Biology subjects

Kon, M.

Publications and source records attributed to Kon, M..

3 recordsLinked to original sources

bioRxiv2019

BowSaw: inferring higher-order trait interactions associated with complex biological phenotypes

Machine learning is helping the interpretation of biological complexity by enabling the inference and classification of cellular, organismal and ecological phenotypes based on large datasets, e.g. from genomic, transcriptomic and metagenomic analyses. A number of available algorithms can help search these datasets to uncover patterns associated with specific traits, including disease-related attributes. While, in many instances, treating an algorithm as a black box is sufficient, it is interesting to pursue an enhanced understanding of how system variables end up contributing to a specific output, as an avenue towards new mechanistic insight. Here we address this challenge through a suite of algorithms, named BowSaw, which takes advantage of the structure of a trained random forest algorithm to identify combinations of variables ("rules") frequently used for classification. We first apply BowSaw to a simulated dataset, and show that the algorithm can accurately recover the sets of variables used to generate the phenotypes through complex Boolean rules, even under challenging noise levels. We next apply our method to data from the integrative Human Microbiome Project and find previously unreported high-order combinations of microbial taxa putatively associated with Crohns disease. By leveraging the structure of trees within a random forest, BowSaw provides a new way of using decision trees to generate testable biological hypotheses.

systems biology↗

bioRxiv2019

Factors associated with opioid cessation: a machine learning approach

Background and AimsPeople with opioid use disorder (OUD) can stop using opioids on their own, with help from groups and with treatment, but there is limited research on the factors that influence opioid cessation.\n\nMethodsWe employed multiple machine learning prediction algorithms (LASSO, random forest, deep neural network, and support vector machine) to assess factors associated with ceasing opioid use in a sample comprised of African Americans (AAs) and European Americans (EAs) who met DSM-5 criteria for mild to severe OUD. Values for several thousand demographic, alcohol and other drug use, general health, and behavioral variables, as well as diagnoses for other psychiatric disorders, were obtained for each participant from a detailed semi-structured interview.\n\nResultsSupport vector machine models performed marginally better on average than those derived using other machine learning methods with maximum prediction accuracies of 75.4% in AAs and 79.4% in EAs. Subsequent stepwise regression analyses that considered the 83 most highly ranked variables across all methods and models identified less recent cocaine use (p<5x10-8), a shorter duration of opioid use (p<5x10-6), and older age (p<5x10-9) as the strongest independent predictors of opioid cessation. Factors related to drug use comprised about half of the significant independent predictors, with other predictors related to non-drug use behaviors, psychiatric disorders, overall health, and demographics.\n\nConclusionsThese proof-of-concept findings provide information that can help develop strategies for improving OUD management and the methods we applied provide a framework for personalizing OUD treatment.

neuroscience↗

bioRxiv2018

Machine learning reveals missing edges and putative interaction mechanisms in microbial ecosystem networks

Microbes affect each others growth in multiple, often elusive ways. The ensuing interdependencies form complex networks, believed to influence taxonomic composition, as well as community-level functional properties and dynamics. Elucidation of these networks is often pursued by measuring pairwise interaction in co-culture experiments. However, combinatorial complexity precludes the exhaustive experimental analysis of pairwise interactions even for moderately sized microbial communities. Here, we use a machine-learning random forest approach to address this challenge. In particular, we show how partial knowledge of a microbial interaction network, combined with trait-level representations of individual microbial species, can provide accurate inference of missing edges in the network and putative mechanisms underlying interactions. We applied our algorithm to two case studies: an experimentally mapped network of interactions between auxotrophic E. coli strains, and a large in silico network of metabolic interdependencies between 100 human gut-associated bacteria. For this last case, 5% of the network is enough to predict the remaining 95% with 80% accuracy, and mechanistic hypotheses produced by the algorithm accurately reflect known metabolic exchanges. Our approach, broadly applicable to any microbial or other ecological network, can drive the discovery of new interactions and new molecular mechanisms, both for therapeutic interventions involving natural communities and for the rational design of synthetic consortia.\n\nImportanceDifferent organisms in a microbial community may drastically affect each others growth phenotype, significantly affecting the community dynamics, with important implications for human and environmental health. Novel culturing methods and decreasing costs of sequencing will gradually enable high-throughput measurements of pairwise interactions in systematic co-culturing studies. However, a thorough characterization of all interactions that occur within a microbial community is greatly limited both by the combinatorial complexity of possible assortments, and by the limited biological insight that interaction measurements typically provide without laborious specific follow-ups. Here we show how a simple and flexible formal representation of microbial pairs can be used for classification of interactions with machine learning. The approach we propose predicts with high accuracy the outcome of yet to be performed experiments, and generates testable hypotheses about the mechanisms of specific interactions.

bioinformatics↗