Friendship and Natural Selection

More than any other species, humans form social ties to individuals who are neither kin nor mates, and these ties tend to be with similar people. Here, we show that this similarity extends to genotypes. Across the whole genome, friends' genotypes at the SNP level tend to be positively correlated (homophilic); however, certain genotypes are negatively correlated (heterophilic). A focused gene set analysis suggests that some of the overall correlation can be explained by specific systems; for example, an olfactory gene set is homophilic and an immune system gene set is heterophilic. Finally, homophilic genotypes exhibit significantly higher measures of positive selection, suggesting that, on average, they may yield a synergistic fitness advantage that has been helping to drive recent human evolution

Can We Count on Counting? An Analysis of the Validity of Community Engagement Survey Measures

Researchers use various survey efforts to understand students’ community engagement experiences. Among the crucial pieces of information for both academic and applied research is the extent to which (or whether or not) students participate in community engagement activities. However, recent studies have questioned the validity of many college student survey items. This paper describes an exploratory study that sought to investigate the validity of several survey items related to students’ community engagement participation. The study found that large percentages of students who have taken community-based learning courses do not accurately report these experiences on student surveys and examines what factors relate to misreporting. Implications for future community engagement research are discussed.

Identifying driver mutations in sequenced cancer genomes: computational approaches to enable precision medicine

High-throughput DNA sequencing is revolutionizing the study of cancer and enabling the measurement of the somatic mutations that drive cancer development. However, the resulting sequencing datasets are large and complex, obscuring the clinically important mutations in a background of errors, noise, and random mutations. Here, we review computational approaches to identify somatic mutations in cancer genome sequences and to distinguish the driver mutations that are responsible for cancer from random, passenger mutations. First, we describe approaches to detect somatic mutations from high-throughput DNA sequencing data, particularly for tumor samples that comprise heterogeneous populations of cells. Next, we review computational approaches that aim to predict driver mutations according to their frequency of occurrence in a cohort of samples, or according to their predicted functional impact on protein sequence or structure. Finally, we review techniques to identify recurrent combinations of somatic mutations, including approaches that examine mutations in known pathways or protein-interaction networks, as well as de novo approaches that identify combinations of mutations according to statistical patterns of mutual exclusivity. These techniques, coupled with advances in high-throughput DNA sequencing, are enabling precision medicine approaches to the diagnosis and treatment of cancer

Racial categories in machine learning

Controversies around race and machine learning have sparked debate among computer scientists over how to design machine learning systems that guarantee fairness. These debates rarely engage with how racial identity is embedded in our social experience, making for sociological and psychological complexity. This complexity challenges the paradigm of considering fairness to be a formal property of supervised learning with respect to protected personal attributes. Racial identity is not simply a personal subjective quality. For people labeled "Black" it is an ascribed political category that has consequences for social differentiation embedded in systemic patterns of social inequality achieved through both social and spatial segregation. In the United States, racial classification can best be understood as a system of inherently unequal status categories that places whites as the most privileged category while signifying the Negro/black category as stigmatized. Social stigma is reinforced through the unequal distribution of societal rewards and goods along racial lines that is reinforced by state, corporate, and civic institutions and practices. This creates a dilemma for society and designers: be blind to racial group disparities and thereby reify racialized social inequality by no longer measuring systemic inequality, or be conscious of racial categories in a way that itself reifies race. We propose a third option. By preceding group fairness interventions with unsupervised learning to dynamically detect patterns of segregation, machine learning systems can mitigate the root cause of social disparities, social segregation and stratification, without further anchoring status categories of disadvantage

Application of naïve Bayesian artificial intelligence to referral refinement of chronic open angle glaucoma

The purpose of this study was to determine whether naïve Bayesian artificial intelligence could accurately predict clinical decisions made during the referral refinement of Chronic open angle glaucoma (COAG) by three specialist independent prescribing optometrists using the highly structured standard operating procedure (SOP) adopted by the Community Ophthalmology Team (COT) of the West Kent Clinical Commissioning Group (CCG). The effectiveness of the COT, in terms of reducing false positive referrals and costs to the National Health Service (NHS), was also explored. This was the first study of its kind. Treating the study as a clinical audit allowed collection of unconsented fully anonymised data from the worst affected eyes or right eyes of 1006 cases referred into the COT. Each case was classified according to race, sex, age, family history of COAG, reason for referral, intraocular pressure and its inter-ocular asymmetry (Goldmann Applanation Tonometry), several optic nerve head dimensions (vertical size, cup disc ratio and its inter-ocular asymmetry; dilated stereoscopic slit lamp biomicroscopy with Volk lens), central corneal thickness (ultrasound pachymetry) and the severity of any visual field defects (Humphrey Visual Field Analyser, SITA FAST 24-2 testing strategy, Hodapp-Parrish-Anderson classification). Grouping of data into multiple cut-off points was informed by previous research and National Institute for Health and Care Excellence (NICE) guidelines. Preliminary analyses showed that most cases (79%) were discharged, 7% were followed up and 14% were referred to the NHS hospital eye service. The high discharge rate led to NHS cost savings of over £50 per case. Previous reports of increased intraocular pressure with central corneal thickness and increased cup disc ratios with cup disc size were also confirmed. Despite a high degree of inter-dependency between clinical tests, which violated the key assumption of naïve Bayesian analyses, the scheme learned rapidly and its weighted accuracy, based on randomised stratified tenfold cross-validation, was high (95%, 2.0% SD). However, false discharge (3.4%, 1.6% SD) and referral rates (3.1%, 1.5% SD) were considered unsafe. Making the analysis cost sensitive led to an 80 fold increase in COT follow-ups that would have reduced cost effectivity. The transferability of likelihood ratios was explored along with their use, compared to Chi-square, to rank clinical tests and explore redundancy in the SOP adopted by the COT. In summary, high discharge rates were consistent with the level of false positive referrals for suspected COAG reported in the literature and reduced NHS costs. Although use of a structured SOP led to high accuracy, naïve Bayesian artificial intelligence could not safely predict the decisions of COT optometrists as it caused too many false discharges and referrals. More sophisticated forms of machine learning need to be explored