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 …
A comprehensive review of the imbalance classification of protein post-translational modifications
Post-translational modifications (PTMs) play significant roles in regulating protein structure,
activity and function, and they are closely involved in various pathologies. Therefore, the …
activity and function, and they are closely involved in various pathologies. Therefore, 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 …
LPI-deepGBDT: a multiple-layer deep framework based on gradient boosting decision trees for lncRNA–protein interaction identification
L Zhou, Z Wang, X Tian, L Peng - BMC bioinformatics, 2021 - Springer
Abstract Background Long noncoding RNAs (lncRNAs) play important roles in various
biological and pathological processes. Discovery of lncRNA–protein interactions (LPIs) …
biological and pathological processes. Discovery of lncRNA–protein interactions (LPIs) …
iTTCA-RF: a random forest predictor for tumor T cell antigens
S Jiao, Q Zou, H Guo, L Shi - Journal of translational medicine, 2021 - Springer
Background Cancer is one of the most serious diseases threatening human health. Cancer
immunotherapy represents the most promising treatment strategy due to its high efficacy and …
immunotherapy represents the most promising treatment strategy due to its high efficacy and …
Feature extraction approaches for biological sequences: a comparative study of mathematical features
RP Bonidia, LDH Sampaio… - Briefings in …, 2021 - academic.oup.com
As consequence of the various genomic sequencing projects, an increasing volume of
biological sequence data is being produced. Although machine learning algorithms have …
biological sequence data is being produced. Although machine learning algorithms have …
Revolutionizing enzyme engineering through artificial intelligence and machine learning
The combinatorial space of an enzyme sequence has astronomical possibilities and
exploring it with contemporary experimental techniques is arduous and often ineffective …
exploring it with contemporary experimental techniques is arduous and often ineffective …
LPI-HyADBS: a hybrid framework for lncRNA-protein interaction prediction integrating feature selection and classification
L Zhou, Q Duan, X Tian, H Xu, J Tang, L Peng - BMC bioinformatics, 2021 - Springer
Abstract Background Long noncoding RNAs (lncRNAs) have dense linkages with a plethora
of important cellular activities. lncRNAs exert functions by linking with corresponding RNA …
of important cellular activities. lncRNAs exert functions by linking with corresponding RNA …
iEnhancer-RF: Identifying enhancers and their strength by enhanced feature representation using random forest
Enhancers are short DNA regions bound with activators to increase gene transcription over
long distances. Hence, they play a crucial role in regulating eukaryotic gene expression …
long distances. Hence, they play a crucial role in regulating eukaryotic gene expression …