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Esra Nalbat

Researcher at Middle East Technical University

Publications -  6
Citations -  174

Esra Nalbat is an academic researcher from Middle East Technical University. The author has contributed to research in topics: NoSQL & Cytotoxicity. The author has an hindex of 2, co-authored 5 publications receiving 62 citations. Previous affiliations of Esra Nalbat include Sabancı University.

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DEEPScreen: high performance drug-target interaction prediction with convolutional neural networks using 2-D structural compound representations.

TL;DR: The DEEPScreen system is composed of 704 target protein specific prediction models, each independently trained using experimental bioactivity measurements against many drug candidate small molecules, and optimized according to the binding properties of the target proteins.
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Treatment of breast cancer with autophagy inhibitory microRNAs carried by AGO2-conjugated nanoparticles

TL;DR: It is proposed that AGO2 protein conjugated SPIONs are a new class of theranostic nanoparticles and can be efficiently used as innovative, non-cationic,non-toxic gene therapy tools for targeted therapy of cancer.
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CROssBAR: comprehensive resource of biomedical relations with knowledge graph representations

TL;DR: CROssBAR as mentioned in this paper is a comprehensive system that integrates large-scale biological/biomedical data from various resources and stores them in a NoSQL database, enriched with the deep-learning-based prediction of relationships between numerous data entries, which is followed by the rigorous analysis of the enriched data to obtain biologically meaningful modules.
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Synthesis and biological evaluation of novel isoxazole-piperazine hybrids as potential anti-cancer agents with inhibitory effect on liver cancer stem cells.

TL;DR: In this paper, a series of isoxazole-piperazine analogues were prepared, and primarily screened for their antiproliferative potential against hepatocellular carcinoma (HCC; Huh7/Mahlavu) and breast (MCF-7) cancer cells.
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CROssBAR: Comprehensive Resource of Biomedical Relations with Deep Learning Applications and Knowledge Graph Representations

TL;DR: A comprehensive system that integrates large-scale biomedical data from various resources and store them in a new NoSQL database, enrich these data with deep-learning-based prediction of relations between numerous biomedical entities, rigorously analyse the enriched data to obtain biologically meaningful modules and display them to users via easy-to-interpret, interactive and heterogenous knowledge graph (KG) representations.