Author

Dušica Vidović

University of MiamiORCID 0000-0001-9798-2108Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Assay:bulk RNA-seq· 3 Research Focus:database / resource· 3 Approach:experimental assay· 3 Approach:statistical model· 3 Research Focus:benchmarking· 2 Research Focus:differential expression· 2 Organism:human· 2 Assay:proteomics· 2

Frequent Co-Authors

Stephan C. SchüreriD· 6 shared Avi Ma’ayaniD· 3 shared Ajay PillaiiD· 2 shared Amar Koleti· 2 shared Behrouz ShamsaeiiD· 2 shared Daniel J. B. ClarkeiD· 2 shared Jacob D. JaffeiD· 2 shared Jarek MelleriD· 2 shared

Activity by Year

2017-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 39 mutual co-author links

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6 shared papersBioinformatics

Stephan C. SchürerORCID

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6 papers

Recent·Most cited

Interactive Data Integrator for Uniform Data Processing in the MorPhiC Program

P.S. McKeever, Jesse Flores, Dylan S. Baker, Ling‐Hong Hung, Bryce Nobutoshi Fukuda, Orges Koci, Anu Shivalikanjli, Dušica Vidović, Helen Parkinson, Stephan C. Schürer, Ka Yee Yeung

The paper introduces an interactive Data Integrator for uniform processing and integration of bulk RNA-seq datasets from the MorPhiC program and external sources.

bioRxiv · Oct 19, 2026 · PreprintBioinformatics

Multiscale mapping of transcriptomic signatures for cardiotoxic drugs

Jens Hansen, Yuguang Xiong, Mustafa M. Siddiq, Priyanka Dhanan, Bin Hu, Bhavana Shewale, Arjun Singh Yadaw, Gomathi Jayaraman, Rosa E. Tolentino, Yibang Chen, Pedro Martinez, Kristin G. Beaumont, Robert Sebra, Dušica Vidović, Stephan C. Schürer, , Ravi Iyengar

The paper identifies cardiotoxic drug signatures using bulk transcriptomics and integrates them with genomic and single-cell data to predict toxicity mechanisms.

Nature Communications · Sep 11, 2024Peer-reviewedSystems Biology

A LINCS microenvironment perturbation resource for integrative assessment of ligand-mediated molecular and phenotypic responses

Sean M. Gross, Mark A. Dane, Rebecca Lee Smith, Kaylyn L. Devlin, Ian C. McLean, Daniel S. Derrick, Caitlin E. Mills, Kartik Subramanian, Alexandra B. London, Denis Torre, John Erol Evangelista, Daniel J. B. Clarke, Zhuorui Xie, Cemal Erdem, Nicholas Lyons, , Laura M. Heiser

The paper presents a LINCS Microenvironment perturbation dataset cataloging transcriptional, proteomic, epigenomic, and phenotypic responses of MCF10A cells to six ligands, enabling integrative analysis of molecular and phenotypic changes.

Communications Biology · Oct 7, 2022Peer-reviewedSystems Biology

Connecting omics signatures of diseases, drugs, and mechanisms of actions with iLINCS

Marcin Pilarczyk, Mehdi Fazel‐Najafabadi, Michal Kouril, Behrouz Shamsaei, Juozas Vasiliauskas, Wen Niu, Naim Al Mahi, Lixia Zhang, Nicholas A. Clark, Yan Ren, Shana White, Rashid Saadman Karim, Huan Xu, Jacek Biesiada, Mark F. Bennett, , Mario Medvedovic

iLINCS provides a web-based platform for analyzing omics signatures of diseases and cellular perturbations through integrative analytics and visualization tools.

Nature Communications · Aug 9, 2022Peer-reviewedBioinformatics

piNET: a versatile web platform for downstream analysis and visualization of proteomics data

Behrouz Shamsaei, Szymon Chojnacki, Marcin Pilarczyk, Mehdi Fazel Najafabadi, Wen Niu, Chuming Chen, Karen E. ROSS, Andrea D. Matlock, Jeremy L. Muhlich, Somchai Chutipongtanate, Jie Zheng, John Turner, Dušica Vidović, Jacob D. Jaffe, Michael J. MacCoss, , Jarek Meller

piNET is a web platform for analyzing and visualizing proteomics data, including PTMs, by connecting them to biological knowledge and LINCS signatures.

Nucleic Acids Research · May 27, 2020Peer-reviewedBioinformatics

Drug Target Ontology to Classify and Integrate Drug Discovery Data

Yu Lin, Saurabh Mehta, Hande Küçük‐McGinty, John Paul Turner, Dušica Vidović, Michele Forlin, Amar Koleti, Ðắc-Trung Nguyễn, Lars Juhl Jensen, Rajarshi Guha, Stephen L. Mathias, Oleg Ursu, Vasileios Stathias, Jianbin Duan, Nooshin Nabizadeh, , Stephan C. Schürer

The paper introduces the Drug Target Ontology (DTO) to classify and integrate drug discovery data by formalizing druggable protein targets and their characteristics.

Journal of Biomedical Semantics · Nov 9, 2017Peer-reviewedBioinformatics