Biological sequence classification: A review on data and general methods
C Ao, S Jiao, Y Wang, L Yu, Q Zou - Research, 2022 - spj.science.org
With the rapid development of biotechnology, the number of biological sequences has
grown exponentially. The continuous expansion of biological sequence data promotes the …
grown exponentially. The continuous expansion of biological sequence data promotes the …
Machine intelligence in peptide therapeutics: A next‐generation tool for rapid disease screening
S Basith, B Manavalan, T Hwan Shin… - Medicinal research …, 2020 - Wiley Online Library
Discovery and development of biopeptides are time‐consuming, laborious, and dependent
on various factors. Data‐driven computational methods, especially machine learning (ML) …
on various factors. Data‐driven computational methods, especially machine learning (ML) …
SBSM-pro: support bio-sequence machine for proteins
Proteins play a pivotal role in biological systems. The use of machine learning algorithms for
protein classification can assist and even guide biological experiments, offering crucial …
protein classification can assist and even guide biological experiments, offering crucial …
MathFeature: feature extraction package for DNA, RNA and protein sequences based on mathematical descriptors
One of the main challenges in applying machine learning algorithms to biological sequence
data is how to numerically represent a sequence in a numeric input vector. Feature …
data is how to numerically represent a sequence in a numeric input vector. Feature …
iFeatureOmega: an integrative platform for engineering, visualization and analysis of features from molecular sequences, structural and ligand data sets
The rapid accumulation of molecular data motivates development of innovative approaches
to computationally characterize sequences, structures and functions of biological and …
to computationally characterize sequences, structures and functions of biological and …
Machine learning approach to gene essentiality prediction: a review
O Aromolaran, D Aromolaran, I Isewon… - Briefings in …, 2021 - academic.oup.com
Essential genes are critical for the growth and survival of any organism. The machine
learning approach complements the experimental methods to minimize the resources …
learning approach complements the experimental methods to minimize the resources …
TACOS: a novel approach for accurate prediction of cell-specific long noncoding RNAs subcellular localization
Long noncoding RNAs (lncRNAs) are primarily regulated by their cellular localization, which
is responsible for their molecular functions, including cell cycle regulation and genome …
is responsible for their molecular functions, including cell cycle regulation and genome …
RF-PseU: a random forest predictor for RNA pseudouridine sites
One of the ubiquitous chemical modifications in RNA, pseudouridine modification is crucial
for various cellular biological and physiological processes. To gain more insight into the …
for various cellular biological and physiological processes. To gain more insight into the …
ACP-MHCNN: an accurate multi-headed deep-convolutional neural network to predict anticancer peptides
Although advancing the therapeutic alternatives for treating deadly cancers has gained
much attention globally, still the primary methods such as chemotherapy have significant …
much attention globally, still the primary methods such as chemotherapy have significant …
Finding lncRNA-protein interactions based on deep learning with dual-net neural architecture
L Peng, C Wang, X Tian, L Zhou… - IEEE/ACM transactions on …, 2021 - ieeexplore.ieee.org
The identification of lncRNA-protein interactions (LPIs) is important to understand the
biological functions and molecular mechanisms of lncRNAs. However, most computational …
biological functions and molecular mechanisms of lncRNAs. However, most computational …