Transformer-based deep learning for predicting protein properties in the life sciences

A Chandra, L Tünnermann, T Löfstedt, R Gratz - Elife, 2023 - elifesciences.org
Recent developments in deep learning, coupled with an increasing number of sequenced
proteins, have led to a breakthrough in life science applications, in particular in protein …

Plant genome and transcriptome annotations: from misconceptions to simple solutions

ME Bolger, B Arsova, B Usadel - Briefings in bioinformatics, 2018 - academic.oup.com
Next-generation sequencing has triggered an explosion of available genomic and
transcriptomic resources in the plant sciences. Although genome and transcriptome …

Prediction and analysis of cell-penetrating peptides using pseudo-amino acid composition and random forest models

L Chen, C Chu, T Huang, X Kong, YD Cai - Amino acids, 2015 - Springer
Cell-penetrating peptides, a group of short peptides, can traverse cell membranes to enter
cells and thus facilitate the uptake of various molecular cargoes. Thus, they have the …

Analysis and review of techniques and tools based on machine learning and deep learning for prediction of lysine malonylation sites in protein sequences

S Ramazi, SAH Tabatabaei, E Khalili, AG Nia… - Database, 2024 - academic.oup.com
The post-translational modifications occur as crucial molecular regulatory mechanisms
utilized to regulate diverse cellular processes. Malonylation of proteins, a reversible post …

Computational methods and online resources for identification of piRNA-related molecules

Y Liu, A Li, G **e, G Liu, X Hei - Interdisciplinary sciences: computational …, 2021 - Springer
Abstract piRNAs are a class of small non-coding RNA molecules, which interact with the
PIWI family and have many important and diverse biological functions. The present review is …

An evaluation of deep neural network performance on limited protein phosphorylation site prediction data

FR Lumbanraja, B Mahesworo, TW Cenggoro… - Procedia Computer …, 2019 - Elsevier
One of the common and important post-translational modification (PTM) types is
phosphorylation. Protein phosphorylation is used to regulate various enzyme and receptor …

RF‐Phos: A novel general phosphorylation site prediction tool based on random Forest

HD Ismail, A Jones, JH Kim… - BioMed research …, 2016 - Wiley Online Library
Protein phosphorylation is one of the most widespread regulatory mechanisms in
eukaryotes. Over the past decade, phosphorylation site prediction has emerged as an …

Predicting protein phosphorylation sites in soybean using interpretable deep tabular learning network

E Khalili, S Ramazi, F Ghanati… - Briefings in …, 2022 - academic.oup.com
Phosphorylation of proteins is one of the most significant post-translational modifications
(PTMs) and plays a crucial role in plant functionality due to its impact on signaling, gene …

Prediction of protein kinase-specific phosphorylation sites in hierarchical structure using functional information and random forest

W Fan, X Xu, Y Shen, H Feng, A Li, M Wang - Amino acids, 2014 - Springer
Reversible protein phosphorylation is one of the most important post-translational
modifications, which regulates various biological cellular processes. Identification of the …

PARROT is a flexible recurrent neural network framework for analysis of large protein datasets

D Griffith, AS Holehouse - Elife, 2021 - elifesciences.org
The rise of high-throughput experiments has transformed how scientists approach biological
questions. The ubiquity of large-scale assays that can test thousands of samples in a day …