Laboratory of Computational Intelligence and Biotechnology, Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, 81310 UTM Skudai, Malaysia.
Published: June 2010
AI Article Synopsis
Article Abstract
Protein-protein interactions (PPIs) play a significant role in many crucial cellular operations such as metabolism, signaling and regulations. The computational methods for predicting PPIs have shown tremendous growth in recent years, but problem such as huge false positive rates has contributed to the lack of solid PPI information. We aimed at enhancing the overlap between computational predictions and experimental results in an effort to partially remove PPIs falsely predicted. The use of protein function predictor named PFP() that are based on shared interacting domain patterns is introduced in this study with the purpose of aiding the Gene Ontology Annotations (GOA). We used GOA and PFP() as agents in a filtering process to reduce false positive pairs in the computationally predicted PPI datasets. The functions predicted by PFP() were extracted from cross-species PPI data in order to assign novel functional annotations for the uncharacterized proteins and also as additional functions for those that are already characterized by the GO (Gene Ontology). The implementation of PFP() managed to increase the chances of finding matching function annotation for the first rule in the filtration process as much as 20%. To assess the capability of the proposed framework in filtering false PPIs, we applied it on the available S. cerevisiae PPIs and measured the performance in two aspects, the improvement made indicated as Signal-to-Noise Ratio (SNR) and the strength of improvement, respectively. The proposed filtering framework significantly achieved better performance than without it in both metrics.
G protein-coupled estrogen receptor 1 (GPER1) plays a crucial role in the progression of breast cancer and has emerged as a promising therapeutic target. However, while missense mutations in GPER1 have been detected in breast invasive carcinoma (BIC) samples, the resulting molecular, cellular and pharmacological changes remain unclear. The present study categorized BIC samples from The Cancer Genome Atlas database based on mutation information available in the cBioPortal database.
Glioblastoma (GBM) is one of the most malignant forms of cancer with the lowest survival ratio. Our study aims to utilize an integrated bioinformatic analysis to identify hub genes against GBM and explore the active phytochemicals with drug-like properties in treating GBM. The study employed databases of DisGenet, GeneCards, and Gene Expression Omnibus to retrieve GBM-associated genes, revealing 142 overlapping genes.
The utility of environmental RNA (eRNA) in capturing biological responses to stresses has been discussed previously; however, the limited number of genes detected remains a significant hindrance to its widespread implementation. Here, we investigated the potential of eRNA to assess the health status of Japanese medaka fish exposed to linear alkylbenzene sulfonate. Analyzing eRNA and organismal RNA (oRNA) in aquarium water within 12 h, we achieved high mapping rates and 10 times more differentially expressed genes than previously reported.
Engineering Research Center of Edible and Medicinal Fungi, Ministry of Education, Jilin Agricultural University, No. 2888 Xincheng Street, Changchun , Jilin Province, 130118, China.
Background: Hericium coralloides is a traditional edible and medicinal mushroom. Light is a key factor in forming fruiting bodies of fungi; however, the effects of different light on the yield and morphogenesis of H. coralloides are still unknown.
Background: Hypertrophic cardiomyopathy (HCM) is a common heritable heart disease where the most frequently associated mutations occur in the myosin-binding protein C () sarcomere-associated gene. HCM is also a common veterinary clinical problem in certain cat breeds such as Maine Coons and Ragdolls, also most associated with mutations in . Mouse models of HCM in which mutations are introduced recapitulate some, but not all, features of human HCM.
