Poetry, Activism, and Queer Indigenous Imaginative Landscapes: Conversations with Janice Gould

This essay offers a final interview with Koyoonk’auwi writer and scholar Janice Gould (1949-2019). Tatonetti first contextulaizes Gould's work and then presents their discussion in three sections: Questions on Seed (2019), Gould's latest poetry collection; Questions on California; and Questions on Queer Indigenous History.   &nbsp

Joyful Embodiment

This essay uses Dian Million's felt theory to read across the work of one of the earliest trans Indigenous people writing in English, arguing that Max Wolf Valeriorepresents his experiences of--and others’ reactions to--his sex and gender presentations as relational, highly affective processes across all of his texts. And, while affective knowledges exist widely across Indigenous texts and contexts, I turn in this special issue to how, when used to read Valerio’s essay and autobiography, felt theory reveals embodied ruptures and cultural dislocation/disavowal, or what Million terms “colonialism as a felt, affective relationship” (Therapeutic Nations 46). At the same time, this essay highlights the ways, in Valerio’s stories, felt knowledges offer a map of becoming and a lived route to survivance, healing, and joy

REVIEW ESSAY. Weaving the Present, Writing the Future: Benaway, Belcourt, and Whitehead's Queer Indigenous Imaginaries

Review Essay: Gwen Benaway, Billy-Ray Belcourt, and Joshua Whitehea

Predicting drug side-effects by chemical systems biology

Chemical systems biology approaches can explain unexpected observations of drug inefficacy or side-effects

3D Pharmacophoric Similarity improves Multi Adverse Drug Event Identification in Pharmacovigilance

Adverse drugs events (ADEs) detection constitutes a considerable concern in patient safety and public health care. For this reason, it is important to develop methods that improve ADE signal detection in pharmacovigilance databases. Our objective is to apply 3D pharmacophoric similarity models to enhance ADE recognition in Offsides, a pharmacovigilance resource with drug-ADE associations extracted from the FDA Adverse Event Reporting System (FAERS). We developed a multi-ADE predictor implementing 3D drug similarity based on a pharmacophoric approach, with an ADE reference standard extracted from the SIDER database. The results showed that the application of our 3D multi-type ADE predictor to the pharmacovigilance data in Offsides improved ADE identification and generated enriched sets of drug-ADE signals. The global ROC curve for the Offsides ADE candidates ranked with the 3D similarity score showed an area of 0.7. The 3D predictor also allows the identification of the most similar drug that causes the ADE under study, which could provide hypotheses about mechanisms of action and ADE etiology. Our method is useful in drug development, screening potential adverse effects in experimental drugs, and in drug safety, applicable to the evaluation of ADE signals selected through pharmacovigilance data mining

The Evolution of Diversity: Revising Student Learning Outcomes

Presentation and group discussion about the composition and revision of diversity-related student learning outcomes

Ten Simple Rules to Enable Multi-site Collaborations through Data Sharing

Open access, open data, and software are critical for advancing science and enabling collaboration across multiple institutions and throughout the world. Despite near universal recognition of its importance, major barriers still exist to sharing raw data, software, and research products throughout the scientific community. Many of these barriers vary by specialty [1], increasing the difficulties for interdisciplinary and/or translational researchers to engage in collaborative research. Multi-site collaborations are vital for increasing both the impact and the generalizability of research results. However, they often present unique data sharing challenges. We discuss enabling multi-site collaborations through enhanced data sharing in this set of Ten Simple Rules

Knowledge Graph Completion to Predict Polypharmacy Side Effects

The polypharmacy side effect prediction problem considers cases in which two drugs taken individually do not result in a particular side effect; however, when the two drugs are taken in combination, the side effect manifests. In this work, we demonstrate that multi-relational knowledge graph completion achieves state-of-the-art results on the polypharmacy side effect prediction problem. Empirical results show that our approach is particularly effective when the protein targets of the drugs are well-characterized. In contrast to prior work, our approach provides more interpretable predictions and hypotheses for wet lab validation.Comment: 13th International Conference on Data Integration in the Life Sciences (DILS2018

3D Pharmacophoric Similarity improves Multi Adverse Drug Event Identification in Pharmacovigilance

Adverse drugs events (ADEs) detection constitutes a considerable concern in patient safety and public health care. For this reason, it is important to develop methods that improve ADE signal detection in pharmacovigilance databases. Our objective is to apply 3D pharmacophoric similarity models to enhance ADE recognition in Offsides, a pharmacovigilance resource with drug-ADE associations extracted from the FDA Adverse Event Reporting System (FAERS). We developed a multi-ADE predictor implementing 3D drug similarity based on a pharmacophoric approach, with an ADE reference standard extracted from the SIDER database. The results showed that the application of our 3D multi-type ADE predictor to the pharmacovigilance data in Offsides improved ADE identification and generated enriched sets of drug-ADE signals. The global ROC curve for the Offsides ADE candidates ranked with the 3D similarity score showed an area of 0.7. The 3D predictor also allows the identification of the most similar drug that causes the ADE under study, which could provide hypotheses about mechanisms of action and ADE etiology. Our method is useful in drug development, screening potential adverse effects in experimental drugs, and in drug safety, applicable to the evaluation of ADE signals selected through pharmacovigilance data mining