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Interpretation of microbiota-based diagnostics by explaining individual classifier decisions.

ABSTRACT: The human microbiota is associated with various disease states and holds a great promise for non-invasive diagnostics. However, microbiota data is challenging for traditional diagnostic approaches: It is high-dimensional, sparse and comprises of high inter-personal variation. State of the art machine learning tools are therefore needed to achieve this goal. While these tools have the ability to learn from complex data and interpret patterns therein that cannot be identified by humans, they often operate as black boxes, offering no insight into their decision-making process. In most cases, it is difficult to represent the learning of a classifier in a comprehensible way, which makes them prone to be mistrusted, or even misused, in a clinical environment. In this study, we aim to elucidate microbiota-based classifier decisions in a biologically meaningful context to allow their interpretation.We applied a method for explanation of classifier decisions on two microbiota datasets of increasing complexity: gut versus skin microbiota samples, and inflammatory bowel disease versus healthy gut microbiota samples. The algorithm simulates bacterial species as being unknown to a pre-trained classifier, and measures its effect on the outcome. Consequently, each patient is assigned a unique quantitative estimation of which species in their microbiota defined the classification of their sample. The algorithm was able to explain the classifier decisions well, demonstrated by our validation method, and the explanations were biologically consistent with recent microbiota findings.Application of a method for explaining individual classifier decisions for complex microbiota analysis proved feasible and opens perspectives on personalized therapy. Providing an explanation to support a microbiota-based diagnosis could guide decisions of clinical microbiologists, and has the potential to increase their confidence in the outcome of such decision support systems. This may facilitate the development of new diagnostic applications.

SUBMITTER: Eck A

PROVIDER: S-EPMC5628491 | biostudies-other | 2017 Oct

REPOSITORIES: biostudies-other

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Interpretation of microbiota-based diagnostics by explaining individual classifier decisions.

Eck A A Zintgraf L M LM de Groot E F J EFJ de Meij T G J TGJ Cohen T S TS Savelkoul P H M PHM Welling M M Budding A E AE

BMC bioinformatics 20171004 1

<h4>Background</h4>The human microbiota is associated with various disease states and holds a great promise for non-invasive diagnostics. However, microbiota data is challenging for traditional diagnostic approaches: It is high-dimensional, sparse and comprises of high inter-personal variation. State of the art machine learning tools are therefore needed to achieve this goal. While these tools have the ability to learn from complex data and interpret patterns therein that cannot be identified by ...[more]

PMID: 28978318

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Project description:Crying is the principal means by which newborn infants shape parental behavior to meet their needs. While this mechanism can be highly effective, infant crying can also be an aversive stimulus that leads to parental frustration and even abuse. Fathers have recently become more involved in direct caregiving activities in modern, developed nations, and fathers are more likely than mothers to physically abuse infants. In this study, we attempt to explain variation in the neural response to infant crying among human fathers, with the hope of identifying factors that are associated with a more or less sensitive response. We imaged brain function in 39 first-time fathers of newborn infants as they listened to both their own and a standardized unknown infant cry stimulus, as well as auditory control stimuli, and evaluated whether these neural responses were correlated with measured characteristics of fathers and infants that were hypothesized to modulate these responses. Fathers also provided subjective ratings of each cry stimulus on multiple dimensions. Fathers showed widespread activation to both own and unknown infant cries in neural systems involved in empathy and approach motivation. There was no significant difference in the neural response to the own vs. unknown infant cry, and many fathers were unable to distinguish between the two cries. Comparison of these results with previous studies in mothers revealed a high degree of similarity between first-time fathers and first-time mothers in the pattern of neural activation to newborn infant cries. Further comparisons suggested that younger infant age was associated with stronger paternal neural responses, perhaps due to hormonal or novelty effects. In our sample, older fathers found infant cries less aversive and had an attenuated response to infant crying in both the dorsal anterior cingulate cortex (dACC) and the anterior insula, suggesting that compared with younger fathers, older fathers may be better able to avoid the distress associated with empathic over-arousal in response to infant cries. A principal components analysis revealed that fathers with more negative emotional reactions to the unknown infant cry showed decreased activation in the thalamus and caudate nucleus, regions expected to promote positive parental behaviors, as well as increased activation in the hypothalamus and dorsal ACC, again suggesting that empathic over-arousal might result in negative emotional reactions to infant crying. In sum, our findings suggest that infant age, paternal age and paternal emotional reactions to infant crying all modulate the neural response of fathers to infant crying. By identifying neural correlates of variation in paternal subjective reactions to infant crying, these findings help lay the groundwork for evaluating the effectiveness of interventions designed to increase paternal sensitivity and compassion.

Dataset Information

Interpretation of microbiota-based diagnostics by explaining individual classifier decisions.

Publications

Interpretation of microbiota-based diagnostics by explaining individual classifier decisions.

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