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48 results for “recommender systems”
A unified DTI prediction framework based on knowledge graph and recommendation system
<p>## A unified DTI prediction framework based on knowledge graph and recommendation system</p> <p> </p> <p># Code and data description</p> <p>## Scripts</p> <p>- `kge_nfm.py`: the complement of the KGE_NFM & NFM methods.</p> <p>- `kge_rf.py`: the complement of the KGE_RF & RF methods.</p> <p>- `deepdit.py`: the complement of the MPNN_CNN & DeepDTI methods.</p> <p>- the complement of DTINet and DTiGEMS is tested based on their source packages (more in Prerequisites)</p> <p><br> </p> <p>## `data/` directory</p> <p>#### `yamanishi_08/` directory</p> <p>- `data_folds/`: 10 folds training set and test set in the three scenarios</p> <p>- `warm_start_1_1/`</p> <p>- `warm_start_1_10/`</p> <p>- `drug_coldstart/`</p> <p>- `protein_coldstart/`</p> <p>- `kg_data/`: supporting knowledge graph data</p> <p>- `dt_all_08.csv`: whole DTI dataset</p> <p>- `791drug_struc.csv`: drugbank id and smiles of drugs</p> <p>- `989proseq.csv`: kegg id and sequences of proteins</p> <p>- `morganfp.txt`: list of drug morgan fingerprints</p> <p>- `pro_ctd.txt`: list of protein descriptors</p> <p> </p> <p>#### `BioKG/` directory</p> <p>- `data_folds/`: 10 folds training set and test set in the three scenarios</p> <p>- `warm_start_1_10/`</p> <p>- `drug_coldstart/`</p> <p>- `protein_coldstart/`</p> <p>- `kg.csv`: supporting knowledge graph data</p> <p>- `dti.csv`: whole DTI dataset</p> <p>- `comp_struc.csv`: drugbank id and smiles of drugs</p> <p>- `pro_seq.csv`: sequences of proteins</p> <p>- `fp_df.csv`: list of drug morgan fingerprints</p> <p>- `prodes_df.csv`: list of protein descriptors</p> <p> </p> <p>#### `hetionet/` directory</p> <p>- `data_folds/`: 10 folds training set and test set in the three scenarios</p> <p>- `warm_start_1_10/`</p> <p>- `drug_coldstart/`</p> <p>- `protein_coldstart/`</p> <p>- `kg.csv`: supporting knowledge graph data</p> <p>- `dti.csv`: whole DTI dataset</p> <p>- `map_drugs_df`: drugbank id and smiles of drugs</p> <p>- `pro_seq.csv`: sequences of proteins</p> <p>- `fp_df.csv`: list of drug morgan fingerprints</p> <p>- `prodes_df.csv`: list of protein descriptors</p> <p> </p> <p>#### `luo's_dataset/` directory</p> <p>- `data_folds/`: 10 folds training set and test set in the three scenarios</p> <p>- `warm_start_1_1/`</p> <p>- `warm_start_1_10/`</p> <p>- `drug_coldstart/`</p> <p>- `protein_coldstart/`</p> <p>- `mapping/`: related mappings and similarity matrix (https://github.com/luoyunan/DTINet)</p> <p>- `protein.txt`: list of protein names</p> <p>- `disease.txt`: list of disease names</p> <p>- `se.txt`: list of side effect names</p> <p>- `drug_dict_map`: a complete ID mapping between drug names and DrugBank ID</p> <p>- `protein_dict_map`: a complete ID mapping between protein names and UniProt ID</p> <p>- `Similarity_Matrix_Drugs.txt` : Drug similarity scores based on chemical structures of drugs</p> <p>- `Similarity_Matrix_Proteins.txt` : Protein similarity scores based on primary sequences of proteins</p> <p>- `feature/`: related features used in methods</p> <p>- `drug_smiles.csv`: drugbank id and smiles</p> <p>- `seq.txt`: list of protein sequences</p> <p>- `morganfp.txt`: list of drug morgan fingerprints</p> <p>- `pro_ctd.txt`: list of protein descriptors</p> <p> </p> <p>#### `eg_model/` directory</p> <p>We provided a pre-trained kge model for example.</p> <p>- `dismult_400_warm_1_10.pkl`</p> <p><br> </p> <p># Prerequisites</p> <p>#### Operating system: Linux</p> <p>#### Programing language: python</p> <p>#### KGE_NFM & NFM dependencies</p> <p>```</p> <p>- python 3.6</p> <p>- pandas '1.1.5'</p> <p>- numpy '1.18.4'</p> <p>- scikit-learn '0.24.1'</p> <p>- tensorflow '1.15.0'</p> <p>- ampligraph '1.3.2'</p> <p>- deepctr '0.8.4'</p> <p>```</p> <p>#### baseline dependencies</p> <p>- RF & KGE_RF (included in KGE_NFM&NFM dependencies)</p> <p>- MPNN_CNN & DeepDTI:</p> <p>- source: https://github.com/kexinhuang12345/DeepPurpose</p> <p>```</p> <p>- deeppurpose '0.0.9'</p> <p>- torch '1.6.0+cu101'</p> <p>```</p> <p>- DTINet:</p> <p>- source: https://github.com/luoyunan/DTINet</p> <p>- note: in this work, we run the DTINet in a python environment, which need Linux system and python2. Importantly, this method requires the [Inductive Matrix Completion](http://bigdata.ices.utexas.edu/software/inductive-matrix-completion/) (IMC) library. More detailed information about the installation of this method could be found in the source code of the DTINet.</p> <p>- DTiGEMS:</p> <p>- source: https://github.com/MahaThafar/DTiGEMSplus</p> <p>- TriModel:</p> <p>- source: http://drugtargets.insight-centre.org/</p> <p><br> <br> </p> <p># Example (kge_nfm.py)</p> <p> </p> <p>#### A brief presentation of the results:</p> <p>- return average loss when training kge model</p> <p>```</p> <p>Average Loss: 0.475181: 2%|###3 | 1/50 [01:10<57:31, 70.44s/epoch]</p> <p>```</p> <p>- return performance(mrr) on training set of DTI for early stopping (kge_model in `eg_model/`)</p> <p>```</p> <p>In [35]: roc = roc_auc(test_label,test_score)</p> <p>...: pr = pr_auc(test_label,test_score)</p> <p>...: print(roc)</p> <p>...: print(pr)</p> <p>0.8731770833333332</p> <p>0.44079654835037246</p> <p>```</p> <p> </p> <p>- nfm training process (`patience=10`)</p> <p> </p> <p>```</p> <p>In [45]: roc_nfm,pr_nfm,pred_y = train_nfm(feature_columns,train_model_input,train_label,test_model_input,test_label,patience)</p> <p>Train on 44851 samples</p> <p>Epoch 1/2000</p> <p>44851/44851 - 2s - loss: 0.5332 - precision: 0.0976</p> <p>Epoch 2/2000</p> <p>44851/44851 - 1s - loss: 0.4143 - precision: 0.0000e+00</p> <p>Epoch 3/2000</p> <p>44851/44851 - 1s - loss: 0.3456 - precision: 0.0000e+00</p> <p>Epoch 4/2000</p> <p>44851/44851 - 1s - loss: 0.3443 - precision: 0.0000e+00</p> <p>Epoch 5/2000</p> <p>44851/44851 - 1s - loss: 0.3470 - precision: 0.0000e+00</p> <p>Epoch 6/2000</p> <p>44851/44851 - 1s - loss: 0.3382 - precision: 0.0000e+00</p> <p>......</p> <p>Epoch 279/2000</p> <p>44851/44851 - 1s - loss: 0.0758 - precision: 0.9248</p> <p>Epoch 280/2000</p> <p>44851/44851 - 1s - loss: 0.0753 - precision: 0.9327</p> <p>Epoch 281/2000</p> <p>44851/44851 - 1s - loss: 0.0796 - precision: 0.9155</p> <p>Epoch 282/2000</p> <p>44851/44851 - 1s - loss: 0.0764 - precision: 0.9276</p> <p>Epoch 283/2000</p> <p>44851/44851 - 1s - loss: 0.0739 - precision: 0.9127</p> <p>```</p> <p> </p> <p>- reutrn results as type of roc_auc & pr_auc</p> <p>```</p> <p>0.9812476679104477</p> <p>0.8803416284646345</p> <p>```</p>
Deep Learning-Based Recommendation System: Systematic Review and Classification - Outputs
<p>The datasets provided are the outputs of the paper titled "Deep Learning-Based Recommendation System: Systematic Review and Classification." They encompass multiple outputs, including primary articles, domain-focused articles, technique mapping, and domain mapping for each category.</p>
The Study of Recommendation System DiaCompanion for Women With Gestational Diabetes Mellitus
ClinicalTrials.gov study NCT05179798. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Patient Experience Recommender System for Persuasive Communication Tailoring
ClinicalTrials.gov study NCT02200432. IPD Sharing: Not stated. Countries: 1. Publications: 2.
The System Fammeal With Tailored Recommendations and Gamification for Parents to Counteract Childhood Obesity
ClinicalTrials.gov study NCT03881280. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: A clinical decision support system learned from data to personalize treatment recommendations towards preventing breast cancer metastasis
Open the record for dataset details and reuse information.
Supporting Information for Recommender Systems in Antiviral Drug Discovery
<p>Supporting Information for Recommender Systems in Antiviral Drug Discovery</p>
Figure 5 from: Landel S, Lymer G, Pasterk M, Guiraud M, Worley K (2024) A report on recommendations for the most suitable financial contribution model for the Distributed System of Scientific Collections Research Infrastructure (DiSSCo-RI). Research Ideas and Outcomes 10: e117217. https://doi.org/10.3897/rio.10.e117217
Figure 5 Option C: GDP/cap and GERD. Vertical axis: annual monetary contribution per country. Horizontal axis: countries corresponding to Table (left).
Figure 3 from: Landel S, Lymer G, Pasterk M, Guiraud M, Worley K (2024) A report on recommendations for the most suitable financial contribution model for the Distributed System of Scientific Collections Research Infrastructure (DiSSCo-RI). Research Ideas and Outcomes 10: e117217. https://doi.org/10.3897/rio.10.e117217
Figure 3 Option A: GDP and GERD testing. Vertical axis: annual monetary contribution per country. Horizontal axis: countries corresponding to table (left).
Figure 4 from: Landel S, Lymer G, Pasterk M, Guiraud M, Worley K (2024) A report on recommendations for the most suitable financial contribution model for the Distributed System of Scientific Collections Research Infrastructure (DiSSCo-RI). Research Ideas and Outcomes 10: e117217. https://doi.org/10.3897/rio.10.e117217
Figure 4 Option B with GDP and GERD/cap. Vertical axis: annual monetary contribution per country. Horizontal axis: countries corresponding to Table (left).
Figure 10 from: Landel S, Lymer G, Pasterk M, Guiraud M, Worley K (2024) A report on recommendations for the most suitable financial contribution model for the Distributed System of Scientific Collections Research Infrastructure (DiSSCo-RI). Research Ideas and Outcomes 10: e117217. https://doi.org/10.3897/rio.10.e117217
Figure 10 Visualisation of annual membership fees distribution according to the two proposals selected.
Replication Package for "Improving and Evaluating Code Recommender Systems"
<p>Replication Package for "Improving and Evaluating Code Recommender Systems"</p>
Data for The "Effect of Semantic Knowledge Graph Richness on Embedding Based Recommender Systems"
Open the record for dataset details and reuse information.
recommender systems
<p>A collection of datasets for recommender systems.</p>
Usage Requirements on Recommender Systems for a Web Platform for Continuing Education in Public Transport in Germany
<p>The dataset contains usage requirements on recommender systems for a web platform for continuing education in public transport in Germany. The underlying data was collected in 28 stakeholder interviews. </p>
E-Course Recommendation System
<p>Students data with all attributes for course recommendation system</p>
Obtaining a Set of Recommendations for Evolving Executable Languages towards Systems-of-Systems Architecture Design
<p>Obtaining a Set of Recommendations for Evolving Executable Languages towards Systems-of-Systems Architecture Design</p>
Figure 6 from: Landel S, Lymer G, Pasterk M, Guiraud M, Worley K (2024) A report on recommendations for the most suitable financial contribution model for the Distributed System of Scientific Collections Research Infrastructure (DiSSCo-RI). Research Ideas and Outcomes 10: e117217. https://doi.org/10.3897/rio.10.e117217
Figure 6 Simulating inflation, between 2024 and 2040 – Basic number: 2% inflation per year.
Figure 8 from: Landel S, Lymer G, Pasterk M, Guiraud M, Worley K (2024) A report on recommendations for the most suitable financial contribution model for the Distributed System of Scientific Collections Research Infrastructure (DiSSCo-RI). Research Ideas and Outcomes 10: e117217. https://doi.org/10.3897/rio.10.e117217
Figure 8 Simulation of inflation, Model B.
Figure 7 from: Landel S, Lymer G, Pasterk M, Guiraud M, Worley K (2024) A report on recommendations for the most suitable financial contribution model for the Distributed System of Scientific Collections Research Infrastructure (DiSSCo-RI). Research Ideas and Outcomes 10: e117217. https://doi.org/10.3897/rio.10.e117217
Figure 7 Simulation of inflation, Model A.
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.