scholarly journals Extracting LncRNA-protein Interactions from Literature Using a Text Feature-based Approach

2015 ◽  
Vol 48 (28) ◽  
pp. 22-26
Author(s):  
Qiguang Zang ◽  
Dongdong Sun ◽  
Huanqing Feng ◽  
Ao Li
2016 ◽  
Vol 206 ◽  
pp. 73-80 ◽  
Author(s):  
Ao Li ◽  
Qiguang Zang ◽  
Dongdong Sun ◽  
Minghui Wang

Author(s):  
Vishal Sahu ◽  
Amit Kumar Mishra ◽  
Vivek Sharma ◽  
Ramakant Bhardwaj

Author(s):  
Ricardo Campos ◽  
Vítor Mangaravite ◽  
Arian Pasquali ◽  
Alípio Mário Jorge ◽  
Célia Nunes ◽  
...  

Cancers ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2779
Author(s):  
Ionut Dragomir ◽  
Adnan Akbar ◽  
John W. Cassidy ◽  
Nirmesh Patel ◽  
Harry W. Clifford ◽  
...  

Sporadic cancer develops from the accrual of somatic mutations. Out of all small-scale somatic aberrations in coding regions, 95% are base substitutions, with 90% being missense mutations. While multiple studies focused on the importance of this mutation type, a machine learning method based on the number of protein–protein interactions (PPIs) has not been fully explored. This study aims to develop an improved computational method for driver identification, validation and evaluation (DRIVE), which is compared to other methods for assessing its performance. DRIVE aims at distinguishing between driver and passenger mutations using a feature-based learning approach comprising two levels of biological classification for a pan-cancer assessment of somatic mutations. Gene-level features include the maximum number of protein–protein interactions, the biological process and the type of post-translational modifications (PTMs) while mutation-level features are based on pathogenicity scores. Multiple supervised classification algorithms were trained on Genomics Evidence Neoplasia Information Exchange (GENIE) project data and then tested on an independent dataset from The Cancer Genome Atlas (TCGA) study. Finally, the most powerful classifier using DRIVE was evaluated on a benchmark dataset, which showed a better overall performance compared to other state-of-the-art methodologies, however, considerable care must be taken due to the reduced size of the dataset. DRIVE outlines the outstanding potential that multiple levels of a feature-based learning model will play in the future of oncology-based precision medicine.


Author(s):  
S.B. Andrews ◽  
R.D. Leapman ◽  
P.E. Gallant ◽  
T.S. Reese

As part of a study on protein interactions involved in microtubule (MT)-based transport, we used the VG HB501 field-emission STEM to obtain low-dose dark-field mass maps of isolated, taxol-stabilized MTs and correlated these micrographs with detailed stereo images from replicas of the same MTs. This approach promises to be useful for determining how protein motors interact with MTs. MTs prepared from bovine and squid brain tubulin were purified and free from microtubule-associated proteins (MAPs). These MTs (0.1-1 mg/ml tubulin) were adsorbed to 3-nm evaporated carbon films supported over Formvar nets on 600-m copper grids. Following adsorption, the grids were washed twice in buffer and then in either distilled water or in isotonic or hypotonic ammonium acetate, blotted, and plunge-frozen in ethane/propane cryogen (ca. -185 C). After cryotransfer into the STEM, specimens were freeze-dried and recooled to ca.-160 C for low-dose (<3000 e/nm2) dark-field mapping. The molecular weights per unit length of MT were determined relative to tobacco mosaic virus standards from elastic scattering intensities. Parallel grids were freeze-dried and rotary shadowed with Pt/C at 14°.


2013 ◽  
Vol 54 ◽  
pp. 79-90 ◽  
Author(s):  
Saba Valadkhan ◽  
Lalith S. Gunawardane

Eukaryotic cells contain small, highly abundant, nuclear-localized non-coding RNAs [snRNAs (small nuclear RNAs)] which play important roles in splicing of introns from primary genomic transcripts. Through a combination of RNA–RNA and RNA–protein interactions, two of the snRNPs, U1 and U2, recognize the splice sites and the branch site of introns. A complex remodelling of RNA–RNA and protein-based interactions follows, resulting in the assembly of catalytically competent spliceosomes, in which the snRNAs and their bound proteins play central roles. This process involves formation of extensive base-pairing interactions between U2 and U6, U6 and the 5′ splice site, and U5 and the exonic sequences immediately adjacent to the 5′ and 3′ splice sites. Thus RNA–RNA interactions involving U2, U5 and U6 help position the reacting groups of the first and second steps of splicing. In addition, U6 is also thought to participate in formation of the spliceosomal active site. Furthermore, emerging evidence suggests additional roles for snRNAs in regulation of various aspects of RNA biogenesis, from transcription to polyadenylation and RNA stability. These snRNP-mediated regulatory roles probably serve to ensure the co-ordination of the different processes involved in biogenesis of RNAs and point to the central importance of snRNAs in eukaryotic gene expression.


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