Machine learning meets omics: applications and perspectives
The innovation of biotechnologies has allowed the accumulation of omics data at an
alarming rate, thus introducing the era of 'big data'. Extracting inherent valuable knowledge …
alarming rate, thus introducing the era of 'big data'. Extracting inherent valuable knowledge …
Deep learning and ensemble deep learning for circRNA-RBP interaction prediction in the last decade: A review
D Lasantha, S Vidanagamachchi… - … Applications of Artificial …, 2023 - Elsevier
Circular ribonucleic acids (circRNAs) are widely expressed in cells and tissues and play vital
roles in cellular physiological processes. Their expressions are associated with …
roles in cellular physiological processes. Their expressions are associated with …
Gastric cancer and genomics: review of literature
T Onoyama, S Ishikawa, H Isomoto - Journal of Gastroenterology, 2022 - Springer
Gastric cancer (GC) is a major health concern in many countries. GC is a heterogeneous
disease stratified by histopathological differences. However, these variations are not used to …
disease stratified by histopathological differences. However, these variations are not used to …
Deep learning in drug target interaction prediction: current and future perspectives
Drug-target Interactions (DTIs) prediction plays a central role in drug discovery.
Computational methods in DTIs prediction have gained more attention because carrying out …
Computational methods in DTIs prediction have gained more attention because carrying out …
Competing endogenous RNA networks as biomarkers in neurodegenerative diseases
L Moreno-García, T López-Royo, AC Calvo… - International journal of …, 2020 - mdpi.com
Protein aggregation is classically considered the main cause of neuronal death in
neurodegenerative diseases (NDDs). However, increasing evidence suggests that alteration …
neurodegenerative diseases (NDDs). However, increasing evidence suggests that alteration …
Recent trends in RNA informatics: a review of machine learning and deep learning for RNA secondary structure prediction and RNA drug discovery
Computational analysis of RNA sequences constitutes a crucial step in the field of RNA
biology. As in other domains of the life sciences, the incorporation of artificial intelligence …
biology. As in other domains of the life sciences, the incorporation of artificial intelligence …
A comprehensive review of federated learning for COVID‐19 detection
Abstract The coronavirus of 2019 (COVID‐19) was declared a global pandemic by World
Health Organization in March 2020. Effective testing is crucial to slow the spread of the …
Health Organization in March 2020. Effective testing is crucial to slow the spread of the …
Boosting tree-assisted multitask deep learning for small scientific datasets
Machine learning approaches have had tremendous success in various disciplines.
However, such success highly depends on the size and quality of datasets. Scientific …
However, such success highly depends on the size and quality of datasets. Scientific …
Non-coding RNA-based regulation of inflammation
Inflammation is a multifactorial process and various biological mechanisms and pathways
participate in its development. The presence of inflammation is involved in pathogenesis of …
participate in its development. The presence of inflammation is involved in pathogenesis of …
Oncogenic roles of small nucleolar RNA host gene 7 (SNHG7) long noncoding RNA in human cancers and potentials
S Najafi, S Ghafouri-Fard, BM Hussen… - Frontiers in cell and …, 2022 - frontiersin.org
Long noncoding RNAs (lncRNAs) are a class of noncoding transcripts characterized with
more than 200 nucleotides of length. Unlike their names, some short open reading frames …
more than 200 nucleotides of length. Unlike their names, some short open reading frames …