AI Medical Compendium Journal:
Computational biology and chemistry

Showing 111 to 120 of 191 articles

Deep_CNN_LSTM_GO: Protein function prediction from amino-acid sequences.

Computational biology and chemistry
Protein amino acid sequences can be used to determine the functions of the protein. However, determining the function of a single protein requires many resources and a tremendous amount of time. Computational Intelligence methods such as Deep learnin...

In silico screening of ssDNA aptamer against Escherichia coli O157:H7: A machine learning and the Pseudo K-tuple nucleotide composition based approach.

Computational biology and chemistry
This study was planned to in silico screening of ssDNA aptamer against Escherichia coli O157:H7 by combination of machine learning and the PseKNC approach. For this, firstly a total numbers of 47 validated ssDNA aptamers as well as 498 random DNA seq...

An ensemble learning framework for potential miRNA-disease association prediction with positive-unlabeled data.

Computational biology and chemistry
To explore the pathogenic mechanisms of MicroRNA (miRNA) on diverse diseases, many researchers have concentrated on discovering the potential associations between miRNA and disease using machine learning methods. However, the prediction accuracy of s...

ABLE: Attention based learning for enzyme classification.

Computational biology and chemistry
Classifying proteins into their respective enzyme class is an interesting question for researchers for a variety of reasons. The open source Protein Data Bank (PDB) contains more than 1,60,000 structures, with more being added everyday. This paper pr...

ActTRANS: Functional classification in active transport proteins based on transfer learning and contextual representations.

Computational biology and chemistry
MOTIVATION: Primary and secondary active transport are two types of active transport that involve using energy to move the substances. Active transport mechanisms do use proteins to assist in transport and play essential roles to regulate the traffic...

Ensembling machine learning models to boost molecular affinity prediction.

Computational biology and chemistry
This study unites six popular machine learning approaches to enhance the prediction of a molecular binding affinity between receptors (large protein molecules) and ligands (small organic molecules). Here we examine a scheme where affinity of ligands ...

DeepSIRT: A deep neural network for identification of sirtuin targets and their subcellular localizations.

Computational biology and chemistry
Sirtuins are a family of proteins that play a key role in regulating a wide range of cellular processes including DNA regulation, metabolism, aging/longevity, cell survival, apoptosis, and stress resistance. Sirtuins are protein deacetylases and incl...

MD-UNET: Multi-input dilated U-shape neural network for segmentation of bladder cancer.

Computational biology and chemistry
Accurate segmentation of the tumour area is crucial for the treatment and prognosis of patients with bladder cancer. However, the complex information from the MRI image poses an important challenge for us to accurately segment the lesion, for example...

Drug repurposing for hyperlipidemia associated disorders: An integrative network biology and machine learning approach.

Computational biology and chemistry
Hyperlipidemia causes diseases like cardiovascular disease, cancer, Type II Diabetes and Alzheimer's disease. Drugs that specifically target HL associated diseases are required for treatment. 34 KEGG pathways targeted by lipid lowering drugs were use...

Convolutional neural networks with image representation of amino acid sequences for protein function prediction.

Computational biology and chemistry
Proteins are one of the most important molecules that govern the cellular processes in most of the living organisms. Various functions of the proteins are of paramount importance to understand the basics of life. Several supervised learning approache...