We propose Adversarial Inductive Transfer Learning (AITL), a method for addressing discrepancies in input and output spaces between source and target domains. AITL utilizes adversarial domain adaptation and multi-task learning to address these discrepancies. Our motivating application is pharmacogenomics where the goal is to predict drug response in patients using their genomic information. The challenge is that clinical data (i.e. patients) with drug response outcome is very limited, creating a need for transfer learning to bridge the gap between large pre-clinical pharmacogenomics datasets (e.g. cancer cell lines) and clinical datasets. Discrepancies exist between 1) the genomic data of pre-clinical and clinical datasets (the input space), and 2) the different measures of the drug response (the output space). To the best of our knowledge, AITL is the first adversarial inductive transfer learning method to address both input and output discrepancies. Experimental results indicate that AITL outperforms state-of-the-art pharmacogenomics and transfer learning baselines and may guide precision oncology more accurately.
The paper proposes an adversarial transfer learning network that can handle the adaptation of both the input space and the output space. The paper is motivated by the application of drug response prediction where the source domain is cell line data and the target domain is patient data. Patient data are usually scarce, hence motivating transferring the knowledge learned from the more widely available cell line data to improve the predictive performance based on the patient data.  The idea of making use of adversarial networks is to learn a representation of the data points that is invariant to whether the data points come from the source domain or the target domain. Experiments on real-world data over four drugs demonstrate the effectiveness of the proposed methods compared to other methods that are not specifically designed for this scenario.
The major utility of the proposed method demonstrated by the paper is its empirical effectiveness on real-world data with four drugs. The reviewer has the following concerns:
1. While the proposed method is capable of handling adaption of the output space between the source domain and the target domain, it makes use of a multi-task subnetwork component, which is a sensible modeling choice but it seems that the idea of leveraging a global discriminator and a class-wise discriminator has been exploited in previous works, as pointed out by the authors in Section 4.2. Therefore, the reviewer is concerned about the modeling contribution made in this paper is somewhat incremental given existing literature.
2. The multi-task subnetwork component itself also seems to be a straightforward and naive way to deal with the different tasks addressed in the source domain and the target domain. For one thing, only the data of the source domain are used to optimize the loss in the source domain and only the data of the target domain are used to optimize the loss in the target domain. While it is understandable that such a decision is due to the lack of ground truth of binary label in the source domain and numeric response in the target domain, ideally, the discriminative process might gain further benefits from borrowing information across the two domains.
3. It is also unclear from the paper how each component of the architecture contributes to the final performance. An ablation study that gets rid of the multi-task subnetwork, global discriminator, and class-wise discriminators (and potential combinations of these components) could help to provide better insights in the importance of each component and determine whether empirically these components do play a role coinciding with the description of the paper.
Other issues/questions:
1. Intuitively, what is the purpose of the shared layer g() in section 3.2.2?
2. In the spirit of the class-wise discriminators, will it be helpful to also add a discriminator based on the value of IC50?
3. Concepts and terminology are not well explained. e.g. what is a cell line? The authors should also provide further descriptions of what do the cell line and patient datasets look like early on in the paper. It was not until section 3 it is clear to the readers that in the dataseets consider in this paper, the source domain and the target domain have the same raw feature representation. Furthermore, in section 2, the authors could consider providing a taxonomy in table format to better explain different types of transfer learning and the three approaches to inductive transfer learning.