Learning Multi-Modal Biomarker Representations via Globally Aligned Longitudinal Enrichments
Authors: Lyujian Lu, Saad Elbeleidy, Lauren Zoe Baker, Hua Wang817-824
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We have conducted extensive experiments on the Alzheimer s Disease Neuroimaging Initiative (ADNI) data using one genotypic and two phenotypic biomarkers. Empirical results have demonstrated that the learned enriched biomarker representations are more effective in predicting the outcomes of various cognitive assessments. We have performed extensive experiments on the Alzheimer s Disease Neuroimaging Initiative (ADNI) dataset (Weiner et al. 2010) and achieved obvious prediction capability gains by using the newly learned fixed-length representation compared to baseline biomarkers. |
| Researcher Affiliation | Academia | Department of Computer Science, Colorado School of Mines, Golden, CO 80401 {lyujianlu, selbeleidy, laurenzoebaker}@mymail.mines.edu huawangcs@gmail.com |
| Pseudocode | Yes | Algorithm 1: (Liu et al. 2017) The algorithm to solve the problem (6). Algorithm 2: The ADMM algorithm. Algorithm 3: Solve the optimization problem in Eq. (13). |
| Open Source Code | No | The paper does not contain any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | Data used in the preparation of the experiments were obtained from the ADNI database (Weiner et al. 2010). We download 1.5 T MRI scans and demographic information for 821 ADNI-1 participants. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. |
| Dataset Splits | Yes | For RR, Lasso and SVR models, we conduct a standard 5-fold cross-validation approach and compute the root mean square error (RMSE) between the predicted values and ground truth values of the cognitive scores on the testing data. |
| Hardware Specification | No | The paper does not specify the hardware (e.g., specific GPU/CPU models, memory details) used for running the experiments. It does not mention any particular processor, GPU, or computing cluster specifications. |
| Software Dependencies | No | The paper mentions the use of 'ridge regression (RR), Lasso, support vector regression (SVR), and convolutional neural networks (CNN)' but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | For the model parameters, reduced dimension r is studied in {40, 60, . . . , 180, 200} and γ1,γ2 γ3 are fine tuned by searching the grid of {10 5, . . . , 10 1, 1, 10, , 105}. The reduced dimension r is set to 60. |