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 …
Learning with Hilbert–Schmidt independence criterion: A review and new perspectives
T Wang, X Dai, Y Liu - Knowledge-based systems, 2021 - Elsevier
Abstract The Hilbert–Schmidt independence criterion (HSIC) was originally designed to
measure the statistical dependence of the distribution-based Hilbert space embedding in …
measure the statistical dependence of the distribution-based Hilbert space embedding in …
Application of machine learning for drug–target interaction prediction
L Xu, X Ru, R Song - Frontiers in genetics, 2021 - frontiersin.org
Exploring drug–target interactions by biomedical experiments requires a lot of human,
financial, and material resources. To save time and cost to meet the needs of the present …
financial, and material resources. To save time and cost to meet the needs of the present …
[HTML][HTML] AttentionMGT-DTA: A multi-modal drug-target affinity prediction using graph transformer and attention mechanism
The accurate prediction of drug-target affinity (DTA) is a crucial step in drug discovery and
design. Traditional experiments are very expensive and time-consuming. Recently, deep …
design. Traditional experiments are very expensive and time-consuming. Recently, deep …
Machine learning aided construction of the quorum sensing communication network for human gut microbiota
S Wu, J Feng, C Liu, H Wu, Z Qiu, J Ge, S Sun… - Nature …, 2022 - nature.com
Quorum sensing (QS) is a cell-cell communication mechanism that connects members in
various microbial systems. Conventionally, a small number of QS entries are collected for …
various microbial systems. Conventionally, a small number of QS entries are collected for …
DNN-DTIs: Improved drug-target interactions prediction using XGBoost feature selection and deep neural network
Abstract Analysis and prediction of drug-target interactions (DTIs) play an important role in
understanding drug mechanisms, as well as drug repositioning and design. Machine …
understanding drug mechanisms, as well as drug repositioning and design. Machine …
[HTML][HTML] Shared subspace-based radial basis function neural network for identifying ncRNAs subcellular localization
Non-coding RNAs (ncRNAs) play an important role in revealing the mechanism of human
disease for anti-tumor and anti-virus substances. Detecting subcellular locations of ncRNAs …
disease for anti-tumor and anti-virus substances. Detecting subcellular locations of ncRNAs …
CRBPDL: Identification of circRNA-RBP interaction sites using an ensemble neural network approach
Circular RNAs (circRNAs) are non-coding RNAs with a special circular structure produced
formed by the reverse splicing mechanism. Increasing evidence shows that circular RNAs …
formed by the reverse splicing mechanism. Increasing evidence shows that circular RNAs …
NmRF: identification of multispecies RNA 2'-O-methylation modification sites from RNA sequences
C Ao, Q Zou, L Yu - Briefings in bioinformatics, 2022 - academic.oup.com
O-methylation (Nm) is a post-transcriptional modification of RNA that is catalyzed by 2'-O-
methyltransferase and involves replacing the H on the 2′-hydroxyl group with a methyl …
methyltransferase and involves replacing the H on the 2′-hydroxyl group with a methyl …
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 …