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Objectives Pre-operative exercise may improve functional outcomes for lung cancer patients, but barriers associated with cost, resources, and burden make it challenging to deliver pre-operative exercise programs. The goal of this proof-of-concept study was to determine level of moderate-vigorous physical activity (MVPA) and change in aerobic capacity after participation in a home-based pre-operative exercise intervention. Materials and methods Eighteen patients scheduled for surgery for suspected stage I-III lung cancer received an exercise prescription from their surgeon and wore a commercially-available device that tracked their daily MVPA throughout the pre-operative period. Descriptive statistics were used to calculate adherence to the exercise prescription. A one-sample t-test was used to explore change in aerobic capacity from baseline to the day of surgery. Results Participants exhibited a mean of 20.4 (sd = 46.2) minutes of MVPA per day during the pre-operative period. On average, the sample met the goal of 30 min of MVPA on 16.4% of the days during the pre-operative period. The mean distance achieved at baseline for the 6-min walk test was 456.7 m (sd = 72.9), which increased to 471.1 m (sd = 88.4) on the day of surgery. This equates to a mean improvement of 13.8 m (sd = 37.0), but this difference was not statistically different from zero (p = 0.14). Eight of the 17 participants (47%) demonstrated a clinically significant improvement of 14 m or more. Conclusion A surgeon-delivered exercise prescription plus an activity tracker may promote clinically significant improvement in aerobic capacity and MVPA engagement among patients with lung cancer during the pre-operative period, but may need to be augmented with more contact with and support from practitioners over time to maximize benefits. Trial registration The study protocol was registered with ClinicalTrials.gov prior to initiating participant recruitment (NCT03162718).

Objective The purpose of this study was to explore the feasibility, acceptability and perceived utility of the provision of a wearable fitness device and an exercise prescription from a surgeon, prior to surgery for lung cancer. Methods A single-arm, pre-post feasibility study was conducted with 30 participants scheduled for surgery to treat stage I, II or III lung cancer. Participants were given a Garmin Vivoactive HR device and a prescription for 150 min of moderately to vigorous exercise per week. Participants completed assessments on four occasions and completed a semi-structured interview on two occasions. Descriptive statistics were used to assess the feasibility and acceptability of study procedures, including synchronising the Garmin device and engaging in study assessments. Results Seventy-nine per cent of enrolled participants completed the pre-operative study activities. Seventy-one per cent of enrolled participants successfully synchronised their device during the pre-operative period. Data were transmitted from the device to the study team for an average of 70% of the pre-operative days. Conclusion This pilot study demonstrated the feasibility and acceptability of a pre-operative exercise program for patients scheduled to undergo surgery for lung cancer. Trial registration The study protocol was registered with ClinicalTrials.gov prior to the initiation of participant recruitment (NCT03162718).

The majority of accepted papers in computational biology and biocomputing describe new software approaches to relevant biological problems. While journals and conferences often require the availability of software and source code, there are limited resources available to maximize the distribution and use of developed software within the scientific community. The accepted standard is to make source code available for new approaches in published work, the growing problem of system configuration issues, language, library version conflicts, and other implementation issues often impede the broad distribution, availability of software tools, and reproducibility of research. There are a variety of solutions to these implementation issues, but the learning curve for applying these solutions is steep. This tutorial demonstrates tools and approaches for packaging and distribution of published code, and provides methodological practices for the broad and open sharing of new biocomputing software.

The analysis of large biomedical data often presents with various challenges related to not just the size of the data, but also to data quality issues such as heterogeneity, multidimensionality, noisiness, and incompleteness of the data. The data-intensive nature of computational genomics problems in biomedical informatics warrants the development and use of massive computer infrastructure and advanced software tools and platforms, including but not limited to the use of cloud computing. Our session aims to address these challenges in handling big data for designing a study, performing analysis, and interpreting outcomes of these analyses. These challenges have been prevalent in many studies including those which focus on the identification of novel genetic variant-phenotype associations using data from sources like Electronic Health Records (EHRs) or multi-omic data. One of the biggest challenges to focus on is the imperfect nature of the biomedical data where a lot of noise and sparseness is observed. In our session, we will present research articles that can help in identifying innovative ways to recognize and overcome newly arising challenges associated with pattern recognition in biomedical data.

Methods to identify genes or pathways associated with complex diseases are often inadequate to elucidate most risk because they make implicit and oversimplified assumptions about underlying models of disease etiology. These can lead to incomplete or inadequate conclusions. To address this, we previously developed human phenotype networks (HPN), linking phenotypes based on shared biology. However, such visualization alone is often uninterpretable, and requires additional filtering. Here, we expand the HPN to include another method, evolutionary triangulation (ET). ET utilizes the hypothesis that alleles affecting disease risk in multiple populations are distributed consistently with differences in disease prevalence and compares allele frequencies among populations and their relationship to phenotype prevalence. We hypothesized that combining these methods will increase our ability to detect genetic patterns of association in complex diseases. We combined HPN and ET to identify network patterns associated with type 2 diabetes mellitus (T2DM), a leading cause of death worldwide. Fasting glucose, a continuous trait, was used as a proxy for T2DM and differs significantly among continental populations. The combined method identified several diabetes-related traits and several phenotypes related to cardiovascular diseases, for which diabetes is a major risk factor. ET-HPN found more phenotypes related to our target and related phenotypes than the application of either method alone. Not only could we detect phenotype connections related to T2DM, but we also identified phenotypes that are distributed in parallel to it, e.g., amyotrophic lateral sclerosis. Our analyses showed that ET-filtered HPN provides information that neither technique can individually.

The study of complex networks, and in particular of social networks, has mostly concentrated on relational networks, abstracting the distance between nodes. Spatial networks are, however, extremely relevant in our daily lives, and a large body of research exists to show that the distances between nodes greatly influence the cost and probability of establishing and maintaining a link. A random geometric graph (RGG) is the main type of synthetic network model used to mimic the statistical properties and behavior of many social networks. We propose a model, called REDS, that extends energy-constrained RGGs to account for the synergic effect of sharing the cost of a link with our neighbors, as is observed in real relational networks. We apply both the standard Watts-Strogatz rewiring procedure and another method that conserves the degree distribution of the network. The second technique was developed to eliminate unwanted forms of spatial correlation between the degree of nodes that are affected by rewiring, limiting the effect on other properties such as clustering and assortativity. We analyze both the statistical properties of these two network types and their epidemiological behavior when used as a substrate for a standard susceptible-infected-susceptible compartmental model. We consider and discuss the differences in properties and behavior between RGGs and REDS as rewiring increases and as infection parameters are changed. We report considerable differences both between the network types and, in the case of REDS, between the two rewiring schemes. We conclude that REDS represent, with the application of these rewiring mechanisms, extremely useful and interesting tools in the study of social and epidemiological phenomena in synthetic complex networks.

Given the exponential growth of biomedical data, researchers are faced with numerous challenges in extracting and interpreting information from these large, high-dimensional, incomplete, and often noisy data. To facilitate addressing this growing concern, the "Patterns in Biomedical Data-How do we find them?" session of the 2017 Pacific Symposium on Biocomputing (PSB) is devoted to exploring pattern recognition using data-driven approaches for biomedical and precision medicine applications. The papers selected for this session focus on novel machine learning techniques as well as applications of established methods to heterogeneous data. We also feature manuscripts aimed at addressing the current challenges associated with the analysis of biomedical data.

Genome‐wide association studies (GWAS) have led to the discovery of over 200 single nucleotide polymorphisms (SNPs) associated with type 2 diabetes mellitus (T2DM). Additionally, East Asians develop T2DM at a higher rate, younger age, and lower body mass index than their European ancestry counterparts. The reason behind this occurrence remains elusive. With comprehensive searches through the National Human Genome Research Institute (NHGRI) GWAS catalog literature, we compiled a database of 2,800 ancestry‐specific SNPs associated with T2DM and 70 other related traits. Manual data extraction was necessary because the GWAS catalog reports statistics such as odds ratio and P‐value, but does not consistently include ancestry information. Currently, many statistics are derived by combining initial and replication samples from study populations of mixed ancestry. Analysis of all‐inclusive data can be misleading, as not all SNPs are transferable across diverse populations. We used ancestry data to construct ancestry‐specific human phenotype networks (HPN) centered on T2DM. Quantitative and visual analysis of network models reveal the genetic disparities between ancestry groups. Of the 27 phenotypes in the East Asian HPN, six phenotypes were unique to the network, revealing the underlying ancestry‐specific nature of some SNPs associated with T2DM. We studied the relationship between T2DM and five phenotypes unique to the East Asian HPN to generate new interaction hypotheses in a clinical context. The genetic differences found in our ancestry‐specific HPNs suggest different pathways are involved in the pathogenesis of T2DM among different populations. Our study underlines the importance of ancestry in the development of T2DM and its implications in pharmocogenetics and personalized medicine.

Cellular automata (CAs) in their original form are laid out on regular structures such as rings or lattices. An unsophisticated evolutionary algorithm applied to the underlying structure of the CA's connectivity is capable to significantly improve its performance solving non-trivial tasks. In this work, we study the network properties that emerge in CAs with evolving topology for the density classification problem. We compare a simple rewiring mutation operator to a more sophisticated one that allows an increase in connectivity. We also analyze the effect of initial structure in the CAs before evolution, working over the entire spectrum of regular, irregular, and random networks. We conclude that, unsurprisingly, an increase in connectivity is the driver of fitness. This also result in an increase in the clustering coefficient, and decrease in assortativity. However, our study shows that artificial evolution can also achieve high fitness in CAs with constant degree by creating shortcuts through the network, lowing the characteristic path length, and keeping the assortativity and clustering coefficient constant.

Networks are central to turning the colossal amount of information generated by high-throughput genetic technology into manageable sources of knowledge. They are an intuitive way of representing interaction data, yet they offer a full set of sophisticated quantitative tools to analyze the phenomena they model. When combining genetic information, diseases, and phenotypic traits, networks can reveal and facilitate the analysis of pleiotropic and epistatic effects at the genome-wide scale. Genome-wide association study data is publicly available, and so are gene and pathway databases, and many more, making the global overview next to impossible. Networks allow information from these multiple sources to be encompassed. We use connections between the strata of the network to characterize pleiotropy and epistasis effects taking place between traits and biological pathways. The global graph-theory-based quantitative methods reveal that levels of pleiotropy and epistasis are in-line with theoretical expectations. The results of the magnified "glaucoma" region of the network confirm the existence of well-documented interactions, supported by overlapping genes and biological pathways and more obscure associations. They have the potential to generate new hypotheses for yet uncharacterized interactions. As the amount and complexity of genetic data increase, bipartite and, more generally, multipartite networks that combine human diseases and other physical attributes with layers of genetic information have the potential to become ubiquitous tools in the study of complex genetic, phenotypic interactions, and possibly improve personalized medicine.

Networks are central to turning the colossal amount of information generated by high-throughput genetic technology into manageable sources of knowledge. They are an intuitive way of representing interaction data, yet they offer a full set of sophisticated quantitative tools to analyze the phenomena they model. When combining genetic information, diseases, and phenotypic traits, networks can reveal and facilitate the analysis of pleiotropic and epistatic effects at the genome-wide scale. Genome-wide association study data is publicly available, and so are gene and pathway databases, and many more, making the global overview next to impossible. Networks allow information from these multiple sources to be encompassed. We use connections between the strata of the network to characterize pleiotropy and epistasis effects taking place between traits and biological pathways. The global graph-theory-based quantitative methods reveal that levels of pleiotropy and epistasis are in-line with theoretical expectations. The results of the magnified "glaucoma" region of the network confirm the existence of well-documented interactions, supported by overlapping genes and biological pathways and more obscure associations. They have the potential to generate new hypotheses for yet uncharacterized interactions. As the amount and complexity of genetic data increase, bipartite and, more generally, multipartite networks that combine human diseases and other physical attributes with layers of genetic information have the potential to become ubiquitous tools in the study of complex genetic, phenotypic interactions, and possibly improve personalized medicine.

Environmental exposure is a key factor of understanding health and diseases. Beyond genetic propensities, many disorders are, in part, caused by human interaction with harmful substances in the water, the soil, or the air. Limited data is available on a disease or substance basis. However, we compile a global repository from literature surveys matching environmental chemical substances exposure with human disorders. We build a bipartite network linking 60 substances to over 150 disease phenotypes. We quantitatively and qualitatively analyze the network and its projections as simple networks. We identify mercury, lead and cadmium as associated with the largest number of disorders. Symmetrically, we show that breast cancer, harm to the fetus and non-Hodgkin's lymphoma are associated with the most environmental chemicals. We conduct statistical analysis of how vertices with similar characteristics form the network interactions. This dyadicity and heterophilicity measures the tendencies of vertices with similar properties to either connect to one-another. We study the dyadic distribution of the substance classes in the networks show that, for instance, tobacco smoke compounds, parabens and heavy metals tend to be connected, which hint at common disease causing factors, whereas fungicides and phytoestrogens do not. We build an exposure network at the systems level. The information gathered in this study is meant to be complementary to the genome and help us understand complex diseases, their commonalities, their causes, and how to prevent and treat them.