Current Bioinformatics - Volume 11, Issue 2, 2016

DNA-binding proteins (DNA-BPs) play an important role in many biological processes. Now next-generation sequencing technologies are widely used to obtain genome of many organisms. Consequently, identification of DNA-BPs accurately and rapidly will provide significant helps in annotation of genomes. Chaos game representation (CGR) can reveal the information hidden in protein sequences. Furthermore, fractal dimensions a Read More

Gene expression is arguably the most important indicator of biological function. Thus identifying differentially expressed genes is one of the main aims of high throughout studies that use microarray and RNAseq platforms to study deregulated cellular pathways. There are many tools for analysing differentia gene expression from transciptomic datasets. The major challenge of this topic is to estimate gene expression vari Read More

Small non-coding RNA genes have been concerned as an important field of life sciences in recent years. It plays important regulatory roles in cellular processes. However, the prediction of noncoding RNA genes is a great challenge, because non-coding RNAs have a small size, are not translated into proteins and show variable stability. In this paper, we propose an improved inter-nucleotide distances model as sequence Read More

Protein folding is one of the most important problems in molecular biology. The kinetic order of protein folding is one of the main aspects of the folding process. Previous methods for predicting protein folding kinetic order require to use the information on tertiary or predicted secondary structure of a protein. In this paper, based on physicochemical properties of amino acids, we propose an approach to predict the protei Read More

Protein function prediction is one of the most important tasks in bioinformatics. Nowadays, high-throughput experiments have generated large scale genomics and proteomics data. To accurately annotate proteins, it is necessary and wise to integrate these heterogeneous data sources. In this paper, a multi-source protein global propagation (MS-PGP) algorithm has been proposed, which integrates multiple data sources an Read More

It is a fundamental challenge that identifying disease genes from a large number of candidates for a specific disease. As the biological experiment-based methods are generally timeconsuming and laborious, it has become a new strategy to identify disease candidates by using computational approaches. In this paper, we proposed an algorithm based on the search engine ranking method, named PDGTR, to prioritize disease c Read More

Translating data derived from cancer genomes into personalized cancer therapy is a holy grail of computational biology. An important, yet challenging, question in this undertaking is to relate features of tumor cells to clinical outcomes of anticancer drugs. Recent progress in large pharmacogenomic studies has provided a wealth of data about cancer cell lines, indicating that many genetic and gene expression cand Read More

Biological networks play a significant role in addressing biological problems. Random walk model is a highly efficient way to study networks which has been widely used in solving biological problems based on networks. In this work, those biological problems are classified into four categories, ranking nodes in biological networks, measuring similarity or distance between nodes in biological networks, detecting models from biol Read More

Previous studies indicated that many overlapping structures exist among the modular structures in protein-protein interaction (PPI) networks, which may reflect common functional components shared by different biological processes. In this paper, a Markov clustering based Link Clustering (MLC) method for the identification of overlapping modular structures in PPI networks is proposed. Firstly, MLC method calculates the extende Read More

Automated methods for protein three-dimensional structure comparison play an important role in understanding protein function, evolution and biochemical reaction mechanisms. Since the tertiary structure of proteins is more conserved than their amino-acid sequences, accurately aligning three-dimensional structures allows to detect homology between proteins in the “twilight zone”, those sharing less than ~25% sequence Read More

The availability of large set of high throughput biological data needs algorithm that automatically reconstructs gene regulatory networks from these datasets. Cancerous regulatory network modules when analyzed critically may reveal the underlying mechanism of cancer, which may help in better diagnosis. Identification of cancerous genes and their regulation is an important research area in cancer systems biology. In this p Read More

Motivation: Small Open Reading Frames (smORFs) are involved in a variety of cellular processes varying from metabolism to gene regulation and eukaryotic genomes have been predicted to contain a large number of smORFs. Only a meager 174 smORFs have been annotated in the genome of the human malaria parasite Plasmodium falciparum. Although millions of smORFs can be extracted from the parasite genome, the id Read More

Ubiquitination is involved in various cellular processes such as protein degradation and stability, cell cycle progression, transcriptional regulation, antigen processing, DNA repair, inflammation and regulation of apoptosis, etc. In silico prediction of potential candidate lysine (K) for ubiquitination will not only save time and money but will also generate valuable data for further scientific research. We developed Ubipredictor (http:// Read More

Psychological stress perturbs normal physiological function or homeostasis. Restoration of normalcy demands more supply of energy. A physiological mechanism via activated stress response system is aimed at providing quick energy to deal with such emergency situations. If stress response system remains activated for longer period, maintaining physiological homeostasis becomes difficult because of higher deman Read More

To classify human protein-protein interaction information and consolidate existing data, supervised learning algorithms are implemented. These algorithms require a feature vector to generate a prediction model and feature vectors could be constructed based on various input data. The suitability of feature vector for classification algorithm results in a more predictive model and predictions with higher accuracies based o Read More