Drosophila Gene Expression Pattern Annotations via Multi-Instance Biological Relevance Learning
Authors: Hua Wang, Cheng Deng, Hao Zhang, Xinbo Gao, Heng Huang
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments have demonstrated the effectiveness of our new method. In this section, we will conduct experiments to evaluate the proposed method empirically on Drosophila gene expression data and compare it with other state-of-art classification methods for both stage classification and anatomical term annotation. |
| Researcher Affiliation | Academia | Department of Electrical Engineering and Computer Science, Colorado School of Mines, Golden, CO 80401, USA Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX 76019, USA School of Electronic Engineering, Xidian University, Xi an, Shaanxi 710071, P. R. China |
| Pseudocode | Yes | Algorithm 1: The algorithm to solve the problem (11). t = 1. Initialize vt C ; while not converge do 1. Calculate λt = f(vt) g(vt) ; 2. Calculate vt+1 = arg minv C f(v) λtg(v) ; 3. t = t + 1 ; |
| Open Source Code | No | The paper mentions external resources like 'published codes posted on the corresponding author s web sites' for competing methods and a database URL (fruitfly.org), but it does not provide an explicit statement or link indicating that the source code for their own methodology is publicly available. |
| Open Datasets | Yes | all the images from the Berkeley Drosophila Genome Project (BDGP) database1 have been pre-processed, including alignment and resizing to 128 × 320 gray images. For the sake of simplicity, we extract the popular SIFT (Lowe 2004) features from the regular patches with the radius as well as the spacing as 16 pixels (Shuiwang et al. 2009). 1http://www.fruitfly.org/ |
| Dataset Splits | No | The paper mentions 'We use the optimal parameter values for C and γ obtained from cross-validation for SVM.' but does not provide specific details on the dataset splits (e.g., percentages, sample counts, or explicit k-fold values) for training, validation, and testing. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'LIBSVM' and extracting 'SIFT' features, but it does not specify the version numbers for these or any other ancillary software components needed to replicate the experiment. |
| Experiment Setup | No | The paper states 'We use the optimal parameter values for C and γ obtained from cross-validation for SVM.' but does not provide the concrete values of these or any other hyperparameters (e.g., learning rate, batch size, number of epochs) or system-level training settings. |