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Similar diseases are often caused by their similar molecular origins, such as disease-related protein-coding genes (PCGs). And nowadays, the function of PCGs has been widely studied on a gene function network, where each node represents a gene and each edge indicates an interaction between pair-wise genes. Therefore, functional interaction between disease-related PCGs should be exploited to measure...
Protein-peptide interactions are the promising targets for potential protein drugs due to their critical role in many signal pathways with its small interaction interface. As a significant step in virtual screening in peptide drug discovery, binding affinity prediction is still a unsolved problem compared with achievements in docking. Most of current binding affinity prediction models are limited...
We consider the problem of ligand affinity prediction as a regression task, typically with few labelled examples, many unlabelled instances, and multiple views on the data. In chemoinformatics, the prediction of binding affinities for protein ligands is an important but also challenging task. As protein-ligand bonds trigger biochemical reactions, their characterisation is a crucial step in the process...
In clinical practice at Tibetan area of China, Traditional Tibetan Medicine formula Wuwei-Ganlu-Yaoyu-Keli(WGYK) is widely added in warm water of bath therapy to treat rheumatoid arthritis (RA). However, its action mechanism is not well interpreted yet. In this study, based on gene expression data from microarray experiments, we apply approaches of network pharmacology to further reveal the action...
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...
In drug development process, adverse drug reaction (ADR) is one of the biggest challenges to evaluate the drug safety for passing to the market. Genomic expression data following in vitro drug treatments and thus have become widely used in ADR identification and prediction. In this research, we develop the prediction method by using system pharmacology-based study. We performed the proteomic, small...
Multifunctional genes possess special significance due to their role in multiple biological and molecular activities in many organisms. In this paper, we study multifunctional genes in human with gene functional annotations in Gene Ontology, and we utilize the extensive information available for genes related to gene functionalities from the Molecular Function and Biological Process aspects of the...
Nowadays, life scientists grapple with a problem how to fast and easily access/obtain high quality data, especially for a specific research area, from a large amount of biological data deposited in public databases. In this work, we developed an effective system for managing biological data which are a class of functionally important membrane protein; they are hard to collected from the existing databases...
Vaccine development evolves to peptide-based vaccine which is effective and efficient if it is composed from epitopes of pathogens. Experimental-based epitope prediction such as X-ray crystallography is time consuming and expensive. Available data from this experiment makes a possibility to develop computer based epitope prediction method. This approach is crucial to accelerate epitope identification...
Bupleuri Radix, a famous Chinese herbal medicine called Chai-Hu, has been widely used to treat depression like syndromes over a long history. Although associated clinical evidences has been accumulated and studied, its molecule level therapeutic mechanism is still obscure which blocks the understanding and new drug discovery against major depression. In this study, focused on the active compounds...
There are several methods proposed to extract data from biological databases developed by bioinformatics experts. These methods retrieve bioinformatics information through the use of structured and semi-structured data tools from remote database servers. This paper investigates and evaluates the performance of these methods in terms of their ability to gain access to a cross representation of bioinformatics...
Text mining techniques are useful in extracting hidden information about the biomedical interactions such as Protein-Protein, Drug-Drug and Protein-Drug. Recently, there is an increased interest in automated methods due to the vast growth in the volume of published text regarding biomedical interactions. This work mainly focuses on extraction of Drug-Drug interactions (DDIs) in biomedical research...
In previous works, we presented Cross Motif Search (CMS), an algorithm designed to explore new techniques in the field of protein motif retrieval and identification. The novelty of the CMS approach is to look for geometrical similarities in the secondary structures of proteins, instead of homologous topology. Put in other words, while the connections among different secondary structures are still...
Clustering, or unsupervised classification, is animportant issue in Bioinformatics. It serves to automaticallygroup protein sequences into families. Most researchers treatthe biclass clustering problem. In this paper we present ourapproach for the multiclass clustering of protein sequences. It isa difficult problem, because we are based on primary structure. This approach consists of four steps. In...
In this paper we propose an on-the-fly reconfigurable hardware-software codesign based reconfigurable solution for real-time protein identification. Reconfigurable string matching is performed in the disciplines of protein identification and biomarkers discovery. With the generation of plethora of sequenced data and number of biomarkers for several diseases, it is becoming necessary to have an accelerated...
Multi-omic approaches offer the opportunity to characterize complex diseases such as cancer at various molecular levels. In this paper, we present transcriptomic, proteomic/glycoproteomic, glycomic, and metabolomic (TPGM) data we acquired by analysis of liver tissues from hepatocellular carcinoma (HCC) cases and patients with liver cirrhosis. We evaluated changes in the levels of transcripts, proteins,...
Study of genetic variants in the context of molecular networks has recently gained much attention. However, many of these studies suffer from the lack of functional information about the network rewiring effect of genetic variants. After large-scale homology modeling, plus extracting native structure from PDB database, we performed structure-based prediction about the rewiring effect using our SNP-IN...
Acanthopanacis Cortex (AC) is a Chinese herbal medicine (Wu-Jia-Pi in PinYin) which is recognized with therapeutic effect of “strengthening bone and sinew”, yet its regulating biological network is still obscure. In this study, started from AC's chemical compounds, their direct target proteins were filtered out. Then these targeted proteins were annotated with biological processes under the framework...
Glaucoma is a silent eye disease that steals sight without warning and it is the second leading cause of blindness worldwide. It cannot be cured but it can be controlled. A gene network analysis to understand the cross talk between glaucoma networks of genes could contribute to better understanding and treatment of the disease. In this paper, we extend our previous text mining and network analysis...
Prediction of protein fold families remains an existing challenge in molecular biology and bioinformatics, mainly because proteins form a broad range of complex three-dimensional configurations and because the number of proteins registered in datasets has dramatically increased in the recent years. Computational alternatives must then be designed for substituting experimental methods. However, implementations...
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