Researcher:
Safadoust, Sadra

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Sadra

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Safadoust

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Safadoust, Sadra

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20212

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Researcher Of Publications: search.filters.isAuthorOfPublication.a429889b-d389-47c9-bd81-8e9b3df83703

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    NetControl4BioMed: a web-based platform for controllability analysis of protein-protein interaction networks
    (Oxford University Press (OUP), 2021) Popescu, Victor-Bogdan; Ángel Sánchez-Martín, José; Schacherer, Daniela; Majidi, Negin; Andronescu, Andrei; Nedea, Alexandru; Ion, Diana; Mititelu, Eduard; Czeizler, Eugen; Petre, Ion; N/A; Safadoust, Sadra; PhD Student; Graduate School of Sciences and Engineering; N/A
    Motivation: There is an increasing amount of data coming from genome-wide studies identifying disease-specific survivability-essential proteins and host factors critical to a cell becoming infected. Targeting such proteins has a strong potential for targeted, precision therapies. Typically however, too few of them are drug targetable. An alternative approach is to influence them through drug targetable proteins upstream of them. Structural target network controllability is a suitable solution to this problem. It aims to discover suitable source nodes (e.g. drug targetable proteins) in a directed interaction network that can control (through a suitable set of input functions) a desired set of targets. Results: We introduce NetControl4BioMed, a free open-source web-based application that allows users to generate or upload directed protein-protein interaction networks and to perform target structural network controllability analyses on them. The analyses can be customized to focus the search on drug targetable source nodes, thus providing drug therapeutic suggestions. The application integrates protein data from HGNC, Ensemble, UniProt, NCBI and InnateDB, directed interaction data from InnateDB, Omnipath and SIGNOR, cell-line data from COLT and DepMap, and drug-target data from DrugBank.
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    PublicationOpen Access
    Self-supervised monocular scene decomposition and depth estimation
    (IEEE Computer Society, 2021) Department of Computer Engineering; N/A; Güney, Fatma; Safadoust, Sadra; Department of Computer Engineering; Koç Üniversitesi İş Bankası Yapay Zeka Uygulama ve Araştırma Merkezi (KUIS AI)/ Koç University İş Bank Artificial Intelligence Center (KUIS AI); College of Engineering; Graduate School of Sciences and Engineering; 187939; N/A
    Self-supervised monocular depth estimation approaches either ignore independently moving objects in the scene or need a separate segmentation step to identify them. We propose MonoDepthSeg to jointly estimate depth and segment moving objects from monocular video without using any ground-truth labels. We decompose the scene into a fixed number of components where each component corresponds to a region on the image with its own transformation matrix representing its motion. We estimate both the mask and the motion of each component efficiently with a shared encoder. We evaluate our method on three driving datasets and show that our model clearly improves depth estimation while decomposing the scene into separately moving components.