AI Medical Compendium Journal:
Computational biology and chemistry

Showing 121 to 130 of 191 articles

GanDTI: A multi-task neural network for drug-target interaction prediction.

Computational biology and chemistry
Drug discovery processes require drug-target interaction (DTI) prediction by virtual screenings with high accuracy. Compared with traditional methods, the deep learning method requires less time and domain expertise, while achieving higher accuracy. ...

Structural protein fold recognition based on secondary structure and evolutionary information using machine learning algorithms.

Computational biology and chemistry
Understanding the function of protein is conducive to research in advanced fields such as gene therapy of diseases, the development and design of new drugs, etc. The prerequisite for understanding the function of a protein is to determine its tertiar...

Apache Spark based kernelized fuzzy clustering framework for single nucleotide polymorphism sequence analysis.

Computational biology and chemistry
This paper introduces a kernel based fuzzy clustering approach to deal with the non-linear separable problems by applying kernel Radial Basis Functions (RBF) which maps the input data space non-linearly into a high-dimensional feature space. Discover...

TAP 1.0: A robust immunoinformatic tool for the prediction of tumor T-cell antigens based on AAindex properties.

Computational biology and chemistry
Immunotherapy is a research area with great potential in drug discovery for cancer treatment. Because of the capacity of tumor antigens to activate the immune response and promote the destruction of tumor cells, they are considered excellent immunoth...

Computational approach for identification, characterization, three-dimensional structure modelling and machine learning-based thermostability prediction of xylanases from the genome of Aspergillus fumigatus.

Computational biology and chemistry
Identification of thermostable and alkaline xylanases from different fungal and bacterial species have gained an interest for the researchers because of its biotechnological relevance in many industries, such as pulp, paper, and bioethanol. In this s...

diSBPred: A machine learning based approach for disulfide bond prediction.

Computational biology and chemistry
The protein disulfide bond is a covalent bond that forms during post-translational modification by the oxidation of a pair of cysteines. In protein, the disulfide bond is the most frequent covalent link between amino acids after the peptide bond. It ...

Cnngeno: A high-precision deep learning based strategy for the calling of structural variation genotype.

Computational biology and chemistry
Genotype plays a significant role in determining characteristics in an organism and genotype calling has been greatly accelerated by sequencing technologies. Furthermore, most parametric statistical models are unable to effectively call genotype, whi...

LMI-DForest: A deep forest model towards the prediction of lncRNA-miRNA interactions.

Computational biology and chemistry
The interactions between miRNAs and long non-coding RNAs (lncRNAs) are subject to intensive recent studies due to its critical role in gene regulations. Computational prediction of lncRNA-miRNA interactions has become a popular alternative strategy t...

A Recurrent Neural Network model to predict blood-brain barrier permeability.

Computational biology and chemistry
The rapid development of computational methods and the increasing volume of chemical and biological data have contributed to an immense growth in chemical research. This field of study is known as "chemoinformatics," which is a discipline that uses m...

DeepAdd: Protein function prediction from k-mer embedding and additional features.

Computational biology and chemistry
With the application of new high throughput sequencing technology, a large number of protein sequences is becoming available. Determination of the functional characteristics of these proteins by experiments is an expensive endeavor that requires a lo...