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14
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ShareScore release 0.9.0
Dataset results
14 results for “Process Discovery”
Improving the drug discovery process by using multiple classifier systems
<p>High-quality dataset gathered from ChEMBL version 22 based on UniProt accession P34972. Regarding to activity data potential, duplicates were ignored, no activity or data validity comments were allowed, only data from binding assays and with a pCheMBL value were kept. This led to a dataset composed of 3925 chemical compounds (instances) represented using 2132 features. The first 2048 features epitomize different chemical structures fingerprints (represented using FCFP_6 notation), while the remaining 84 are associated with several physicochemical descriptors (such as Fractional Polar Surface Area, Rotatable Bonds or Molecular Weight). Finally, the set was transformed into a binary classification set where the activity cut-off was defined at a pChEMBL value > 7 and written to a tab-delimited text file. The final set contained 1977 active compounds and 1948 inactive compounds. Table 3 shows the codification of each feature grouped by type.</p>
Compositional discovery of architecture-aware and sound process models from event logs of multi-agent systems: experimental data.
<p>This repository contains the experimental data used for the evaluation of the compositional approach to the discovery of process models from event logs of multi-agent systems, where agents interact according to specific patterns of synchronous and asynchronous interactions.</p> <p>According to the experiment plan, there is the folder for each interface pattern containing:</p> <ol> <li>The reference model (Petri net encoded in PNML-file)</li> <li>The event log obtained by simulating the behavior of the reference model (XES-file)</li> <li>The model discovered directly from the generated event log (Petri net encoded in PNML-file)</li> <li>The model discovered by composing the agent model w.r.t. the interface pattern (Petri net encoded in PNML-file)</li> </ol>
Processed CODEX Datasets from - Discovery and Generalization of Tissue Structures from Spatial Omics Data
<p>This entry provides access to processed CODEX data files of four studies analyzed in the article "Discovery and Generalization of Tissue Structures from Spatial Omics Data". Details of datasets can be found in the STAR Methods section of the article.</p> <p>For each dataset, a zip file containing multiple comma-separated values (CSV) files is included.</p> <p>Each region is assigned an unique identifier (e.g., DKD_kidney_001), and its related data files are:</p> <ul> <li>`{region_id}.cell_data.csv`, a table containing three columns: "CELL_ID", "X", and "Y". This table provides centroid locations for all cells segmented in this region.</li> <li>`{region_id}.expression.csv`, a table containing multiple columns: "CELL_ID", "DAPI", "CD45", etc. This table provides detailed protein biomarker expression quantified for all cells in this region.</li> <li>`{region_id}.scgp_annotations.csv`, a table containing two columns: "CELL_ID" and "SCGP". This table provides SCGP/SCGP-Extension annotations for all cells in this region.</li> </ul> <p>Code base for SCGP is also included in this entry. Please refer to <a href="https://gitlab.com/enable-medicine-public/scgp">https://gitlab.com/enable-medicine-public/scgp</a> for the latest codes, questions, and/or issues. Raw CODEX data and images will be accessible through links posted at the code base. Raw data will also be available from lead contact (A.E.T.) upon request.</p>
FIGURE. Taphonomic process corresponding to the abundance of different kinds of plant remains in the three layers of "vegetational Pompeii" tuff bed. Single, double and triple repeated icons in different layers indicate rare, moderate and frequent occurrence respectively. Note that the thickness of the tuff bed is scaled but that of the two coal beds is neglected. in Discovery of coprolites in an Early Permian fern mesophyll
FIGURE. Taphonomic process corresponding to the abundance of different kinds of plant remains in the three layers of "vegetational Pompeii" tuff bed. Single, double and triple repeated icons in different layers indicate rare, moderate and frequent occurrence respectively. Note that the thickness of the tuff bed is scaled but that of the two coal beds is neglected.
Experiment results for paper about discovery of improvement opportunities in knock-out checks of Business Processes
<p>This dataset contains the results (in .csv format) of experiments performed using the code from the paper "Minimizing Overprocessing Waste via Predictive Activity Ordering" (https://doi.org/10.1007/978-3-319-39696-5_12).</p> <p>Each file corresponds to an execution of the entire training & evaluation pipeline, each time with different random subsets of the full dataset. They contain the number of knockout checks performed with 3 different approaches (variable rejection probabilities, constant rejection probabilities, and random ordering).</p>
Data for "The increase in prevalence of natural products through the drug discovery process"
Open the record for dataset details and reuse information.
Extending Genetic Process Discovery to Reveal Unfairness in Processes - Supplementary Material
Open the record for dataset details and reuse information.
A functional genomics process for systematic dissection and mutation-specific target discovery in breast cancer PIK3CA hotspot mutations [CRISPR KO Screen Data]
GEO Series GSE248041. Homo sapiens. 6 samples. Type: Other.
A functional genomics process for systematic dissection and mutation-specific target discovery in breast cancer PIK3CA hotspot mutations [RNA-seq]
GEO Series GSE247819. Homo sapiens. 9 samples. Type: Expression profiling by high throughput sequencing.
Systematic discovery of RNA binding proteins that control microRNA processing
GEO Series GSE102497. Homo sapiens. 68 samples. Type: Non-coding RNA profiling by high throughput sequencing.
A functional genomics process for systematic dissection and mutation-specific target discovery in breast cancer PIK3CA hotspot mutations
GEO Series GSE247822. Homo sapiens. 21 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
A functional genomics process for systematic dissection and mutation-specific target discovery in breast cancer PIK3CA hotspot mutations [ATAC-Seq]
GEO Series GSE247821. Homo sapiens. 6 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Sparse Inverse Gaussian Process Regression with Application to Climate Network Discovery
Regression problems on massive data sets are ubiquitous in many application domains including the Internet, earth and space sciences, and finances. Gaussian Process regression is a popular technique for modeling the input-output relations of a set of variables under the assumption that the weight vector has a Gaussian prior. However, it is challenging to apply Gaussian Process regression to large data sets since prediction based on the learned model requires inversion of an order n kernel matrix. Approximate solutions for sparse Gaussian Processes have been proposed for sparse problems. However, in almost all cases, these solution techniques are agnostic to the input domain and do not preserve the similarity structure in the data. As a result, although these solutions sometimes provide excellent accuracy, the models do not have interpretability. Such interpretable sparsity patterns are very important for many applications. We propose a new technique for sparse Gaussian Process regression that allows us to compute a parsimonious model while preserving the interpretability of the sparsity structure in the data. We discuss how the inverse kernel matrix used in Gaussian Process prediction gives valuable domain information and then adapt the inverse covariance estimation from Gaussian graphical models to estimate the Gaussian kernel. We solve the optimization problem using the alternating direction method of multipliers that is amenable to parallel computation. We demonstrate the performance of our method in terms of accuracy, scalability and interpretability on a climate data set.
CIPHER: An extensive and flexible processing platform for next-generation sequencing data and de-novo enhancer discovery
GEO Series GSE89871. Homo sapiens. 1 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.