Graph Space Embedding
Authors: João Pereira, Albert K. Groen, Erik S. G. Stroes, Evgeni Levin
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In an empirical evaluation on a real-world clinical cohort containing patients with suspected coronary artery disease, the GSE achieves far better performance than traditional algorithms. and We benchmarked the GSE performance and running time when predicting ischaemia against the random-walk graph kernel, RBF, and random forests. ... The results are shown in table 1. |
| Researcher Affiliation | Collaboration | 1Amsterdam University Medical Center, The Netherlands 2Horaizon BV, The Netherlands |
| Pseudocode | Yes | Algorithm 1 Even Descent algorithm Input: f, x0, A Parameter: τ, λ, θa, b, cl, Mmin Output: X , f 1: i 0, fi f(x0), f [fi], converged False 2: E[||τ0||] = 0, X [x0] 3: while converged = True do 4: i i + 1 5: f Compute Partial Ders(x0, A, f) 6: τ0 τ/|N f| 7: a, b, c Updatep S(i, θa, Mmin, E[||τ0||], cl) 8: E[||τ0||] (E[||τ0||](i 1) + ||τ0||)/i 9: δ Even Sample(λ, a, b, c) 10: xi xi δ f 11: Append(f, f(xi)), Append(X , xi) 12: if |fi fi 1| < τ then 13: converged True 14: end if 15: end while 16: return X , f |
| Open Source Code | No | The paper states, 'We implemented the GSE and the random walk kernel in python and used sci-kit learn implementation [Pedregosa et al., 2011] for the other algorithms in the comparison.' However, it does not provide an explicit statement about the availability of their own source code or a link to a repository. |
| Open Datasets | Yes | For all our analysis, we used plasma protein levels of patients with suspected coronary artery disease who were diagnosed for the presence of ischaemia [Bom et al., 2018]. A total of 332 protein levels were measured using proximity extension arrays [Assarsson et al., 2014], and of the 196 patients, 108 were diagnosed with ischaemia. The protein-protein interactions data is available for download at String DB [Jensen et al., 2009]. |
| Dataset Splits | Yes | For this benchmark, we performed a 10-cycle stratified shuffle cross-validation split on the normalized protein data and recorded the average ROC area under the curve (AUC). and We then measured the ROC AUC on the validation set using 12 multiples of ν . |
| Hardware Specification | No | No specific hardware details (like GPU models, CPU types, or memory) used for running the experiments are provided in the paper. |
| Software Dependencies | No | The paper states 'We implemented the GSE and the random walk kernel in python and used sci-kit learn implementation [Pedregosa et al., 2011] for the other algorithms in the comparison.' However, it does not provide specific version numbers for Python or sci-kit learn, or any other key software dependencies. |
| Experiment Setup | Yes | To speed up the analysis, we used a training set of 90 pre-selected proteins using univariate feature selection with the F-statistic [Hira and Gillies, 2015]. and To test this, the ν = maxν σ2[k(ν)] was found using a gradient descent (ADAM [Kingma and Ba, 2015]) on the training set over 20 stratified shuffle splits (same preprocessing as in 3.2). |