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6 results on '"Papoutsaki, Alexandra"'

1. Designing Flexible Longitudinal Regimens: Supporting Clinician Planning for Discontinuation of Psychiatric Drugs

Author

Jo, Eunkyung, Ryu, Myeonghan, Kenderova, Georgia, So, Samuel, Shapiro, Bryan, Papoutsaki, Alexandra, and Epstein, Daniel A

Subjects
Biomedical and Clinical Sciences, Health Services and Systems, Clinical Sciences, Health Sciences, Psychology, Clinical Research, Clinical decision support systems, Planning, Psychiatric drugs, Antidepressants, Building, Business and Management, Strategy, management and organisational behaviour
Abstract

Clinical decision support tools have typically focused on one-time support for diagnosis or prognosis, but have the ability to support providers in longitudinal planning of patient care regimens amidst infrastructural challenges. We explore an opportunity for technology support for discontinuing antidepressants, where clinical guidelines increasingly recommend gradual discontinuation over abruptly stopping to avoid withdrawal symptoms, but providers have varying levels of experience and diverse strategies for supporting patients through discontinuation. We conducted two studies with 12 providers, identifying providers' needs in developing discontinuation plans and deriving design guidelines. We then iteratively designed and implemented AT Planner, instantiating the guidelines by projecting taper schedules and providing flexibility for adjustment. Provider feedback on AT Planner highlighted that discontinuation plans required balancing interpersonal and infrastructural constraints and surfaced the need for different technological support based on clinical experience. We discuss the benefits and challenges of incorporating flexibility and advice into clinical planning tools.

Published
2022

2. Readability Research: An Interdisciplinary Approach

Author

Beier, Sofie, Berlow, Sam, Boucaud, Esat, Bylinskii, Zoya, Cai, Tianyuan, Cohn, Jenae, Crowley, Kathy, Day, Stephanie L., Dingler, Tilman, Dobres, Jonathan, Healey, Jennifer, Jain, Rajiv, Jordan, Marjorie, Kerr, Bernard, Li, Qisheng, Miller, Dave B., Nobles, Susanne, Papoutsaki, Alexandra, Qian, Jing, Rezvanian, Tina, Rodrigo, Shelley, Sawyer, Ben D., Sheppard, Shannon M., Stein, Bram, Treitman, Rick, Vanek, Jen, Wallace, Shaun, and Wolfe, Benjamin

Subjects
Computer Science - Human-Computer Interaction, Computer Science - Computers and Society
Abstract

Readability is on the cusp of a revolution. Fixed text is becoming fluid as a proliferation of digital reading devices rewrite what a document can do. As past constraints make way for more flexible opportunities, there is great need to understand how reading formats can be tuned to the situation and the individual. We aim to provide a firm foundation for readability research, a comprehensive framework for modern, multi-disciplinary readability research. Readability refers to aspects of visual information design which impact information flow from the page to the reader. Readability can be enhanced by changes to the set of typographical characteristics of a text. These aspects can be modified on-demand, instantly improving the ease with which a reader can process and derive meaning from text. We call on a multi-disciplinary research community to take up these challenges to elevate reading outcomes and provide the tools to do so effectively., Comment: This paper was generated collaboratively over the course of a series of online workshops, the results of which were extensively edited by Dr. Zoya Bylinskii, Dr. Ben D. Sawyer, and Dr. Benjamin Wolfe. Original illustrations by Bernard Kerr. Corresponding Author: Dr. Ben D. Sawyer

Published
2021

3. Accurate Computation of Survival Statistics in Genome-wide Studies

Author

Vandin, Fabio, Papoutsaki, Alexandra, Raphael, Benjamin J., and Upfal, Eli

Subjects
Quantitative Biology - Quantitative Methods, Statistics - Applications
Abstract

A key challenge in genomics is to identify genetic variants that distinguish patients with different survival time following diagnosis or treatment. While the log-rank test is widely used for this purpose, nearly all implementations of the log-rank test rely on an asymptotic approximation that is not appropriate in many genomics applications. This is because: the two populations determined by a genetic variant may have very different sizes; and the evaluation of many possible variants demands highly accurate computation of very small p-values. We demonstrate this problem for cancer genomics data where the standard log-rank test leads to many false positive associations between somatic mutations and survival time. We develop and analyze a novel algorithm, Exact Log-rank Test (ExaLT), that accurately computes the p-value of the log-rank statistic under an exact distribution that is appropriate for any size populations. We demonstrate the advantages of ExaLT on data from published cancer genomics studies, finding significant differences from the reported p-values. We analyze somatic mutations in six cancer types from The Cancer Genome Atlas (TCGA), finding mutations with known association to survival as well as several novel associations. In contrast, standard implementations of the log-rank test report dozens-hundreds of likely false positive associations as more significant than these known associations., Comment: Full version of RECOMB 2013 paper

Published
2013

4. Readability Research: An Interdisciplinary Approach

Author

Beier, Sofie, primary, Berlow, Sam, primary, Boucaud, Esat, primary, Bylinskii, Zoya, primary, Cai, Tianyuan, primary, Cohn, Jenae, primary, Crowley, Kathy, primary, Day, Stephanie L., primary, Dingler, Tilman, primary, Dobres, Jonathan, primary, Healey, Jennifer, primary, Jain, Rajiv, primary, Jordan, Marjorie, primary, Kerr, Bernard, primary, Li, Qisheng, primary, Miller, Dave B., primary, Nobles, Susanne, primary, Papoutsaki, Alexandra, primary, Qian, Jing, primary, Rezvanian, Tina, primary, Rodrigo, Shelley, primary, Sawyer, Ben D., primary, Sheppard, Shannon M., primary, Stein, Bram, primary, Treitman, Rick, primary, Vanek, Jen, primary, Wallace, Shaun, primary, and Wolfe, Benjamin, primary

Published
2022
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6. Pan-Cancer Network Analysis Identifies Combinations of Rare Somatic Mutations across Pathways and Protein Complexes

Author

Leiserson, Mark D.M., Vandin, Fabio, Wu, Hsin-Ta, Dobson, Jason R., Eldridge, Jonathan V., Thomas, Jacob L., Papoutsaki, Alexandra, Kim, Younhun, Niu, Beifang, McLellan, Michael, Lawrence, Michael S., Gonzalez-Perez, Abel, Tamborero, David, Cheng, Yuwei, Ryslik, Gregory A., Lopez-Bigas, Nuria, Getz, Gad, Ding, Li, and Raphael, Benjamin J.

Abstract

Cancers exhibit extensive mutational heterogeneity and the resulting long tail phenomenon complicates the discovery of the genes and pathways that are significantly mutated in cancer. We perform a Pan-Cancer analysis of mutated networks in 3281 samples from 12 cancer types from The Cancer Genome Atlas (TCGA) using HotNet2, a novel algorithm to find mutated subnetworks that overcomes limitations of existing single gene and pathway/network approaches.. We identify 14 significantly mutated subnetworks that include well-known cancer signaling pathways as well as subnetworks with less characterized roles in cancer including cohesin, condensin, and others. Many of these subnetworks exhibit co-occurring mutations across samples. These subnetworks contain dozens of genes with rare somatic mutations across multiple cancers; many of these genes have additional evidence supporting a role in cancer. By illuminating these rare combinations of mutations, Pan-Cancer network analyses provide a roadmap to investigate new diagnostic and therapeutic opportunities across cancer types.

Published
2014
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