Simplified method for predicting a functional class of proteins in transcription factor complexes.

Background: Initiation of transcription is essential for most of the cellular responses to environmental conditions and for cell and tissue specificity. This process is regulated through numerous proteins, their ligands and mutual interactions, as well as interactions with DNA. The key such regulatory proteins are transcription factors (TFs) and transcription co-factors (TcoFs).

PIMiner: a web tool for extraction of protein interactions from biomedical literature.

Information on Protein Interactions (Pls) is valuable for biomedical research, but often lies buried in the scientific literature and cannot be readily retrieved. While much progress has been made over the years in extracting Pls from the literature using computational methods, there is a lack of free, public, user-friendly tools for the discovery of Pls. We developed an online tool for the extraction of PI relationships from PubMed-abstracts, which we name PIMiner.

Predicting atrial fibrillation and flutter using electronic health records.

Electronic Health Records (EHR) contain large amounts of useful information that could potentially be used for building models for predicting onset of diseases. In this study, we have investigated the use of free-text and coded data in Marshfield Clinic's EHR, individually and in combination for building machine learning based models to predict the first ever episode of atrial fibrillation and/or atrial flutter (AFF). We trained and evaluated our AFF models on the EHR data across different time intervals (1, 3, 5 and all years) prior to first documented onset of AFF.

Dragon TIS Spotter: an Arabidopsis-derived predictor of translation initiation sites in plants.

Summary: In higher eukaryotes, the identification of translation initiation sites (TISs) has been focused on finding these signals in cDNA or mRNA sequences. Using Arabidopsis thaliana (A.t.

Context-specific protein network miner--an online system for exploring context-specific protein interaction networks from the literature.

Background: Protein interaction networks (PINs) specific within a particular context contain crucial information regarding many cellular biological processes. For example, PINs may include information on the type and directionality of interaction (e.g.

A database of annotated promoters of genes associated with common respiratory and related diseases.

Many genes have been implicated in the pathogenesis of common respiratory and related diseases (RRDs), yet the underlying mechanisms are largely unknown. Differential gene expression patterns in diseased and healthy individuals suggest that RRDs affect or are affected by modified transcription regulation programs. It is thus crucial to characterize implicated genes in terms of transcriptional regulation.

IMID: integrated molecular interaction database.

Motivation: Molecular interaction information, such as protein-protein interactions and protein-small molecule interactions, is indispensable for understanding the mechanism of biological processes and discovering treatments for diseases. Many databases have been built by manual annotation of literature to organize such information into structured form. However, most databases focus on only one type of interactions, which are often not well annotated and integrated with related functional information.

Dragon PolyA Spotter: predictor of poly(A) motifs within human genomic DNA sequences.

Motivation: Recognition of poly(A) signals in mRNA is relatively straightforward due to the presence of easily recognizable polyadenylic acid tail. However, the task of identifying poly(A) motifs in the primary genomic DNA sequence that correspond to poly(A) signals in mRNA is a far more challenging problem. Recognition of poly(A) signals is important for better gene annotation and understanding of the gene regulation mechanisms.

Integrated bio-entity network: a system for biological knowledge discovery.

A significant part of our biological knowledge is centered on relationships between biological entities (bio-entities) such as proteins, genes, small molecules, pathways, gene ontology (GO) terms and diseases. Accumulated at an increasing speed, the information on bio-entity relationships is archived in different forms at scattered places. Most of such information is buried in scientific literature as unstructured text.

Genome-wide analysis of regions similar to promoters of histone genes.

Background: The purpose of this study is to: i) develop a computational model of promoters of human histone-encoding genes (shortly histone genes), an important class of genes that participate in various critical cellular processes, ii) use the model so developed to identify regions across the human genome that have similar structure as promoters of histone genes; such regions could represent potential genomic regulatory regions, e.g. promoters, of genes that may be coregulated with histone genes, and iii/ identify in this way genes that have high likelihood of being coregulated with the histone genes.

Bayesian inference of protein-protein interactions from biological literature.

Motivation: Protein-protein interaction (PPI) extraction from published biological articles has attracted much attention because of the importance of protein interactions in biological processes. Despite significant progress, mining PPIs from literatures still rely heavily on time- and resource-consuming manual annotations.

Results: In this study, we developed a novel methodology based on Bayesian networks (BNs) for extracting PPI triplets (a PPI triplet consists of two protein names and the corresponding interaction word) from unstructured text.

Finding functional promoter motifs by computational methods: a word of caution.

The standard practice in the analysis of promoters is to select promoter regions of convenient length. This may lead to false results when searching for Transcription Factor Binding Sites (TFBSs), since the sequences may contain coding segments. In such cases, motif detection may single out motifs from the coding regions.

Computational promoter analysis of mouse, rat and human antimicrobial peptide-coding genes.

Background: Mammalian antimicrobial peptides (AMPs) are effectors of the innate immune response. A multitude of signals coming from pathways of mammalian pathogen/pattern recognition receptors and other proteins affect the expression of AMP-coding genes (AMPcgs). For many AMPcgs the promoter elements and transcription factors that control their tissue cell-specific expression have yet to be fully identified and characterized.

Dragon Promoter Mapper (DPM): a Bayesian framework for modelling promoter structures.

Unlabelled: Dragon Promoter Mapper (DPM) is a tool to model promoter structure of co-regulated genes using methodology of Bayesian networks. DPM exploits an exhaustive set of motif features (such as motif, its strand, the order of motif occurrence and mutual distance between the adjacent motifs) and generates models from the target promoter sequences, which may be used to (1) detect regions in a genomic sequence which are similar to the target promoters or (2) to classify other promoters as similar or not to the target promoter group. DPM can also be used for modelling of enhancers and silencers.

Promoter modeling: the case study of mammalian histone promoters.

Motivation: Histone proteins play important roles in chromosomal functions. They are significantly evolutionarily conserved across species, which suggests similarity in their transcription regulation. The abundance of experimental data on histone promoters provides an excellent background for the evaluation of computational methods.