AI Medical Compendium Journal:
Briefings in functional genomics

Showing 21 to 30 of 39 articles

DeepPRMS: advanced deep learning model to predict protein arginine methylation sites.

Briefings in functional genomics
Protein methylation is a form of post-translational modifications of protein, which is crucial for various cellular processes, including transcription activity and DNA repair. Correctly predicting protein methylation sites is fundamental for research...

Predicting drug synergy using a network propagation inspired machine learning framework.

Briefings in functional genomics
Combination therapy is a promising strategy for cancers, increasing therapeutic options and reducing drug resistance. Yet, systematic identification of efficacious drug combinations is limited by the combinatorial explosion caused by a large number o...

THGNCDA: circRNA-disease association prediction based on triple heterogeneous graph network.

Briefings in functional genomics
Circular RNAs (circRNAs) are a class of noncoding RNA molecules featuring a closed circular structure. They have been proved to play a significant role in the reduction of many diseases. Besides, many researches in clinical diagnosis and treatment of...

Prediction of drug-protein interaction based on dual channel neural networks with attention mechanism.

Briefings in functional genomics
The precise identification of drug-protein inter action (DPI) can significantly speed up the drug discovery process. Bioassay methods are time-consuming and expensive to screen for each pair of drug proteins. Machine-learning-based methods cannot acc...

Predicting gastric cancer tumor mutational burden from histopathological images using multimodal deep learning.

Briefings in functional genomics
Tumor mutational burden (TMB) is a significant predictive biomarker for selecting patients that may benefit from immune checkpoint inhibitor therapy. Whole exome sequencing is a common method for measuring TMB; however, its clinical application is li...

Omics-based deep learning approaches for lung cancer decision-making and therapeutics development.

Briefings in functional genomics
Lung cancer has been the most common and the leading cause of cancer deaths globally. Besides clinicopathological observations and traditional molecular tests, the advent of robust and scalable techniques for nucleic acid analysis has revolutionized ...

ncRNALocate-EL: a multi-label ncRNA subcellular locality prediction model based on ensemble learning.

Briefings in functional genomics
Subcellular localizations of ncRNAs are associated with specific functions. Currently, an increasing number of biological researchers are focusing on computational approaches to identify subcellular localizations of ncRNAs. However, the performance o...

Molecular language models: RNNs or transformer?

Briefings in functional genomics
Language models have shown the capacity to learn complex molecular distributions. In the field of molecular generation, they are designed to explore the distribution of molecules, and previous studies have demonstrated their ability to learn molecule...

COPPER: an ensemble deep-learning approach for identifying exclusive virus-derived small interfering RNAs in plants.

Briefings in functional genomics
Antiviral defenses are one of the significant roles of RNA interference (RNAi) in plants. It has been reported that the host RNAi mechanism machinery can target viral RNAs for destruction because virus-derived small interfering RNAs (vsiRNAs) are fou...

Deep learning-based classifier of diffuse large B-cell lymphoma cell-of-origin with clinical outcome.

Briefings in functional genomics
Diffuse large B-cell lymphoma (DLBCL) is an aggressive form of non-Hodgkin lymphoma with poor response to R-CHOP therapy due to remarkable heterogeneity. Based on gene expression, DLBCL cases were divided into two subtypes, i.e. ABC and GCB, where AB...