The sequence comparison index: A novel method for comparing proteins and proteomes

Historically morphological features were used as the primary means to classify organisms. However, the age of molecular genetics has allowed us to approach this field from the perspective of the organism's genetic code. Early work used highly conserved sequences, such as ribosomal RNA. The increasing number of complete genomes in the public data repositories provides the opportunity to look not only at a single gene, but at organisms' entire parts list. ^ Here the Sequence Comparison Index (SCI) and the Organism Comparison Index (OCI), algorithms and methods to compare proteins and proteomes, are presented. The complete proteomes of 104 sequenced organisms were compared. Over 280 million full Smith-Waterman alignments were performed on sequence pairs which had a reasonable expectation of being related. From these alignments a whole proteome phylogenetic tree was constructed. This method was also used to compare the small subunit (SSU) rRNA from each organism and a tree constructed from these results. The SSU rRNA tree by the SCI/OCI method looks very much like accepted SSU rRNA trees from sources such as the Ribosomal Database Project, thus validating the method. The SCI/OCI proteome tree showed a number of small but significant differences when compared to the SSU rRNA tree and proteome trees constructed by other methods. Horizontal gene transfer does not appear to affect the SCI/OCI trees until the transferred genes make up a large portion of the proteome. ^ As part of this work, the Database of Related Local Alignments (DaRLA) was created and contains over 81 million rows of sequence alignment information. DaRLA, while primarily used to build the whole proteome trees, can also be applied shared gene content analysis, gene order analysis, and creating individual protein trees. ^ Finally, the standard BLAST method for analyzing shared gene content was compared to the SCI method using 4 spirochetes. The SCI system performed flawlessly, finding all proteins from one organism against itself and finding all the ribosomal proteins between organisms. The BLAST system missed some proteins from its respective organism and failed to detect small ribosomal proteins between organisms. ^

Risk Index Score (RISc)

The main objective of this study was to develop and validate a computer-based statistical algorithm based on a multivariable logistic model that can be translated into a simple scoring system in order to ascertain stroke cases using hospital admission medical records data. This algorithm, the Risk Index Score (RISc), was developed using data collected prospectively by the Brain Attack Surveillance in Corpus Christ (BASIC) project. The validity of the RISc was evaluated by estimating the concordance of scoring system stroke ascertainment to stroke ascertainment accomplished by physician review of hospital admission records. The goal of this study was to develop a rapid, simple, efficient, and accurate method to ascertain the incidence of stroke from routine hospital admission hospital admission records for epidemiologic investigations. ^ The main objectives of this study were to develop and validate a computer-based statistical algorithm based on a multivariable logistic model that could be translated into a simple scoring system to ascertain stroke cases using hospital admission medical records data. (Abstract shortened by UMI.)^

Biomedical informatics applications for epilepsy management: A systematic review

Epilepsy is a very complex disease which can have a variety of etiologies, co-morbidities, and a long list of psychosocial factors4. Clinical management of epilepsy patients typically includes serological tests, EEG's, and imaging studies to determine the single best antiepileptic drug (AED). Self-management is a vital component of achieving optimal health when living with a chronic disease. For patients with epilepsy self-management includes any necessary actions to control seizures and cope with any subsequent effects of the condition9; including aspects of treatment, seizure, and lifestyle. The use of computer-based applications can allow for more effective use of clinic visits and ultimately enhance the patient-provider relationship through focused discussion of determinants affecting self-management. ^ The purpose of this study is to conduct a systematic literature review on informatics application in epilepsy self-management in an effort to describe current evidence for informatics applications and decision support as an adjunct to successful clinical management of epilepsy. Each publication was analyzed for the type of study design utilized. ^ A total of 68 publications were included and categorized by the study design used, development stage, and clinical domain. Descriptive study designs comprised of three-fourths of the publications and indicate an underwhelming use of prospective studies. The vast majority of prospective studies also focused on clinician use to increase knowledge in treating patients with epilepsy. ^ Due to the chronic nature of epilepsy and the difficulty that both clinicians and patients can experience in managing epilepsy, more prospective studies are needed to evaluate applications that can effectively increase management activities. Within the last two decades of epilepsy research, management studies have employed the use of biomedical informatics applications. While the use of computer applications to manage epilepsy has increased, more progress is needed.^