K
Khalimat Murtazalieva
Researcher at Moscow Institute of Physics and Technology
Publications - 5
Citations - 91
Khalimat Murtazalieva is an academic researcher from Moscow Institute of Physics and Technology. The author has contributed to research in topics: Source code & Genome project. The author has an hindex of 3, co-authored 5 publications receiving 58 citations. Previous affiliations of Khalimat Murtazalieva include Russian National Research Medical University.
Papers
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Journal ArticleDOI
How good are publicly available web services that predict bioactivity profiles for drug repurposing
Khalimat Murtazalieva,Dmitry S. Druzhilovskiy,Rajesh Kumar Goel,G N Sastry,Vladimir Poroikov +4 more
TL;DR: It is demonstrated that the performance of machine learning methods surpassed those of chemical similarity assessments, particularly in the case of novel repurposed indications.
Journal ArticleDOI
Computational platform Way2Drug: from the prediction of biological activity to drug repurposing
Dmitry S. Druzhilovskiy,A. V. Rudik,Dmitry Filimonov,Tatyana A. Gloriozova,Alexey Lagunin,Alexander V. Dmitriev,Pavel V. Pogodin,V. I. Dubovskaya,Sergey Ivanov,Olga Tarasova,V. M. Bezhentsev,Khalimat Murtazalieva,M. I. Semin,I. S. Maiorov,Anamika Singh Gaur,Garikapati Narahari Sastry,Vladimir Poroikov +16 more
TL;DR: The Way2Drug informational-computational platform provides access to the data on drugs approved for medicinal use in the USA and Russian Federation, as well as computational possibilities for the prediction of biological activity of drug-like organic compounds.
Journal ArticleDOI
TransPrise: a novel machine learning approach for eukaryotic promoter prediction.
Stepan Pachganov,Khalimat Murtazalieva,Aleksei Zarubin,Dmitry Sokolov,Duane R. Chartier,Tatiana V. Tatarinova +5 more
TL;DR: This paper compared predictions of TransPrise classification and regression models with the TSSPlant approach for the well annotated genome of Oryza sativa to demonstrate significant improvement over existing promoter-prediction methods.
Journal ArticleDOI
CFM: a database of experimentally validated protocols for chemical compound-based direct reprogramming and transdifferentiation
Alexey Sizykh,Khalimat Murtazalieva,Yulia Vyshkvorkina,Alexey Stupnikov,Yulia A. Medvedeva,Yulia A. Medvedeva +5 more
TL;DR: CFM (cell fate mastering), a database of experimentally validated protocols for chemical compound-based direct reprogramming and direct cell conversion, which allows stem cell biologists to compare and choose the best protocol with high efficiency and reliability for their needs.
Book ChapterDOI
Prediction of Rice Transcription Start Sites Using TransPrise: A Novel Machine Learning Approach.
Stepan Pachganov,Khalimat Murtazalieva,Alexei Zarubin,Tatiana Taran,Duane R. Chartier,Tatiana V. Tatarinova +5 more
TL;DR: TransPrise as mentioned in this paper is an efficient deep learning tool for predicting positions of eukaryotic transcription start sites, which offers significant improvement over existing promoter-prediction methods and has been shown to be able to predict TSS in Oryza sativa.