Gene-based approach to human gene-phenotype correlations

Elucidating the genetic basis of human phenotypes is a major goal of contemporary geneticists. Logically, two fundamental and contrasting approaches are available, one that begins with a phenotype and concludes with the identification of a responsible gene or genes; the other that begins with a gene and works toward identifying one or more phenotypes resulting from allelic variation of it. This paper provides a conceptual overview of phenotype-based vs. gene-based procedures with emphasis on gene-based methods. A key feature of a gene-based approach is that laboratory effort first is devoted to developing an assay for mutations in the gene under regard; the assay then is applied to the evaluation of large numbers of unrelated individuals with a variety of phenotypes that are deemed potentially resulting from alleles at the gene. No effort is directed toward chromosomally mapping the loci responsible for the phenotypes scanned. Example is made of my laboratory’s successful use of a gene-based approach to identify genes causing hereditary diseases of the retina such as retinitis pigmentosa. Reductions in the cost and improvements in the speed of scanning individuals for DNA sequence anomalies may make a gene-based approach an efficient alternative to phenotype-based approaches to correlating genes with phenotypes.

The Patient Informatics Consult Service (PICS): an approach for a patient-centered service

The Patient Informatics Consult Service (PICS) at the Eskind Biomedical Library at Vanderbilt University Medical Center (VUMC) provides patients with consumer-friendly information by using an information prescription mechanism. Clinicians refer patients to the PICS by completing the prescription and noting the patient's condition and any relevant factors. In response, PICS librarians critically appraise and summarize consumer-friendly materials into a targeted information report. Copies of the report are given to both patient and clinician, thus facilitating doctor-patient communication and closing the clinician-librarian feedback loop. Moreover, the prescription form also circumvents many of the usual barriers for patients in locating information, namely, patients' unfamiliarity with medical terminology and lack of knowledge of authoritative sources. PICS librarians capture the time and expertise put into these reports by creating Web-based pathfinders on prescription topics. Pathfinders contain librarian-created disease overviews and links to authoritative resources and seek to minimize the consumer's exposure to unreliable information. Pathfinders also adhere to strict guidelines that act as a model for locating, appraising, and summarizing information for consumers. These mechanisms—the information prescription, research reports, and pathfinders—serve as steps toward the long-term goal of full integration of consumer health information into patient care at VUMC.

Structure-activity relationships derived by machine learning: the use of atoms and their bond connectivities to predict mutagenicity by inductive logic programming.

We present a general approach to forming structure-activity relationships (SARs). This approach is based on representing chemical structure by atoms and their bond connectivities in combination with the inductive logic programming (ILP) algorithm PROGOL. Existing SAR methods describe chemical structure by using attributes which are general properties of an object. It is not possible to map chemical structure directly to attribute-based descriptions, as such descriptions have no internal organization. A more natural and general way to describe chemical structure is to use a relational description, where the internal construction of the description maps that of the object described. Our atom and bond connectivities representation is a relational description. ILP algorithms can form SARs with relational descriptions. We have tested the relational approach by investigating the SARs of 230 aromatic and heteroaromatic nitro compounds. These compounds had been split previously into two subsets, 188 compounds that were amenable to regression and 42 that were not. For the 188 compounds, a SAR was found that was as accurate as the best statistical or neural network-generated SARs. The PROGOL SAR has the advantages that it did not need the use of any indicator variables handcrafted by an expert, and the generated rules were easily comprehensible. For the 42 compounds, PROGOL formed a SAR that was significantly (P < 0.025) more accurate than linear regression, quadratic regression, and back-propagation. This SAR is based on an automatically generated structural alert for mutagenicity.