Estimating the individuals' potential response to varying treatment doses is crucial for decision-making in areas such as precision medicine and management science. Most recent studies predict counterfactual outcomes by learning a covariate representation that is independent of the treatment variable. However, such independence constraints neglect much of the covariate information that is useful for counterfactual prediction, especially when the treatment variables are continuous. To tackle the above issue, in this paper, we first theoretically demonstrate the importance of the balancing and prognostic representations for unbiased estimation of the heterogeneous dose-response curves, that is, the learned representations are constrained to satisfy the conditional independence between the covariates and both of the treatment variables and the potential responses. Based on this, we propose a novel Contrastive balancing Representation learning Network using a partial distance measure, called CRNet, for estimating the heterogeneous dose-response curves without losing the continuity of treatments. Extensive experiments are conducted on synthetic and real-world datasets demonstrating that our proposal significantly outperforms previous methods.

After mutation, a gene either gains or loses a function, which is considered to be a critical piece of information for understanding a related pathology and thus for drug discovery. By classifying association of genes and phenotypes using the information of gain-of-function or loss-of-function, the exploration for drug repurposing can be guided in a much more efficient way. We present the active gene annotation corpus (AGAC), which contains 500 manually annotated abstracts collected from PubMed. Five bio-concept labels and three regulatory concept labels were designed for concept level annotation. Furthermore, two relation types, “Theme” and “Cause”, were designed to interlink regulatory concepts with their thematic and causal elements. We evaluated AGAC from three aspects, the results of which indicate the high quality of the annotation. Eventually, a PubMed-wide case study was performed to show that by using AGAC the process of anti-epilepsy drug discovery can be enhanced. After text retrieval, filtering, text classification and matching with DrugBank entries, 281 gene-drug pairs and 112 drugs were obtained and 30 out of 112 were recorded in databases. Among 10 newly predicted multi-target drugs which were not recorded in databases, 6 of them were found to be related to epilepsy with literature support, i.e., Oxazepam, Temazepam, Halazepam, Prazepam, Zolpidem and Thiamylal. The result of the case study support the potential of AGAC for enhancing knowledge discovery for drug repurposing.

In this work-in-progress paper, an innovative curriculum design process using participatory design strategy is presented. The principle of participatory design is to involve the end users (which are the students in our case) in every stage of the design process. Guided by the theory of situated learning, the faculty and student co-designers re-examined the Collaborative Project-based Learning (CPBL) model based on the evidence collected to identify effective pedagogical components to address the learning needs of under-represented minority students. In the course redesign process, the faculty and students worked together to improve the pedagogical methods based on cognitive apprenticeship and its four building blocks including content, method, sequence, and sociology. The redesigned course is currently being offered in Spring 2014, where both qualitative and quantitative data are collected to evaluate the effectiveness of the course redesign. We expect to share both the course re-design process and what we learn from the implementation of the redesigned course with the broad engineering education community during the conference.