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A large number of long non-coding RNAs (lncRNAs) have been identified over the past decades. Accumulating evidence proves that lncRNAs play key roles in various biological processes. However, the majority of the lncRNAs have not been functionally characterized. The annotation of lncRNA functions has become an area of focus in the fields of biology and bioinformatics. In this paper, we develop a global...
Protein-protein interactions (PPI) occur at every level of cell functions. The identification of protein interactions provides a global picture of cellular functions and biological processes. It is also an essential step in the construction of PPI networks for human and other organisms. PPI prediction has been considered a promising alternative to the traditional drug design techniques. The identification...
The knowledge about the conformation of a protein molecule allows the inference and study of its biological function. Because protein function is determined by its shape and the physio-chemical properties of its exposed surface, it is extremely important to predict accurate protein models. One of the hardest problems in Structural Bioinformatics is associated with the prediction of the three-dimensional...
Essential proteins play a crucial role in the survival and development process of life, as they provide all available nutrients to maintain life. Therefore, many researchers pay attention to the identification of essential proteins. As experiments methods are usually costly and time-consuming, more and more computational algorithms have been developed to discover essential proteins based on biological...
Referring to ab-initio protein structure prediction, a replica exchange based differential evolution searching method (REDE) was proposed. Firstly, employing fragment assembly technique to reduce the dimension of protein conformational space effectively, thus avoiding the entropy effect in searching. Additionally, every replica layer equipped with a conformation population, updating the population...
Network pharmacology has become the new approach for drug mechanism research and novel drug design. Drug target prediction based on computational approach became one of the primary approaches. However, due to the diversity and complexity of herbal chemical structures, the performance of herb target prediction based on chemical structure similarity is limited by the quality and the data availability...
Using graph databases becomes increasingly popular in domains where data can be modeled as a set of connected objects. Graph databases enable to query such data using graphbased queries in a relatively simple manner in comparison to the classical relational databases. In this paper, we show how one of the most popular graph databases, Neo4j, can be applied to the bioinformatics problem of protein-protein...
The Smith-Waterman local alignment algorithm is the method of choice for protein database searches because it is often able to detect remote homologues for a query protein sequence. However, it is also well known that the reliability of this algorithm degrades sharply for proteins with low similarity to a given query — so-called "twilight zone" matches. In these situations, global alignments...
Breast cancer is a leading cause of cancer-related deaths in women worldwide. Discovery of breast cancer-related disease genes is becoming very important to researcher and opens a new way to investigate pathogenic mechanism of breast cancer. Many studies have shown that the availability of human genome-wide protein-protein interactions (PPI) provides us with new opportunity for discovering disease-genes...
Predicting drug-target interaction using computational approaches is an important step in drug discovery and repositioning. To predict whether there will be an interaction between a drug and a target, most existing methods identify similar drugs and targets in the database. The prediction is then made based on the known interactions of these drugs and targets. This idea is promising. However, there...
A signaling pathway, which is represented as a chain of interacting proteins for a biological process, can be predicted from protein-protein interaction (PPI) networks. However, pathway prediction is computationally challenging because of (1) inefficiency in searching all possible paths from the large-scale PPI networks and (2) unreliability of current PPI data generated by automated high-throughput...
Proteomics is currently driven by mass spectrometry. For the analysis of tandem mass spectra many computational algorithms have been proposed. There are two approaches, one which assigns a peptide sequence to a tandem mass spectrum directly and one which employs a sequence database for looking up possible solutions. The former method needs high quality spectra while the latter can tolerate lower quality...
The families of G-Protein Coupled Receptor (GPCR) and enzymes are among the main protein family. They represent to the scientific and medical communities, a significant target for bioactive and drug discovery programs. The model of classification of enzymes and GPCR is characterized by its hierarchical structure in format of tree and this makes more difficult its prediction. In this work we propose...
Proteins perform most important biochemical reactions in organisms, such as the catalysis, signal transduction, and transport of nutrients. The urgent need of automatic annotation is due to the advent of high-throughput sequencing techniques in the post-genomic era. Proteins consist of domains which are elementary building units of protein folding, function, and evolution. The evidence of protein...
In data mining applications it is common to have more than one data source available to describe the same record. For example, in biological sciences, the same genes may be characterized through many types of experiments. Which of the data sources proves to be most reliable in predictions may depend on the record in question. For some records pieces of information may be unavailable because an experiment...
Many protein complexes prediction approaches are based on the assumptions that (i) protein complexes have dense protein-protein interactions (PPI) among their subunits, and (ii) high functional similarity for the subunits. We suggest to investigate those assumptions by studying the subunits' interaction topology and sequences identity. Such consideration can possibly provide better insights for our...
It is an important problem to find functionally analogous enzymes based on the local structures of active-sites. Conventional methods predict active-sites by computing the deviations from the local-structure templates with no statistical parameters. We present a new statistical algorithm that uses parametric templates to compute the deviations of local sites. The parameters of the templates are determined...
Protein complexes are important entities to organize various biological systems. However, they are still limited in availability. Thus, it is a challenging problem to predict protein complexes computationally from existing genome-wide data sets, like protein-protein interaction (PPI) networks. In this paper, we propose an efficient algorithm for predicting protein complexes by random walking on a...
This work presents a system for knowledge discovery from protein databases, based on an Artificial Immune System. The discovered rules have the advantage of representing comprehensible knowledge to biologist users. This task leads to a very challenging problem since a protein can be assigned multiple classes (functions or Gene Ontology (GO) terms) across several levels of the GO's term hierarchy....
As more and more high-throughput protein-protein interactions data are collected, a large fraction of newly discovered proteins have an unknown functional role. A challenge to the scientific community is to assign these newly proteins with a biological function that can be verified by experiment. On the basis of thorough analysis of existing protein function prediction, we take double direction enumeration...
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