Artificial Allies: Validation of Synthetic Text for Peer Support Tools through Data Augmentation in NLP Model Development.

This study investigates the potential of using synthetic text to augment training data for Natural Language Processing (NLP) models, specifically within the context of peer support tools. We surveyed 22 participants-13 professional peer supporters and 9 AI-proficient individuals-tasked with distinguishing between AI-generated and human-written sentences. Using signal detection theory and confidence-based metrics, we evaluated the accuracy and confidence levels of both groups.

Packaging Biocomputing Software to Maximize Distribution and Reuse.

The majority of publications in computational biology and biocomputing develop or apply software approaches to relevant biological problems to some degree. While journals and conferences often prompt authors to make their source code available, these are often only basic requirements. Investigators often wish their software and tools were widely usable to the scientific community, but there are limited resources available to maximize the distribution and provide easy use of developed software.

Potential effectiveness of a surgeon-delivered exercise prescription and an activity tracker on pre-operative exercise adherence and aerobic capacity of lung cancer patients.

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.

A feasibility study of an unsupervised, pre-operative exercise program for adults with lung cancer.

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.

Packaging Biocomputing Software to Maximize Distribution and Reuse.

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.

Session Introduction - Pattern Recognition in Biomedical Data: Challenges in putting big data to work.

The following sections are included:IntroductionReferences.

Infection dynamics on spatial small-world network models.

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.

Session Introduction: Challenges of Pattern Recognition in Biomedical Data.

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.

Evolutionarily derived networks to inform disease pathways.

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.

PATTERNS IN BIOMEDICAL DATA-HOW DO WE FIND THEM?

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.

Studying the Genetics of Complex Disease With Ancestry-Specific Human Phenotype Networks: The Case of Type 2 Diabetes in East Asian Populations.

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.

AN INTEGRATED NETWORK APPROACH TO IDENTIFYING BIOLOGICAL PATHWAYS AND ENVIRONMENTAL EXPOSURE INTERACTIONS IN COMPLEX DISEASES.

Complex diseases are the result of intricate interactions between genetic, epigenetic and environmental factors. In previous studies, we used epidemiological and genetic data linking environmental exposure or genetic variants to phenotypic disease to construct Human Phenotype Networks and separately analyze the effects of both environment and genetic factors on disease interactions. To better capture the intricacies of the interactions between environmental exposure and the biological pathways in complex disorders, we integrate both aspects into a single "tripartite" network.

Genome-wide genetic interaction analysis of glaucoma using expert knowledge derived from human phenotype networks.

The large volume of GWAS data poses great computational challenges for analyzing genetic interactions associated with common human diseases. We propose a computational framework for characterizing epistatic interactions among large sets of genetic attributes in GWAS data. We build the human phenotype network (HPN) and focus around a disease of interest.

A bipartite network approach to inferring interactions between environmental exposures and human diseases.

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.

Genome-wide epistasis and pleiotropy characterized by the bipartite human phenotype network.

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.

The multiscale backbone of the human phenotype network based on biological pathways.

Background: Networks are commonly used to represent and analyze large and complex systems of interacting elements. In systems biology, human disease networks show interactions between disorders sharing common genetic background. We built pathway-based human phenotype network (PHPN) of over 800 physical attributes, diseases, and behavioral traits; based on about 2,300 genes and 1,200 biological pathways.

Using the bipartite human phenotype network to reveal pleiotropy and epistasis beyond the gene.

With the rapid increase in the quality and quantity of data generated by modern high-throughput sequencing techniques, there has been a need for innovative methods able to convert this tremendous amount of data into more accessible forms. Networks have been a corner stone of this movement, as they are an intuitive way of representing interaction data, yet they offer a full set of sophisticated statistical tools to analyze the phenomena they model. We propose a novel approach to reveal and analyze pleiotropic and epistatic effects at the genome-wide scale using a bipartite network composed of human diseases, phenotypic traits, and several types of predictive elements (i.

Additive functions in boolean models of gene regulatory network modules.

Gene-on-gene regulations are key components of every living organism. Dynamical abstract models of genetic regulatory networks help explain the genome's evolvability and robustness. These properties can be attributed to the structural topology of the graph formed by genes, as vertices, and regulatory interactions, as edges.