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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...
Ligand binding site prediction from protein structure plays an important role in various complex rational drug design efforts. Its applications include drug side effects prediction, docking prioritization in inverse virtual screening and elucidation of protein function in genome wide structural studies. Currently available tools have limitations that disqualify them from many possible use cases. In...
Various problems in biomedicine can be formulated as a ranking problem, where a set of candidate components is ranked relatively based on a set of known components. The most popular problem in biomedicine is identification of disease-associated cellular components, where cellular components can be genes, proteins, microRNAs or other molecules. Besides that, a number of problems in pharmacology is...
MicroRNAs (miRNAs) are a class of small noncoding RNAs which have close relations with human diseases. Herein, predicting the novel associations between human diseases and miRNAS is urgently needed. However, only use of the experimental approaches to identity such relations have many choke points such as time-consumption and high cost. In this study, we adopt a network-based inference (NBI) based...
This article presents a new SVM classifier for the prediction of the extended early-stage (ES) protein structures. The classifier is based on physicochemical features and position-specific scoring matrix (PSSM). Experiments have shown that prediction results for specific classes are significantly better than those already obtained.
In bioinformatics, protein multiple sequence alignment (MSA) and phylogenetic tree construction are among the major problems for which many algorithms have been developed to improve the accuracy the results. However, finding the best algorithm among the available ones remains a challenging task since the efficiency of an algorithm is closely related to the characteristics of the input sequences. Moreover,...
Calcium channel blockers (CCB) disrupt the movement of calcium and prevent it from entering cells of the blood vessel walls. They are used to widen blood vessel resulting in lower blood pressure. Amlodipine is one such calcium channel blocker that dilates the blood vessel and improves blood flow. It is used to treat angina, high blood pressure and hypertension. Amlodipine is quite effective in treating...
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...
Protein structure prediction is a long standing problem in the filed of structural bioinformatics. To address this problem, on the basis of Monte Carlo (MC), a double estimation of distribution guided sampling algorithm for de-novo protein structure prediction (DED) is proposed. Serval MC trajectories are launched simultaneously at the begining of DED. And two probability distribution are designed...
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...
Heterogeneous data sources and multi-label are two important characteristics of protein function prediction. They describe protein data from two different aspects. However, it is of considerable challenge to integrate multiple data sources and multi-label simultaneously for predicting protein functions, especially when there are only a limited number of labeled proteins. In this paper, we propose...
Lymphoma is a type of cancer that originates in resistant framework during infection in battling cells called lymphocytes. Lymphoma is mainly classified into two i.e. Hodgkin's Lymphoma and Non-Hodgkin's Lymphoma. In Non-Hodgkin's Lymphoma there are four stages. In the first stage, cancer will spread to one extra −lymphatic organ or site and in remaining stages it will spread to another part of the...
B-cell epitope prediction is the task of estimating the class label of antigen surface as the epitope or non-epitope. Since each protein dataset consists of different scales, such as physicochemical, statistical and structural, it may be efficient to identify the importance of each scale and its influence on the results of the prediction. To this end, this paper uses some criteria to select the most...
Today's world is gradually getting agitated by Human Immunodeficiency Virus — Type 1 due to its pervasive and death-dealing nature. The virus replicates by exploiting a complex interaction network of HIV-1 and human proteins and destructs human immunity power, gradually leading to AIDS. Anti-HIV drugs are designed to utilize the information on viral-host protein-protein interactions (PPIs), so that...
Advancements in the field of mathematical rigidity theory have opened up a number of exciting opportunities for computational predictions of protein flexibility and their dynamics. Starting with a 3D protein structure, several programs such as FIRST model the protein as a constraint multigraph, consisting of vertices (atoms) and edges (covalent bonds, hydrogen bonds, electrostatic interactions, and...
Protein Structure Prediction (PSP) is an NP-hard optimization problem that has been solved by many existing algorithms. Simply, it can be thought of a process of predicting the native 3D structure from its amino acid sequence. Chemical Reaction Optimization (CRO) is a recent metaheuristic algorithm that has been applied to many well-known problems and has shown better performance compared to the existing...
In this paper, we propose algorithms for biomolecular docking sites selection problem by various machine learning approaches with selective features reduction. The proposed method can reduce the number of various amino acid features before constructing machine learning prediction models. Given frame boxes with features, the proposed method analyzes the important features by correlation coefficients...
Protein residue-residue contacts dictate the topology of protein structure and play an important role in structural biology, especially in de novo protein structure prediction. Accurate prediction of residue contacts could improve the performance of de novo protein structure prediction methods. In this study, a novel method based on learning-to-rank (RRCRank) has been presented to predict protein...
Protein function prediction is a challenging and essential research problem in the field of computational biology. Conventionally, a protein consists of a number of structural domains and performs multiple function. By representing proteins, domains and functions by bags as well as instances and classes respectively, we are able to model the protein function prediction task as the Multi-Instance Multi-Label...
The detection of protein complexes from protein-protein interaction (PPI) networks is an important step toward understanding the functional organization within cells. A great number of graph clustering algorithms have been proposed to undertake this task. Since PPI data collected by high-throughput technologies is quite noisy, simply applying graph clustering algorithms on PPI data is generally not...
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