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Prediction of robust scientific facts from literature

A preprint version of the article is available at arXiv.


The growth of published science in recent years has escalated the difficulty that human and algorithmic agents face in reasoning over prior knowledge to select the next experiment. This challenge is increased by uncertainty about the reproducibility of published findings. The availability of massive digital archives, machine reading, extraction tools and automated high-throughput experiments allows us to evaluate these challenges computationally at scale and identify novel opportunities to craft policies that accelerate scientific progress. Here we demonstrate a Bayesian calculus that enables positive prediction of robust scientific claims with findings extracted from published literature, weighted by scientific, social and institutional factors demonstrated to increase replicability. Illustrated with the case of gene regulatory interactions, our approach automatically estimates and counteracts sources of bias, revealing that scientifically focused but socially and institutionally diverse research activity is most likely to replicate. This results in updated certainty about the literature, which accurately predicts robust scientific facts on which new experiments should build. Our findings allow us to identify and evaluate policy recommendations for scientific institutions that may increase robust scientific knowledge, including sponsorship of increased diversity of and independence between investigations of any particular scientific phenomenon, and diversity of scientific phenomena investigated.

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Fig. 1: Analysis synopsis.
Fig. 2: Feature visualization and estimates from claim-level prediction models.
Fig. 3: Positivity bias in published effects and prediction results.
Fig. 4: Science policy experiments revealing the relationship between community independence, collective attention and certainty about genetic regulatory interactions.

Data availability

To illustrate our pipeline, we used the publicly available GeneWays and Literome datasets (available at and, linked with Clarivate’s Web of Science database of bibliographic information. While we cannot share the Web of Science, we share a linked file, which includes all claims of interest and citation metadata required to perform described analyses.

Code availability

Our code is publicly available at and


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We thank V. Sitnik, V. Danchev and P. Saleiro for fruitful discussions, Y. Babuji for technical help, H. Poon for suggestions regarding the formulation of the project and I. Mayzus, R. Melamed and O. Kel-Margoulis for help with the annotation and the interpretation of biological datasets. We are grateful for comments from participants of the MetaScience Conference at Stanford (2019), and for meetings associated with the Defense Advanced Research Projects Agency (DARPA) Big Mechanism programme. We acknowledge funding from DARPA (14145043, J.E. and A.V.B.; HR00111820006, J.E., A.V.B. and A.R.), the Air Force Office of Scientific Research (FA9550-19-1-0354, J.E.; FA9550-15-1-0162, J.E.), the National Science Foundation (SBE-1829366, J.E.; 1422902, J.E.; 1158803, J.E.) and the John Templeton Foundation to the ‘Metaknowledge Network’ (J.E. and A.R.).

Author information

Authors and Affiliations



A.V.B. proposed and implemented the methodology, validated the model, analysed the data and drafted the paper. J.E. was responsible for conception and funding of the project, contributed to the design of the methodology and drafted the paper. A.R. provided feedback on the experimental work and data interpretation, and participated in drafting the paper. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Alexander V. Belikov or James Evans.

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The authors declare no competing interests.

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Nature Machine Intelligence thanks Luis Amaral and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Illustration of core interaction and claim variables.

Directed regulatory interactions between genes constitute communities of researchers who study them. Features regarding the position of a claim within prior knowledge are derived from its relationship to other genetic regulatory interactions. Features regarding the breadth and independence of support are derived from the connection between publications making claims about the same interaction.

Extended Data Fig. 2 Correlation between claim value and experimental strength across the claim frequency distribution.

Correlation of mean claim value \(\hat \mu ^\alpha\) and interaction strength \(\hat \pi ^\alpha\) from LINCS L1000 as a function of threshold on minimum claim sequence length per interaction for GeneWays (a) and Literome (b).

Extended Data Fig. 3 Data-driven thresholds to partition interactions into neutral, negative and positive interactions for analysis.

C0, C and C+ correspond to classes of neutral, negative and positive genetic regulatory interactions. Distance between C0and C (\(W\left( {g_0,g_ - ,\theta _ - ,\theta _ + } \right)\), solid green), and C0 and C+ (\(W\left( {g_0,g_ + ,\theta _ - ,\theta _ + } \right)\), solid blue), number of claims in C, dotted green, number of claims in C+, dotted blue) for GeneWays (a) and Literome (b). Distance between C0 and C (\(W\left( {g_0,g_ - ,\theta _ - ,\theta _ + } \right)\)) in GeneWays (c) and Literome (d); Distance between C0 and C+ (\(W\left( {g_0,g_ + ,\theta _ - ,\theta _ + } \right)\)) in GeneWays (e) and Literome (f).

Extended Data Fig. 4 Pearson correlation between core analysis variables in both Geneways and Literome datasets.

Heat map indicating correlation between: (a) claim correctness \(y_i^\alpha\) and batch-level features for GeneWays (top row) and Literome (bottom row); (b) claim correctness \(y_i^\alpha\) and claim-level features for GeneWays (top row) and Literome (bottom row); (c) interaction non-neutrality \(\pi _0^\alpha\) and interaction-level features for GeneWays (top row) and Literome (bottom row); (d) interaction positivity \(\pi _ + ^\alpha\) and interaction-level features for GeneWays (top row) and Literome (bottom row).

Extended Data Fig. 5 Variable importance and significance in models of the non-neutrality and positivity of genetic regulatory interactions.

Family importances of random forest model (left, darker shade) and logistic regression coefficients (right, lighter shade) for the model of classification of neutral interactions (top) and positive interactions (bottom) for GeneWays (left) and Literome (right). Vertical centered lines show 95% confidence level on the mean of the corresponding importance/coefficient.

Extended Data Fig. 6 Analysis of the relationship between the distribution of claims per interaction and overall certainty about those interactions.

Examples of claim number distribution ρ(nα) per interaction for test subsamples from GeneWays (a) and Literome (b). Information gain as a function of the slope of claim number distribution β. Solid lines correspond to binned averages and shaded regions denote one standard deviation of the data confidence interval for GeneWays (c) and Literome (d).

Extended Data Fig. 7 Survival functions (complements of the cumulative distribution functions) of claim number per interaction.

Survival functions for GeneWays (a) and Literome (b); for all interactions () and nonzero () interactions, where the probability distribution function is modeled as \(\rho \propto n^\gamma\). Exponents γ equal 2.26 and 2.01 for Geneways for all and non-neutral interactions, respectively; and equal 2.5 and 2.26 for Literome for all and non-neutral interactions. The exponents were obtained by Maximum Likelihood Estimation.

Extended Data Fig. 8 Model selection using ROC AUC values for all models.

Neutral interaction models (a-c,g-i) and positive interaction models (d-f,j-l). Left: the distribution of ROC AUC as a function of random forest depth (a,d,g,j). Center: the distribution of ROC AUC as a function of minimum number of samples in a decision tree leaf (b,e,h,k). Right: the distribution of ROC AUC as a function of the number of trees in a random forest (c,f,i,l).

Extended Data Fig. 9 Science policy experiments revealing the relationship between community independence, collective attention, and certainty about genetic regulatory interactions (complement to Fig. 4).

a, Relationship between the number of communities studying a particular genetic regulatory interaction and the average AUC of out-of-sample predictions for positive interactions. b, Distribution of the average AUC curves for Literome for interactions with 1, 2-3 and greater than 4 communities. c, Relationship between the shape of the distribution of number of claims per interaction on the AUC of out-of-sample predictions for positive interactions. β represents the slope of the claim number per interaction distribution for Literome. (Complement to main Fig. 4).

Extended Data Fig. 10 Positivity bias in published effects and prediction results for Literome (complement to Fig. 3); random forest Gini Importance scores and logistic regression coefficients for features from Literome (complement to Fig. 2b).

a, Joint plot of the mean experimental interaction strength (x-axis) and mean value of the published claim (y-axis) for each genetic interaction. More intense hues of the red (and also greater marker size) correspond to the interactions in Literome with 10 or more claims per interaction; for less intense hues (and also smaller marker size) the cutoff is absent, representing the complete distribution. (See Fig. 3a for comparable Geneways distribution). b, We first predicted the nonexistentence () or existence () of each published gene-gene regulatory interaction (Literome). c, Then, if the interaction was deemed existent (), we predicted whether each claim (of positivity or negativity) from literature was correct. d, Using Bayesian inference, we estimated the sign (positive vs negative) of all genetic regulatory interactions. Mean ROC curves in bold are complemented by a 95% c.i. contours, with fainter individual lines corresponding to ROC curves for 60 models corresponding to different training/validation samples. (Complement to Fig. 3 in the main manuscript). e, Gini Importance or Mean Decrease in Impurity for features in the random forest models (left vertical scale, bold colors), and coefficients from the logistic regression models (right vertical scale, fainter colors) for Literome. Vertical bars represent 95% c.i. for the mean value of the estimate.

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Belikov, A.V., Rzhetsky, A. & Evans, J. Prediction of robust scientific facts from literature. Nat Mach Intell 4, 445–454 (2022).

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