforms that integrate Space data in daily operations.

Developing 3D geological and hydrogeological models for the Low Level Waste Repository site, west Cumbria, UK

This case study presents the recent development of new 3D geological models for the Low Level Waste Repository in Cumbria through the review of historical and new datasets. The site is located in an area of complex Late Pleistocene glacial geology and geomorphology, which control near-surface groundwater movement, and benefits from extensive historical site characterization data. This work emphasizes the value of a quality review of existing borehole records, drilling techniques and supplementary information to build a predictive geological model. The integration of different datasets has proved crucial to our understanding of the ground conditions, which are characterized by complex Quaternary sequences, and the depositional processes responsible for their formation. The updated geological interpretation suggests that the Low Level Waste Repository is located on a former ice-dammed lake plain. This is contrary to the previous interpretation of multiple and extensive till units in the area. Although the permeabilities may be similar in both interpretations, the extents may be different. Understanding the depositional processes and extent and the hydrogeological characteristics of the deposits is key to developing a hydrogeological model that is appropriate for the assessment of the environmental safety of the site, both now and into the future.Thematic collection: This article is part of the Ground models in engineering geology and hydrogeology collection available at: https://www.lyellcollection.org/cc/Ground-models-in-engineering-geology-and-hydrogeology

Executable cancer models: successes and challenges.

Making decisions on how best to treat cancer patients requires the integration of different data sets, including genomic profiles, tumour histopathology, radiological images, proteomic analysis and more. This wealth of biological information calls for novel strategies to integrate such information in a meaningful, predictive and experimentally verifiable way. In this Perspective we explain how executable computational models meet this need. Such models provide a means for comprehensive data integration, can be experimentally validated, are readily interpreted both biologically and clinically, and have the potential to predict effective therapies for different cancer types and subtypes. We explain what executable models are and how they can be used to represent the dynamic biological behaviours inherent in cancer, and demonstrate how such models, when coupled with automated reasoning, facilitate our understanding of the mechanisms by which oncogenic signalling pathways regulate tumours. We explore how executable models have impacted the field of cancer research and argue that extending them to represent a tumour in a specific patient (that is, an avatar) will pave the way for improved personalized treatments and precision medicine. Finally, we highlight some of the ongoing challenges in developing executable models and stress that effective cross-disciplinary efforts are key to forward progress in the field.

New Horizons in the use of routine data for ageing research.

The past three decades have seen a steady increase in the availability of routinely collected health and social care data and the processing power to analyse it. These developments represent a major opportunity for ageing research, especially with the integration of different datasets across traditional boundaries of health and social care, for prognostic research and novel evaluations of interventions with representative populations of older people. However, there are considerable challenges in using routine data at the level of coding, data analysis and in the application of findings to everyday care. New Horizons in applying routine data to investigate novel questions in ageing research require a collaborative approach between clinicians, data scientists, biostatisticians, epidemiologists and trial methodologists. This requires building capacity for the next generation of research leaders in this important area. There is a need to develop consensus code lists and standardised, validated algorithms for common conditions and outcomes that are relevant for older people to maximise the potential of routine data research in this group. Lastly, we must help drive the application of routine data to improve the care of older people, through the development of novel methods for evaluation of interventions using routine data infrastructure. We believe that harnessing routine data can help address knowledge gaps for older people living with multiple conditions and frailty, and design interventions and pathways of care to address the complex health issues we face in caring for older people.

Preface: “Improving seismic networks performances: from site selection to data integration (EGU2019 SM5.2 session)”

Abstract. The number and quality of seismic stations and networks in Europe continually improves, nevertheless there is always scope to optimize their performance. In this EGU2019 SM5.2/GI4.13 session we welcomed contributions from all aspects of seismic network installation, operation and management. This includes site selection; equipment testing and installation; planning and implementing communication paths; policies for redundancy in data acquisition, processing and archiving; and integration of different data sets including GPS and OBS.

PNS29 HEALTH RESEARCH AND BIG DATA IN UKRAINE: PERSPECTIVES AND CONCERNS

The big medical data constitute huge sets of information concerning population characteristics, information about risk factors, prognostic or predictive models, evaluation of healthcare providers, etc., that means integration of different datasets. Today the resources of health data become more numerous. Digital transformation of healthcare is a process that actively evolved during the last decade across the whole world. In Ukraine it was started in 2018 and now it is in process. To date, the national public health database (eHealth) was developed and implemented and more than 24 billion patients (nearly 56% of the total population) have become covered by eHealth system. This reformation will provide exceptional opportunities for the use of medical big data in health economic sand outcomes researches (HEOR). It would be possible to use real-world data, identify specific populations and to build dynamic models that could facilitate decision making in all key fields of Ukrainian healthcare. However, the application of big data concept in Ukraine has a range of limitations and raises some serious ethical concerns. The most important is a quality issue which is caused by a lack of rigorous scientific methodology in conducting such researches, technical obstacles in extracting data and managing datasets, assuring data completeness and accuracy. There is also a lot of ethical concerns stemmed from the release of personal data, lack of transparency, security issues. It is unclear how the concept of informed consent should be used, especially considering the fact that most data are identifiable and some of them might be generated from various sources including payment, administration information, and social media accounts. Therefore, medical big data provide great perspectives for HEOR in Ukraine. However, it needs to study a range of ethical, methodological and legislative concerns and elaborate practical framework for prevention of its future undue using and misunderstanding.

Deconvolution of transcriptomes and miRNomes by independent component analysis provides insights into biological processes and clinical outcomes of melanoma patients

BackgroundThe amount of publicly available cancer-related “omics” data is constantly growing and can potentially be used to gain insights into the tumour biology of new cancer patients, their diagnosis and suitable treatment options. However, the integration of different datasets is not straightforward and requires specialized approaches to deal with heterogeneity at technical and biological levels.MethodsHere we present a method that can overcome technical biases, predict clinically relevant outcomes and identify tumour-related biological processes in patients using previously collected large discovery datasets. The approach is based on independent component analysis (ICA) – an unsupervised method of signal deconvolution. We developed parallel consensus ICA that robustly decomposes transcriptomics datasets into expression profiles with minimal mutual dependency.ResultsBy applying the method to a small cohort of primary melanoma and control samples combined with a large discovery melanoma dataset, we demonstrate that our method distinguishes cell-type specific signals from technical biases and allows to predict clinically relevant patient characteristics. We showed the potential of the method to predict cancer subtypes and estimate the activity of key tumour-related processes such as immune response, angiogenesis and cell proliferation. ICA-based risk score was proposed and its connection to patient survival was validated with an independent cohort of patients. Additionally, through integration of components identified for mRNA and miRNA data, the proposed method helped deducing biological functions of miRNAs, which would otherwise not be possible.ConclusionsWe present a method that can be used to map new transcriptomic data from cancer patient samples onto large discovery datasets. The method corrects technical biases, helps characterizing activity of biological processes or cell types in the new samples and provides the prognosis of patient survival.

Integrative analysis of pathway deregulation in obesity

Obesity is a pandemic disease, linked to the onset of type 2 diabetes and cancer. Transcriptomic data provides a picture of the alterations in regulatory and metabolic activities associated with obesity, but its interpretation is typically blurred by noise. Here, we solve this problem by collecting publicly available transcriptomic data from adipocytes and removing batch effects using singular value decomposition. In this way we obtain a gene expression signature of 38 genes associated to obesity and identify the main pathways involved. We then show that similar deregulation patterns can be detected in peripheral markers, in type 2 diabetes and in breast cancer. The integration of different data sets combined with the study of pathway deregulation allows us to obtain a more complete picture of gene-expression patterns associated with obesity, breast cancer, and diabetes.

Reassessment of recommendations for processing mammal phosphate δ18O data for paleotemperature reconstruction

The stable oxygen isotope composition of phosphates (δ18OP) extracted from vertebrate bone and teeth material is widely used for paleotemperature reconstruction over the Quaternary period. However, inconsistency in analytical and statistical procedures used by different researchers for recalculation of mean air temperatures from measured δ18OP values makes integration of different data sets challenging. Therefore, consistency in both stable isotope data processing and the application of appropriate statistical procedures is highly desirable. This paper evaluates the uncertainty associated with two most commonly used regression methods, least-squares inverted fit and transposed fit. We assessed the performance of both fits by designing ‘real-life’ experiments using various data sets. We used both regression models to reversely calculate the differences between actual recorded in ‘real-life’ values and values calculated from the regression models. Our calculations clearly show that the mean absolute errors are always substantially higher for the inverted fit and the transposed fit always returned mean values closer to the values measured in ‘real life.’ In all studied scenarios, the predictive models performed better than causal models, returning mean absolute errors by 12–65% lower. In the paper, we also examined the other main sources of uncertainty, concerns about calculation of δ18OP of phosphates from δ18OC of carbonates, averaging of δ18OW in precipitation and highlight major challenges in the field of phosphate stable oxygen isotope biogeochemistry. The estimation of paleotemperatures from isotope records could be improved by employing globally consistent calculation procedures.

Preface: "Improving seismic networks performances: from site selection to data integration" (EGU2015 SM1.2/GI1.5 session)

Abstract. The number and quality of seismic stations and networks in Europe continually improves, nevertheless there is always scope to optimize their performance. In this EGU2015 SM1.2/GI1.5 session we welcomed contributions from all aspects of seismic network installation, operation and management. This includes site selection; equipment testing and installation; planning and implementing communication paths; policies for redundancy in data acquisition, processing and archiving; and integration of different data sets including GPS and OBS.

Quantitative assessment and validation of network inference methods in bioinformatics.

Quantitative assessment and validation of network inference methods in bioinformatics.