scholarly journals High-throughput functional annotation and data mining with the Blast2GO suite

2008 ◽  
Vol 36 (10) ◽  
pp. 3420-3435 ◽  
Author(s):  
S. Gotz ◽  
J. M. Garcia-Gomez ◽  
J. Terol ◽  
T. D. Williams ◽  
S. H. Nagaraj ◽  
...  
2019 ◽  
Author(s):  
Mohammad Atif Faiz Afzal ◽  
Mojtaba Haghighatlari ◽  
Sai Prasad Ganesh ◽  
Chong Cheng ◽  
Johannes Hachmann

<div>We present a high-throughput computational study to identify novel polyimides (PIs) with exceptional refractive index (RI) values for use as optic or optoelectronic materials. Our study utilizes an RI prediction protocol based on a combination of first-principles and data modeling developed in previous work, which we employ on a large-scale PI candidate library generated with the ChemLG code. We deploy the virtual screening software ChemHTPS to automate the assessment of this extensive pool of PI structures in order to determine the performance potential of each candidate. This rapid and efficient approach yields a number of highly promising leads compounds. Using the data mining and machine learning program package ChemML, we analyze the top candidates with respect to prevalent structural features and feature combinations that distinguish them from less promising ones. In particular, we explore the utility of various strategies that introduce highly polarizable moieties into the PI backbone to increase its RI yield. The derived insights provide a foundation for rational and targeted design that goes beyond traditional trial-and-error searches.</div>


2004 ◽  
Vol 47 (25) ◽  
pp. 6373-6383 ◽  
Author(s):  
David J. Diller ◽  
Doug W. Hobbs

2004 ◽  
Vol 16 (1) ◽  
pp. 296-301 ◽  
Author(s):  
Dane Morgan ◽  
Gerbrand Ceder ◽  
Stefano Curtarolo

2019 ◽  
Vol 20 (6) ◽  
pp. 476-487 ◽  
Author(s):  
Brian L. Gudenas ◽  
Jun Wang ◽  
Shu-zhen Kuang ◽  
An-qi Wei ◽  
Steven B. Cogill ◽  
...  

Database ◽  
2018 ◽  
Vol 2018 ◽  
Author(s):  
Pengbo Wen ◽  
Junfeng Xia ◽  
Xianbin Cao ◽  
Bin Chen ◽  
Yinping Tao ◽  
...  

AbstractRadiotherapy is used to treat approximately 50% of all cancer patients, with varying prognoses. Intrinsic radiosensitivity is an important factor underlying the radiotherapeutic efficacy of this precise treatment. During the past decades, great efforts have been made to improve radiotherapy treatment through multiple strategies. However, invaluable data remains buried in the extensive radiotherapy literature, making it difficult to obtain an overall view of the detailed mechanisms leading to radiosensitivity, thus limiting advances in radiotherapy. To address this issue, we collected data from the relevant literature contained in the PubMed database and developed a literature-based database that we term the cancer radiosensitivity regulation factors database (dbCRSR). dbCRSR is a manually curated catalogue of radiosensitivity, containing multiple radiosensitivity regulation factors (395 coding genes, 119 non-coding RNAs and 306 chemical compounds) with appropriate annotation. To illustrate the value of the data we collected, data mining was performed including functional annotation and network analysis. In summary, dbCRSR is the first literature-based database to focus on radiosensitivity and provides a resource to better understand the detailed mechanisms of radiosensitivity. We anticipate dbCRSR will be a useful resource to enrich our knowledge and to promote further study of radiosensitivity.Database URL: http://bioinfo.ahu.edu.cn:8080/dbCRSR/


Genomics ◽  
2012 ◽  
Vol 99 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Takayuki Taniya ◽  
Susumu Tanaka ◽  
Yumi Yamaguchi-Kabata ◽  
Hideki Hanaoka ◽  
Chisato Yamasaki ◽  
...  

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