AI Medical Compendium Journal:
Computational biology and chemistry

Showing 101 to 110 of 191 articles

BERT-Promoter: An improved sequence-based predictor of DNA promoter using BERT pre-trained model and SHAP feature selection.

Computational biology and chemistry
A promoter is a sequence of DNA that initializes the process of transcription and regulates whenever and wherever genes are expressed in the organism. Because of its importance in molecular biology, identifying DNA promoters are challenging to provid...

GraphDPA: Predicting drug-pathway associations by graph convolutional networks.

Computational biology and chemistry
Pathway-based drug discovery is a promising strategy for the discovery of drugs with low toxicity and side effects. However, identifying the associations between drug and targeting pathways is challenging for this method. The formation of various bio...

Effective prediction of soil micronutrients using Additive Gaussian process with RAM augmentation.

Computational biology and chemistry
In soil chemistry, the nutrients exhibit non-linear and complex relationships owing to their stochastic nature but mostly their similarity is a function of the distance between the data points. The similarity assessment using distance metrics is a po...

SE-BLTCNN: A channel attention adapted deep learning model based on PSSM for membrane protein classification.

Computational biology and chemistry
Membrane protein classification is a key to inferring the function of uncharacterized membrane protein. To get around the time-consuming and expensive biochemical experiments in the wet lab, there has been a lot of research focusing on developing fas...

Prediction of the disease causal genes based on heterogeneous network and multi-feature combination method.

Computational biology and chemistry
At present, the prediction of disease causal genes is mainly based on heterogeneous. Research shows that heterogeneous network contains more information and have better prediction results. In this paper, we constructed a heterogeneous network includi...

Predicting the evolution of number of native contacts of a small protein by using deep learning approach.

Computational biology and chemistry
Native contacts (NCs) are one of the most vital parameters in order to define the resemblance of a protein conformation with its native state. Prediction of number of native contacts in a protein is useful in protein folding mechanism. In this work, ...

Virtual screening of dipeptidyl peptidase-4 inhibitors using quantitative structure-activity relationship-based artificial intelligence and molecular docking of hit compounds.

Computational biology and chemistry
Dipeptidyl peptidase-4 (DPP-4) inhibitors are becoming an essential drug in the treatment of type 2 diabetes mellitus; however, some classes of these drugs exert side effects, including joint pain and pancreatitis. Studies suggest that these side eff...

Deep protein representations enable recombinant protein expression prediction.

Computational biology and chemistry
A crucial process in the production of industrial enzymes is recombinant gene expression, which aims to induce enzyme overexpression of the genes in a host microbe. Current approaches for securing overexpression rely on molecular tools such as adjust...

Protein function prediction using functional inter-relationship.

Computational biology and chemistry
With the growth of high throughput sequencing techniques, the generation of protein sequences has become fast and cheap, leading to a huge increase in the number of known proteins. However, it is challenging to identify the functions being performed ...

Prediction for understanding the effectiveness of antiviral peptides.

Computational biology and chemistry
The low efficacy of current antivirals in conjunction with the resistance of viruses against existing antiviral drugs has resulted in the demand for the development of novel antiviral agents. Antiviral peptides (AVPs) are those bioactive peptides hav...