Constructing lncRNA-miRNA-mRNA networks specific to individual cancer patients and finding prognostic biomarkers.

Background: The competitive endogenous RNA (ceRNA) hypothesis suggests that microRNAs (miRNAs) mediate a regulatory relation between long noncoding RNAs (lncRNAs) and messenger RNAs (mRNAs) which share similar miRNA response elements (MREs) to bind to the same miRNA. Since the ceRNA hypothesis was proposed, several studies have been conducted to construct a network of lncRNAs, miRNAs and mRNAs in cancer. However, most cancer-related ceRNA networks are intended for representing a general relation of RNAs in cancer rather than for a patient-specific relation.

Eco-friendly natural mineral biotite as a cesium adsorbent: Utilizing low-concentration acid and hydrogen peroxide.

Biotite, a phyllosilicate mineral, possesses significant potential for cesium (Cs) adsorption owing to its negative surface charge, specific surface area (SSA), and frayed edge sites (FES). Notably, FES are known to play an important role in the adsorption of Cs. The objectives of this study were to investigate the Cs adsorption capacity and behavior of artificially weathered biotite and identify mineralogical characteristics for the development of an eco-friendly geologically-based Cs adsorbent.

In Situ Radiation Hardness Study of Amorphous Zn-In-Sn-O Thin-Film Transistors with Structural Plasticity and Defect Tolerance.

Solution-processed metal-oxide thin-film transistors (TFTs) with different metal compositions are investigated for ex situ and in situ radiation hardness experiments against ionizing radiation exposure. The synergetic combination of structural plasticity of Zn, defect tolerance of Sn, and high electron mobility of In identifies amorphous zinc-indium-tin oxide (Zn-In-Sn-O or ZITO) as an optimal radiation-resistant channel layer of TFTs. The ZITO with an elemental blending ratio of 4:1:1 for Zn/In/Sn exhibits superior ex situ radiation resistance compared to In-Ga-Zn-O, Ga-Sn-O, Ga-In-Sn-O, and Ga-Sn-Zn-O.

Prediction of Cancer Metastasis Using Correlations Between miRNAs and Competing Endogenous RNAs.

Cancer metastasis is a complex process which involves the spread of tumor cells from the primary site to other parts of the body. Metastasis is the major cause of cancer mortality, accounting for about 90% of cancer deaths. Metastasis is primarily diagnosed by clinical examinations and imaging techniques, but such a diagnosis is made after metastasis has occurred.

Finding miRNA-RNA Network Biomarkers for Predicting Metastasis and Prognosis in Cancer.

Despite remarkable progress in cancer research and treatment over the past decades, cancer ranks as a leading cause of death worldwide. In particular, metastasis is the major cause of cancer deaths. After an extensive analysis of miRNAs and RNAs in tumor tissue samples, we derived miRNA-RNA pairs with substantially different correlations from those in normal tissue samples.

Constructing Integrative ceRNA Networks and Finding Prognostic Biomarkers in Renal Cell Carcinoma.

Inspired by a newly discovered gene regulation mechanism known as competing endogenous RNA (ceRNA) interactions, several computational methods have been proposed to generate ceRNA networks. However, most of these methods have focused on deriving restricted types of ceRNA interactions such as lncRNA-miRNA-mRNA interactions. Competition for miRNA-binding occurs not only between lncRNAs and mRNAs but also between lncRNAs or between mRNAs.

Comparative Analysis of Gene Correlation Networks of Breast Cancer Patients Based on Mutations in TP53.

Breast cancer is one of the most prevalent cancers in females, with more than 450,000 deaths each year worldwide. Among the subtypes of breast cancer, basal-like breast cancer, also known as triple-negative breast cancer, shows the lowest survival rate and does not have effective treatments yet. Somatic mutations in the TP53 gene frequently occur across all breast cancer subtypes, but comparative analysis of gene correlations with respect to mutations in TP53 has not been done so far.

GeneCoNet: A web application server for constructing cancer patient-specific gene correlation networks with prognostic gene pairs.

Background And Objective: Most prognostic gene signatures that have been known for cancer are either individual genes or combination of genes. Both individual genes and combination of genes do not provide information on gene-gene relations, and often have less prognostic significance than random genes associated with cell proliferation. Several methods for generating sample-specific gene networks have been proposed, but programs implementing the methods are not publicly available.

A New Approach to Deriving Prognostic Gene Pairs From Cancer Patient-Specific Gene Correlation Networks.

Many of the known prognostic gene signatures for cancer are individual genes or combination of genes, found by the analysis of microarray data. However, many of the known cancer signatures are less predictive than random gene expression signatures, and such random signatures are significantly associated with proliferation genes. With the availability of RNA-seq gene expression data for thousands of human cancer patients, we have analyzed RNA-seq and clinical data of cancer patients and constructed gene correlation networks specific to individual cancer patients.

Discovering protein-binding RNA motifs with a generative model of RNA sequences.

Recent advances in high-throughput experimental technologies have generated a huge amount of data on interactions between proteins and nucleic acids. Motivated by the big experimental data, several computational methods have been developed either to predict binding sites in a sequence or to determine if an interaction exists between protein and nucleic acid sequences. However, most of the methods cannot be used to discover new nucleic acid sequences that bind to a target protein because they are classifiers rather than generators.

A generative model for constructing nucleic acid sequences binding to a protein.

Background: Interactions between protein and nucleic acid molecules are essential to a variety of cellular processes. A large amount of interaction data generated by high-throughput technologies have triggered the development of several computational methods either to predict binding sites in a sequence or to determine whether a pair of sequences interacts or not. Most of these methods treat the problem of the interaction of nucleic acids with proteins as a classification problem rather than a generation problem.

Finding prognostic gene pairs for cancer from patient-specific gene networks.

Background: Molecular characterization of individual cancer patients is important because cancer is a complex and heterogeneous disease with many possible genetic and environmental causes. Many studies have been conducted to identify diagnostic or prognostic gene signatures for cancer from gene expression profiles. However, some gene signatures may fail to serve as diagnostic or prognostic biomarkers and gene signatures may not be found in gene expression profiles.

A generalized approach to predicting protein-protein interactions between virus and host.

Background: Viral infection involves a large number of protein-protein interactions (PPIs) between virus and its host. These interactions range from the initial binding of viral coat proteins to host membrane receptor to the hijacking the host transcription machinery by viral proteins. Therefore, identifying PPIs between virus and its host helps understand the mechanism of viral infections and design antiviral drugs.

Sequence-based prediction of putative transcription factor binding sites in DNA sequences of any length.

A transcription factor (TF) is a protein that regulates gene expression by binding to specific DNA sequences. Despite the recent advances in experimental techniques for identifying transcription factor binding sites (TFBS) in DNA sequences, a large number of TFBS are to be unveiled in many species. Several computational methods developed for predicting TFBS in DNA are tissue- or species-specific methods, so cannot be used without prior knowledge of tissue or species.

Predicting Interactions between Virus and Host Proteins Using Repeat Patterns and Composition of Amino Acids.

Previous methods for predicting protein-protein interactions (PPIs) were mainly focused on PPIs within a single species, but PPIs across different species have recently emerged as an important issue in some areas such as viral infection. The primary focus of this study is to predict PPIs between virus and its targeted host, which are involved in viral infection. We developed a general method that predicts interactions between virus and host proteins using the repeat patterns and composition of amino acids.

WIG1 is crucial for AGO2-mediated ACOT7 mRNA silencing via miRNA-dependent and -independent mechanisms.

RNA-binding proteins (RBPs) are involved in mRNA splicing, maturation, transport, translation, storage and turnover. Here, we identified ACOT7 mRNA as a novel target of human WIG1. ACOT7 mRNA decay was triggered by the microRNA miR-9 in a WIG1-dependent manner via classic recruitment of Argonaute 2 (AGO2).

Predicting protein-binding regions in RNA using nucleotide profiles and compositions.

Background: Motivated by the increased amount of data on protein-RNA interactions and the availability of complete genome sequences of several organisms, many computational methods have been proposed to predict binding sites in protein-RNA interactions. However, most computational methods are limited to finding RNA-binding sites in proteins instead of protein-binding sites in RNAs. Predicting protein-binding sites in RNA is more challenging than predicting RNA-binding sites in proteins.

Data of protein-RNA binding sites.

Despite the increasing number of protein-RNA complexes in structure databases, few data resources have been made available which can be readily used in developing or testing a method for predicting either protein-binding sites in RNA sequences or RNA-binding sites in protein sequences. The problem of predicting protein-binding sites in RNA has received much less attention than the problem of predicting RNA-binding sites in protein. The data presented in this paper are related to the article entitled "PRIdictor: Protein-RNA Interaction predictor" (Tuvshinjargal et al.

PRIdictor: Protein-RNA Interaction predictor.

Several computational methods have been developed to predict RNA-binding sites in protein, but its inverse problem (i.e., predicting protein-binding sites in RNA) has received much less attention.

Predicting protein-binding RNA nucleotides with consideration of binding partners.

In recent years several computational methods have been developed to predict RNA-binding sites in protein. Most of these methods do not consider interacting partners of a protein, so they predict the same RNA-binding sites for a given protein sequence even if the protein binds to different RNAs. Unlike the problem of predicting RNA-binding sites in protein, the problem of predicting protein-binding sites in RNA has received little attention mainly because it is much more difficult and shows a lower accuracy on average.

PNImodeler: web server for inferring protein-binding nucleotides from sequence data.

Background: Interactions between DNA and proteins are essential to many biological processes such as transcriptional regulation and DNA replication. With the increased availability of structures of protein-DNA complexes, several computational studies have been conducted to predict DNA binding sites in proteins. However, little attempt has been made to predict protein binding sites in DNA.

DBBP: database of binding pairs in protein-nucleic acid interactions.

Background: Interaction of proteins with other molecules plays an important role in many biological activities. As many structures of protein-DNA complexes and protein-RNA complexes have been determined in the past years, several databases have been constructed to provide structure data of the complexes. However, the information on the binding sites between proteins and nucleic acids is not readily available from the structure data since the data consists mostly of the three-dimensional coordinates of the atoms in the complexes.

Sequence-based prediction of protein-binding sites in DNA: comparative study of two SVM models.

As many structures of protein-DNA complexes have been known in the past years, several computational methods have been developed to predict DNA-binding sites in proteins. However, its inverse problem (i.e.

Classification of diffusion tensor images for the early detection of Alzheimer's disease.

Early detection of Alzheimer's disease (AD) is important since treatments are more efficacious when used at the beginning of the disease. Despite significant advances in diagnostic methods for AD, there is no single diagnostic method for AD with high accuracy. We developed a support vector machine (SVM) model that classifies mild cognitive impairment (MCI) and normal control subjects using probabilistic tractography and tract-based spatial statistics of diffusion tensor imaging (DTI) data.

Modeling the interactions of Alzheimer-related genes from the whole brain microarray data and diffusion tensor images of human brain.

Background: In recent years the genome-wide microarray-based gene expression profiles and diffusion tensor images (DTI) in human brain have been made available with accompanying anatomic and histology data. The challenge is to integrate various types of data to investigate the interactions of genes that are associated with specific neurological disorder.

Results: In this study, we analyzed the whole brain microarray data and the physical connectivity of the hippocampus with other brain regions to identify the genes related to Alzheimer's disease and their interactions with proteins.