Computer-Aided Experiment Planning toward Causal Discovery in Neuroscience

Matiasz, Nicholas J.; Wood, Joanne V.; Wang, Wei; Silva, Alcino J.; Hsu, William

doi:10.3389/fninf.2017.00012

Cited by 9 publications

(10 citation statements)

References 27 publications

(31 reference statements)

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“…Next, these annotations are input to an algorithm that identifies the causal graphs consistent with the annotated results. The scientist can then inspect the consistent graphs to see which inferences arise out of the synthesis of annotated research articles [9], [10].…”

Section: Introductionmentioning

confidence: 99%

“…We characterize this underdetermination with a causal graph's degrees of freedom, which represent the diversity of edge relations that appear in the graphs of an equivalence class [9], [10]. For example, all graphs in an equivalence class may have the same edge relation between the variables X and Y (e.g., X → Y ), but there may be a diversity of edge relations between the variables Y and Z (e.g., Y ← Z and Y → Z).…”

Section: Introductionmentioning

confidence: 99%

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Experiment Selection in Meta-Analytic Piecemeal Causal Discovery

et al. 2021

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Scientists try to design experiments that will yield maximal information. For instance, given the available evidence and a limitation on the number of variables that can be observed simultaneously, it may be more informative to intervene on variable X and observe the response of variable Y than to intervene on X and observe Z; in other situations, the opposite may be true. Scientists must often make these decisions without primary data. To address this problem, in previous work, we created software for annotating aggregate statistics in the literature and deriving consistent causal explanations, expressed as causal graphs. This meta-analytic pipeline is useful not only for synthesizing evidence but also for planning experiments: one can use it strategically to select experiments that could further eliminate causal graphs from consideration. In this paper, we introduce interpretable policies for selecting experiments in the context of piecemeal causal discovery, a common setting in biological sciences in which each experiment can measure not an entire system but rather a strict subset of its variables. The limits of this piecemeal approach are only beginning to be fully characterized, with crucial theoretical work published recently [1]- [3]. With simulations, we show that our experiment-selection policies identify causal structures more efficiently than random experiment selection. Unlike methods that require primary data, our meta-analytic approach offers a flexible alternative for those seeking to incorporate qualitative domain knowledge into their search for causal mechanisms. We also present a method that categorizes hypotheses with respect to their utility for identifying a system's causal structure. Although this categorization is usually infeasible to perform manually, it is critical for conducting research efficiently.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Experiment Selection in Meta-Analytic Piecemeal Causal Discovery

et al. 2021

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“…There are several related approaches to specify experimental data and metadata. For example, Silva and colleagues (Silva and Müller, 2015 ; Matiasz et al, 2017 ) have come up with frameworks for defining neurobiological experiments. Much more structured experiments such as microarrays (Brazma et al, 2001 ), next-generation sequencing, e.g., (Kent et al, 2010 ) or proteomics (Taylor et al, 2006 , 2007 ) have their own metadata formats.…”

Section: Introductionmentioning

confidence: 99%

FindSim: A Framework for Integrating Neuronal Data and Signaling Models

Viswan

HarshaRani

Stefan

et al. 2018

Front. Neuroinform.

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Current experiments touch only small but overlapping parts of very complex subcellular signaling networks in neurons. Even with modern optical reporters and pharmacological manipulations, a given experiment can only monitor and control a very small subset of the diverse, multiscale processes of neuronal signaling. We have developed FindSim (Framework for Integrating Neuronal Data and SIgnaling Models) to anchor models to structured experimental datasets. FindSim is a framework for integrating many individual electrophysiological and biochemical experiments with large, multiscale models so as to systematically refine and validate the model. We use a structured format for encoding the conditions of many standard physiological and pharmacological experiments, specifying which parts of the model are involved, and comparing experiment outcomes with model output. A database of such experiments is run against successive generations of composite cellular models to iteratively improve the model against each experiment, while retaining global model validity. We suggest that this toolchain provides a principled and scalable way to tackle model complexity and diversity of data sources.

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“…Building on our previous discussions of these general concepts [ 1 – 3 ], we introduce an updated research map representation and an accompanying web application, ResearchMaps ( http://researchmaps.org/ ), designed to help biologists integrate and plan experiments. A research map graphically represents hypothetical assertions and empirical findings.…”

Section: Introductionmentioning

confidence: 99%

“…This Bayesian approach expresses integration principles, including convergence and consistency, commonly used by many biologists to judge the strength of causal assertions. Thus, our goal with research maps was not to build another ontology but rather to formalize aspects of biologists’ epistemology [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

ResearchMaps.org for integrating and planning research

et al. 2018

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To plan experiments, a biologist needs to evaluate a growing set of empirical findings and hypothetical assertions from diverse fields that use increasingly complex techniques. To address this problem, we operationalized principles (e.g., convergence and consistency) that biologists use to test causal relations and evaluate experimental evidence. With the framework we derived, we then created a free, open-source web application that allows biologists to create research maps, graph-based representations of empirical evidence and hypothetical assertions found in research articles, reviews, and other sources. With our ResearchMaps web application, biologists can systematically reason through the research that is most important to them, as well as evaluate and plan experiments with a breadth and precision that are unlikely without such a tool.

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Computer-Aided Experiment Planning toward Causal Discovery in Neuroscience

Cited by 9 publications

References 27 publications

Experiment Selection in Meta-Analytic Piecemeal Causal Discovery

Experiment Selection in Meta-Analytic Piecemeal Causal Discovery

FindSim: A Framework for Integrating Neuronal Data and Signaling Models

ResearchMaps.org for integrating and planning research

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