Informacja

Drogi użytkowniku, aplikacja do prawidłowego działania wymaga obsługi JavaScript. Proszę włącz obsługę JavaScript w Twojej przeglądarce.

Wyszukujesz frazę "Modi, M.K." wg kryterium: Autor


Wyświetlanie 1-2 z 2
Tytuł:
In silico prediction and characterization of three-dimensional structure of actin-1 of Arabidopsis thaliana
Autorzy:
Sahu, M.
Dehury, B.
Sarmah, R.
Sahoo, S.
Sahu, J.
Sarma, K.
Sen, P.
Modi, M.K.
Barooah, M.
Tematy:
actin-1
protein sequence
Arabidopsis thaliana
comparative modelling
three-dimensional structure
molecular dynamics simulation
Pokaż więcej
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Powiązania:
https://bibliotekanauki.pl/articles/80321.pdf  Link otwiera się w nowym oknie
Opis:
Actin-1 is a ubiquitous protein belonging to the reproductive class of Actin family in Arabidopsis thaliana . This protein is involved in the formation of filaments that are major components of the cytoskeleton. Despite the importance of this protein, very little information is available regarding its structure and function in plants. In this study, analysis of the protein sequence was done and comparative model of Actin-1 was constructed (UNIPROT ID: P0CJ46) from Arabidopsis thaliana using the crystal structure of Dictyostelium discoideum actin (PDB ID: 1NLV-A) as template employing Modeller version 9.9. The stable structure was generated by 5 nanosecond molecular dynamics simulation steps using GROMOS43A1 96 force field that characterized its structural and dynamic feature. The biochemical function of the final simulated structure was also investigated using PROFUNC. The molecular simulation study suggested that the modeled Actin-1 protein retain its stable conformation in aqueous solution. The predicted binding sites in the modeled Actin-1 protein are very informative for further protein-ligand interaction study.
Dostawca treści:
Biblioteka Nauki
Artykuł
Autorzy:
Prasad Rai, Bhavan
Zahid Raza, Syed
Patil, Vathsala
Hameed, B.M. Zeeshan
Karimi, Hadis
Vigneswaran, Ganesh
Somani, Bhaskar K.
Chłosta, Piotr
Modi, Sachin
Prerepa, Gayathri
Naik, Nithesh
Shekhar, Pranav
Paul, Rahul
Opis:
Over the years, many clinical and engineering methods have been adapted for testing and screening for the presence of diseases. The most commonly used methods for diagnosis and analysis are computed tomography (CT) and X-ray imaging. Manual interpretation of these images is the current gold standard but can be subject to human error, is tedious, and is time-consuming. To improve efficiency and productivity, incorporating machine learning (ML) and deep learning (DL) algorithms could expedite the process. This article aims to review the role of artificial intelligence (AI) and its contribution to data science as well as various learning algorithms in radiology. We will analyze and explore the potential applications in image interpretation and radiological advances for AI. Furthermore, we will discuss the usage, methodology implemented, future of these concepts in radiology, and their limitations and challenges.
Dostawca treści:
Repozytorium Uniwersytetu Jagiellońskiego
Artykuł
    Wyświetlanie 1-2 z 2

    Ta witryna wykorzystuje pliki cookies do przechowywania informacji na Twoim komputerze. Pliki cookies stosujemy w celu świadczenia usług na najwyższym poziomie, w tym w sposób dostosowany do indywidualnych potrzeb. Korzystanie z witryny bez zmiany ustawień dotyczących cookies oznacza, że będą one zamieszczane w Twoim komputerze. W każdym momencie możesz dokonać zmiany ustawień dotyczących cookies