Evolutionary Medicine and the Medical School Curriculum: Meeting Students Along Their Paths to Medical School

Over the past several years, numerous reports that have been independently prepared by prestigious organizations in the U.S. have agreed that new approaches to improve teaching and learning of biology at both the pre-college and undergraduate levels are important and timely. Their recommendations, which are based on emerging research about human learning and cognition, are in agreement that evolution is an organizing principle and foundation of modern biology and should be presented as such. This paper provides an overview of the conclusions and recommendations from those reports and proposes that helping students learn evolution through the lens of human examples that are part of the emerging field of evolutionary medicine could help biology educators improve the teaching of evolution, and biology more generally, by asking students to address biological problems that are inherently interesting and motivating.     

A Ranking Approach to Genomic Selection

Background

Genomic selection (GS) is a recent selective breeding method which uses predictive models based on whole-genome molecular markers. Until now, existing studies formulated GS as the problem of modeling an individual’s breeding value for a particular trait of interest, i.e., as a regression problem. To assess predictive accuracy of the model, the Pearson correlation between observed and predicted trait values was used.

Contributions

In this paper, we propose to formulate GS as the problem of ranking individuals according to their breeding value. Our proposed framework allows us to employ machine learning methods for ranking which had previously not been considered in the GS literature. To assess ranking accuracy of a model, we introduce a new measure originating from the information retrieval literature called normalized discounted cumulative gain (NDCG). NDCG rewards more strongly models which assign a high rank to individuals with high breeding value. Therefore, NDCG reflects a prerequisite objective in selective breeding: accurate selection of individuals with high breeding value.

Results

We conducted a comparison of 10 existing regression methods and 3 new ranking methods on 6 datasets, consisting of 4 plant species and 25 traits. Our experimental results suggest that tree-based ensemble methods including McRank, Random Forests and Gradient Boosting Regression Trees achieve excellent ranking accuracy. RKHS regression and RankSVM also achieve good accuracy when used with an RBF kernel. Traditional regression methods such as Bayesian lasso, wBSR and BayesC were found less suitable for ranking. Pearson correlation was found to correlate poorly with NDCG. Our study suggests two important messages. First, ranking methods are a promising research direction in GS. Second, NDCG can be a useful evaluation measure for GS.     

A Decision Analytic Approach to Exposure-Based Chemical Prioritization

The manufacture of novel synthetic chemicals has increased in volume and variety, but often the environmental and health risks are not fully understood in terms of toxicity and, in particular, exposure. While efforts to assess risks have generally been effective when sufficient data are available, the hazard and exposure data necessary to assess risks adequately are unavailable for the vast majority of chemicals in commerce. The US Environmental Protection Agency has initiated the ExpoCast Program to develop tools for rapid chemical evaluation based on potential for exposure. In this context, a model is presented in which chemicals are evaluated based on inherent chemical properties and behaviorally-based usage characteristics over the chemical’s life cycle. These criteria are assessed and integrated within a decision analytic framework, facilitating rapid assessment and prioritization for future targeted testing and systems modeling. A case study outlines the prioritization process using 51 chemicals. The results show a preliminary relative ranking of chemicals based on exposure potential. The strength of this approach is the ability to integrate relevant statistical and mechanistic data with expert judgment, allowing for an initial tier assessment that can further inform targeted testing and risk management strategies.     

Modes of Admission to Hospital: A Survey of Emergency Admissions to a General Medical Unit

In a survey of the modes of referral and disposal of “acute patients” to a general medical unit during the period 1 February 1968 to 31 July 1970 only 1,432 out of 3,455 were referred by general practitioners. There was a high incidence of self-referral to hospital, and this trend was on the increase. A large percentage of self-referred patients came from their homes, and 65-77% of these arrived by ambulance ordered by themselves.     

Simultaneous Selection of Extreme Populations: A Subset Selection Approach

The problem of selecting a “best” (largest mean, or smallest mean) population from a collection of k independent populations was formulated and solved by Bechhofer (1954). Gupta (1965) solved another important problem, that of selecting a subset of populations containing the “best” population from the original collection of populations. Since then many variations of the problem have been considered. Tong (1969) and Lewis (1980) have investigated the problem of selecting extreme populations (populations with a largest, and populations with a smallest, mean) with respect to one and two standard populations, respectively. In this paper we study the selection of extreme populations in absence of any standard population. We formulate subset-selection procedures when variances are known and equal, and also in the most general case when they are unknown and unequal. Nonexistence of a single-stage procedure is noted for this latter case (even if variances are equal). A two-stage procedure and some of its associated properties are discussed. Tables needed for application are provided, as is a worked example.     

Decision Making under Uncertainty: A Quasimetric Approach

We propose a new approach for solving a class of discrete decision making problems under uncertainty with positive cost. This issue concerns multiple and diverse fields such as engineering, economics, artificial intelligence, cognitive science and many others. Basically, an agent has to choose a single or series of actions from a set of options, without knowing for sure their consequences. Schematically, two main approaches have been followed: either the agent learns which option is the correct one to choose in a given situation by trial and error, or the agent already has some knowledge on the possible consequences of his decisions; this knowledge being generally expressed as a conditional probability distribution. In the latter case, several optimal or suboptimal methods have been proposed to exploit this uncertain knowledge in various contexts. In this work, we propose following a different approach, based on the geometric intuition of distance. More precisely, we define a goal independent quasimetric structure on the state space, taking into account both cost function and transition probability. We then compare precision and computation time with classical approaches.     

Prenatal Diagnosis: A Directive Approach to Genetic Counseling Using Decision Analysis

The decision which prospective parents face concerning mid-trimester amniocentesis for prenatal diagnosis was examined by decision analysis. The prospective parents'' decision depends on the likelihood of the birth of a child affected by a genetic disorder, the risk of amniocentesis, and the probability that the diagnoses provided by the amniocentesis will be correct. The couple''s decision must also depend on their attitudes toward each possible outcome. The likelihoods of the outcomes can be obtained from appropriate medical consultation, while the relative costs or burdens of the outcomes should be obtained from the prospective parents. A truly informed decision for this couple can then be formulated from these probabilities and values, thus allowing genetic counseling to be more directive. The technique is illustrated for the prenatal diagnosis of Down''s syndrome, meningomyelocele, and Duchenne muscular dystrophy.     

Equivalence or Selection: A Distribution-Free Approach

Using a distribution-free approach, a modification of the usual procedure for selecting the better of two treatments is presented. Here the possibility of no selection when the treatments appear to be ‘equivalent’ is allowed. The sample size and the constant needed to implement the proposed procedure are determined by controlling the probabilities of a correct selection and a wrong selection when the two treatments are not equivalent.