wanyu/IteraTeR_v2 huggingface dataset:
Original:  Combining all lipid biomarkers, our re-analysis supported 29 out of 38 reported colocalisation results with eQTLs . Two clearly discordant findings (IFT172, CPNE1), as well as multiple new colocalisation results, highlight the value of a formal systematic statistical test.
Revised:  Combining all lipid biomarkers, our re-analysis supported 29 out of 38 reported colocalisation results with eQTLs . Two clearly discordant findings (IFT172, CPNE1), as well as multiple new colocalisation results, highlighting the value of a formal systematic statistical test.
Edit intent: fluency
Original:  Combining all lipid biomarkers, our re-analysis supported 29 out of 38 reported colocalisation results with eQTLs . Two clearly discordant findings (IFT172, CPNE1), as well as multiple new colocalisation results, highlight the value of a formal systematic statistical test.
Revised:  Combining all lipid biomarkers, our re-analysis supported 29 out of 38 reported colocalisation results with eQTLs . Two clearly discordant findings (IFT172, CPNE1), as well as multiple new colocalisation results, highlight the value of a formal statistical test.
Edit intent: clarity
Original:  Our findings provide information about the causal gene in associated intervals and have direct implications for the understanding of complex diseases as well as the design of drugs to target disease pathways.
Revised:  Our findings provide information about the causal gene in associated intervals and has direct implications for the understanding of complex diseases as well as the design of drugs to target disease pathways.
Edit intent: fluency
Original:  Our findings provide information about the causal gene in associated intervals and have direct implications for the understanding of complex diseases as well as the design of drugs to target disease pathways.
Revised:  Our findings provide information about the causal gene in associated intervals and have direct implications for the understanding of complex diseases and the design of drugs to target disease pathways.
Edit intent: coherence
Original:  For a homogeneous population of neurons we find (i) that synchrony is stabilized by either subthreshold adaptation or synaptic inhibition , and (ii) that synchrony is always unstable for networks with balanced synaptic excitation and inhibition .
Revised:  For a homogeneous population of neurons we find (i) that synchrony is stabilized by either subthreshold adaptation or synaptic inhibition , and (ii) that synchrony is always unstable for networks with balanced recurrent synaptic inputs .
Edit intent: clarity
Original: In a geometric graph, G, the stretch factor between two vertices, u and w, is the ratio between the Euclidean length of the shortest path from u to w in G and the Euclidean distance between u and w.
Revised: In a geometric graph, G, the stretch factor between two vertices, u and w, is the ratio between the Euclidean length of the shortest path from u to w in G and the Euclidean distance between u and w.
Edit intent: fluency
Original:  The average stretch factor of G is the average stretch factor taken over all pairs of vertices in G. We show that for any set, V, of n points in R ^d, there exists a geometric graph with vertex set V, that has O(n) edges, and that has average stretch factor 1+ o_n(1).
Revised:  The average stretch factor of G is the average stretch factor taken over all pairs of vertices in G. We show that for any set, V, of n points in R ^d, there exists a geometric graph with vertex set V, that has O(n) edges, and that has average stretch factor 1+ o_n(1).
Edit intent: fluency
Original:  more US states are domestically than internationally oriented (in both SSCIand SCI) .
Revised:  more US states are domestically oriented in SSCI, whereas more internationally in SCI .
Edit intent: clarity
Original:  In this paper, we give improved approximation algorithms for constructing large connected domatic partitions and packings  .
Revised:  In this paper, we give improved approximation algorithms for constructing large connected domatic partitions and packings graphs .
Edit intent: clarity
Original:  Let n be the number of nodes  and let k be the vertex connectivity of the graph.
Revised:  Let n be the number of nodes in the graph and let k be the vertex connectivity of the graph.
Edit intent: clarity
Original:  The fractional connected domatic number is at most k  , and our algorithms construct partitions and packings whose sizes are proportional to k.
Revised:  The fractional connected domatic number is at most k , and our algorithms construct packings whose sizes are proportional to k.
Edit intent: clarity
Original:  }%DIFDELCMD < \item %%% \item An algorithm for constructing a fractional connected domatic packing of size \Omega(k) for node-capacitated planar and minor-closed families of graphs.
Revised:  }%DIFDELCMD < \item %%% \item fractional connected domatic packing of size \Omega(k) for node-capacitated planar and minor-closed families of graphs.
Edit intent: clarity
Original:  \item An algorithm for constructing an edge-connected domatic partition of size \Omega(k / n) .%DIFDELCMD < \item %%% \item An improved sampling theorem for analyzing the vertex connectivity of a graph under node sampling . itemize
Revised:  \item An algorithm for constructing an edge-connected domatic partition of size \Omega(k / n) %DIFDELCMD < \item %%% \item An improved sampling theorem for analyzing the vertex connectivity of a graph under node sampling . itemize
Edit intent: fluency
Original:  However, existing methods for calculating the likelihood ratio test statistics in mvLMMs are time consuming, and , without approximations, cannot be directly applied to analyze even two traits jointly in a typical-size GWAS . Here, we present a novel algorithm for computing parameter estimates and test statistics (Likelihood ratio and Wald)in mvLMMs that i) reduces per-iteration optimization complexity from cubic to linear in the number of samples ;
Revised:  However, existing methods for calculating the likelihood ratio test statistics in mvLMMs are time consuming, and , without approximations, cannot be directly applied to analyze even two traits jointly in a typical-size GWAS . Here, we present a novel algorithm for computing parameter estimates and test statistics (Likelihood ratio and Wald)in mvLMMs that i) reduces per-iteration optimization complexity from cubic to linear in n ;
Edit intent: clarity
Original:  With real examples , we show that , as expected, the new method is orders of magnitude faster than competing methods in both variance component estimation in a single mvLMM, and in GWAS applications .
Revised:  With simulations , we show that , as expected, the new method is orders of magnitude faster than competing methods in both variance component estimation in a single mvLMM, and in GWAS applications .
Edit intent: clarity
Original:  Our open source library can be used to assemble matrices and vectors which , for this release, come from a Galerkin weak form, discretized with the Non-Uniform Rational B-spline (NURBS) basis .
Revised:  Our open source library can be used to assemble matrices and vectors which come from a Galerkin weak form, discretized with the Non-Uniform Rational B-spline (NURBS) basis .
Edit intent: coherence
Original:  Our open source library can be used to assemble matrices and vectors which , for this release, come from a Galerkin weak form, discretized with the Non-Uniform Rational B-spline (NURBS) basis .
Revised:  Our open source library can be used to assemble matrices and vectors which , for this release, come from a Galerkin weak form, discretized with Non-Uniform Rational B-spline (NURBS) basis .
Edit intent: fluency
Original: We generalize the strong Hanani-Tutte theorem to flat clustered graphs with two disjoint clusters and to clustered graphs with connected clusters , thereby obtaining a new polynomial time algorithm for testing c-planarity in these cases .
Revised: We generalize the strong Hanani--Tutte theorem to clustered graphs with two disjoint clusters and to clustered graphs with connected clusters , thereby obtaining a new polynomial time algorithm for testing c-planarity in these cases .
Edit intent: clarity
Original:  We also give a new and short proof for a result by Di Battista and Frati about efficient c-planarity testing of an embedded flat clustered graph with small faces  based on the matroid intersection algorithm.
Revised:  We also give a new and short proof for a result by Di Battista and Frati about efficient c-planarity testing of an embedded flat clustered graph with small faces . Our proof is based on the matroid intersection algorithm.
Edit intent: coherence
Original: We generalize the strong Hanani--Tutte theorem  to clustered graphs with two disjoint clusters, and show that an extension of our result to flat clustered graphs with three  disjoint clusters is not possible.
Revised: The Hanani--Tutte theorem to clustered graphs with two disjoint clusters, and show that an extension of our result to flat clustered graphs with three disjoint clusters is not possible.
Edit intent: coherence
Original:  Moreover, show a variant of  Hanani--Tutte theorem for c-connected clustered graphs.
Revised:  Moreover, show a variant of the Hanani--Tutte theorem for c-connected clustered graphs.
Edit intent: fluency
Original: Gene selection is an important aim of a microarray data analysis, as it can reveal information leading to a better understanding of the mechanisms of the investigated phenomenon.
Revised: Gene selection is an important part of a microarray data analysis, as it can reveal information leading to a better understanding of the mechanisms of the investigated phenomenon.
Edit intent: clarity
Original:  On the same time it is a very hard task due to the noisy nature of this data , becoming extreme if one wants to identify non-obviously relevant genes which subtle impact lies near the level of noise .
Revised:  On the same time it is a very hard task due to the noisy nature of this data .
Edit intent: clarity
Original:  To this end, it is often approached through machine learning;
Revised:  To this end, gene selection is often approached through machine learning;
Edit intent: clarity
Original:  the analysis is focused on the stability of selection, as it is a necessity for the significance of the results, yet it is often ignored in  similar studies.
Revised:  the analysis was focused on the stability of selection, as it is a necessity for the significance of the results, yet it is often ignored in similar studies.
Edit intent: fluency
Original:  the analysis is focused on the stability of selection, as it is a necessity for the significance of the results, yet it is often ignored in  similar studies.
Revised:  the analysis is focused on the stability of selection, as it is a necessity for the significance of the results, yet it is often ignored in a similar studies.
Edit intent: fluency
Original:  Out of all analysed methods, the Boruta algorithm proved to find most possibly important genes on the same time achieving highest signal-to-noise ratio;
Revised:  Out of analysed methods, the Boruta algorithm proved to find most possibly important genes on the same time achieving highest signal-to-noise ratio;
Edit intent: clarity
Original:  Although it was also the most computationally intensive method, its demand could be reduced to the level comparable with other analysed method by replacing the Random Forest importance with this produced by Random Ferns, a similar but simplified algorithm .
Revised:  Although it was also the most computationally intensive method, its demand could be reduced to the level comparable with other algorithms by replacing the Random Forest importance with this produced by Random Ferns, a similar but simplified algorithm .
Edit intent: clarity
Original: Gene selection is an important part of a microarray data analysis , as it can reveal information leading to a better understanding of the mechanisms of the investigated phenomenon.
Revised: Gene selection is an important part of microarray data analysis , as it can reveal information leading to a better understanding of the mechanisms of the investigated phenomenon.
Edit intent: fluency
Original: Gene selection is an important part of a microarray data analysis , as it can reveal information leading to a better understanding of the mechanisms of the investigated phenomenon.
Revised: Gene selection is an important part of a microarray data analysis because it provides information that can lead to a better understanding of the mechanisms of the investigated phenomenon.
Edit intent: clarity
Original: Gene selection is an important part of a microarray data analysis , as it can reveal information leading to a better understanding of the mechanisms of the investigated phenomenon.
Revised: Gene selection is an important part of a microarray data analysis , as it can reveal information leading to a better mechanistic understanding of an investigated phenomenon.
Edit intent: clarity
Original:  On the same time it is a very hard task due to the noisy nature of this data.
Revised:  At the same time it is a very hard task due to the noisy nature of this data.
Edit intent: fluency
Original:  On the same time it is a very hard task due to the noisy nature of this data.
Revised:  On the same time , gene selection is very difficult because of the noisy nature of this data.
Edit intent: clarity
Original:  in particular with the Random Forest method which has several features crucial for this purpose.
Revised:  in particular with the Random Forest method is particularly well suited for this purpose.
Edit intent: clarity
Original:  In this work, four state-of-art Random Forest-based feature selection methods were compared in a gene selection context ;
Revised:  In this work, four state-of-the-art Random Forest-based feature selection methods were compared in a gene selection context ;
Edit intent: fluency
Original:  the analysis was focused on the stability of selection , as it is a necessity for the significance of the results, yet it is often ignored in a similar studies.
Revised:  the analysis was focused on the stability of selection because, although it is necessary for determining the significance of the results, yet it is often ignored in a similar studies.
Edit intent: coherence
Original:  the analysis was focused on the stability of selection , as it is a necessity for the significance of the results, yet it is often ignored in a similar studies.
Revised:  the analysis was focused on the stability of selection , as it is a necessity for the significance of results, it is often ignored in a similar studies.
Edit intent: coherence
Original:  the analysis was focused on the stability of selection , as it is a necessity for the significance of the results, yet it is often ignored in a similar studies.
Revised:  the analysis was focused on the stability of selection , as it is a necessity for the significance of the results, yet it is often ignored in similar studies.
Edit intent: fluency
Original:  The comparison of post-selection accuracy of a validation Random Forest classifier revealed that all investigated methods were equivalent in this manner .
Revised:  The comparison of post-selection accuracy in the validation of Random Forest classifiers revealed that all investigated methods were equivalent in this manner .
Edit intent: fluency
Original:  The comparison of post-selection accuracy of a validation Random Forest classifier revealed that all investigated methods were equivalent in this manner .
Revised:  The comparison of post-selection accuracy of a validation Random Forest classifier revealed that all investigated methods were equivalent in this context .
Edit intent: clarity
Original:  However, the methods substantially differ with respect to the amount of selected genes and the stability of selection.
Revised:  However, the methods substantially differed with respect to the amount of selected genes and the stability of selection.
Edit intent: fluency
Original:  However, the methods substantially differ with respect to the amount of selected genes and the stability of selection.
Revised:  However, the methods substantially differ with respect to the number of selected genes and the stability of selection.
Edit intent: clarity
Original:  Out of analysed methods, the Boruta algorithm proved to find most possibly important genes .
Revised:  Of the analysed methods, the Boruta algorithm proved to find most possibly important genes .
Edit intent: clarity
Original:  Though frequently used, the post-selection classifier error rate proved to be a possibly deceiving measure of gene selection quality.
Revised:  Though frequently used, the post-selection classifier error rate proved to be a potentially deceptive measure of gene selection quality.
Edit intent: fluency
Original:  When the amount of consistently selected genes is concerned , the Boruta algorithm was a clear best.
Revised:  When the number of consistently selected genes is concerned , the Boruta algorithm was a clear best.
Edit intent: clarity
Original:  When the amount of consistently selected genes is concerned , the Boruta algorithm was a clear best.
Revised:  When the amount of consistently selected genes was considered , the Boruta algorithm was a clear best.
Edit intent: clarity
Original:  When the amount of consistently selected genes is concerned , the Boruta algorithm was a clear best.
Revised:  When the amount of consistently selected genes is concerned , the Boruta algorithm was clearly the best.
Edit intent: fluency
Original:  Although it was also the most computationally intensive method, its demand could be reduced to the level comparable with other algorithms by replacing the Random Forest importance with this produced by Random Ferns , a similar but simplified classifier . Despite of their design assumptions, the minimal optimal selection methods RRF and RFE proved to select a high fraction of false positives.
Revised:  Although it was also the most computationally intensive method, its demand could be reduced to levels comparable to those of other algorithms by replacing the Random Forest importance with this produced by Random Ferns , a similar but simplified classifier . Despite of their design assumptions, the minimal optimal selection methods RRF and RFE proved to select a high fraction of false positives.
Edit intent: clarity
Original:  Although it was also the most computationally intensive method, its demand could be reduced to the level comparable with other algorithms by replacing the Random Forest importance with this produced by Random Ferns , a similar but simplified classifier . Despite of their design assumptions, the minimal optimal selection methods RRF and RFE proved to select a high fraction of false positives.
Revised:  Although it was also the most computationally intensive method, its demand could be reduced to the level comparable with other algorithms by replacing the Random Forest importance with a comparable measure from Random Ferns ( a similar but simplified classifier . Despite of their design assumptions, the minimal optimal selection methods RRF and RFE proved to select a high fraction of false positives.
Edit intent: clarity
Original:  Although it was also the most computationally intensive method, its demand could be reduced to the level comparable with other algorithms by replacing the Random Forest importance with this produced by Random Ferns , a similar but simplified classifier . Despite of their design assumptions, the minimal optimal selection methods RRF and RFE proved to select a high fraction of false positives.
Revised:  Although it was also the most computationally intensive method, its demand could be reduced to the level comparable with other algorithms by replacing the Random Forest importance with this produced by Random Ferns , a similar but simplified classifier ). Despite their design assumptions, the minimal optimal selection methods RRF and RFE proved to select a high fraction of false positives.
Edit intent: fluency
Original:  Although it was also the most computationally intensive method, its demand could be reduced to the level comparable with other algorithms by replacing the Random Forest importance with this produced by Random Ferns , a similar but simplified classifier . Despite of their design assumptions, the minimal optimal selection methods RRF and RFE proved to select a high fraction of false positives.
Revised:  Although it was also the most computationally intensive method, its demand could be reduced to the level comparable with other algorithms by replacing the Random Forest importance with this produced by Random Ferns , a similar but simplified classifier . Despite of their design assumptions, the minimal optimal selection methods , were found to select a high fraction of false positives.
Edit intent: clarity
Original: It has been hypothesized that the remarkably small number of crossings in real syntactic dependency trees is a side-effect of pressure for dependency length minimization.
Revised: It has been hypothesized that the rather small number of crossings in real syntactic dependency trees is a side-effect of pressure for dependency length minimization.
Edit intent: fluency
Original:  We show that that this number depends only on the number of vertices of the dependency tree (the sentence length) and the second moment of vertex degrees.
Revised:  We show that this number depends only on the number of vertices of the dependency tree (the sentence length) and the second moment of vertex degrees.
Edit intent: fluency
Original:  The expected number of crossings is minimum for a star tree ( no crossing is expected ) and maximum for a linear tree (the number of crossings is of the order of the square of the sequence length).
Revised:  The expected number of crossings is minimum for a star tree ( crossings are impossible ) and maximum for a linear tree (the number of crossings is of the order of the square of the sequence length).
Edit intent: clarity
Original:  A parallelization of the butterfly algorithm is introduced which, assuming a message latency of \alpha and per-process inverse bandwidth of \beta, executes in at most O(r^2 N^d/p log N + ( \beta r N^d/p + \alpha)log p) time using p processes.
Revised:  A parallelization of the butterfly algorithm is introduced which, assuming a message latency of \alpha and per-process inverse bandwidth of \beta, executes in at most O(r^2 N^d/p log N + \beta r N^d/p + \alpha)log p) time using p processes.
Edit intent: fluency
Original: We develop a analytical approach to the susceptible-infected-susceptible  epidemic model that allows us to unravel the true origin of the absence of an epidemic threshold in heterogeneous networks.
Revised: We develop an analytical approach to the susceptible-infected-susceptible epidemic model that allows us to unravel the true origin of the absence of an epidemic threshold in heterogeneous networks.
Edit intent: fluency
Original:  In ATLAS, each node computes its persistence, the fraction of time it is permitted to transmit,  in a topology and load dependent manner.
Revised:  In ATLAS, each node computes its persistence, the fraction of time a node is permitted to transmit, in a topology and load dependent manner.
Edit intent: fluency
Original:  Our results show that ATLAS converges to most network changes in less than 0.1s, with about 20\% relative error, scaling with network size.
Revised:  Our results show that ATLAS adapts to most network changes in less than 0.1s, with about 20\% relative error, scaling with network size.
Edit intent: clarity
Original:  We further study ATLAS in more dynamic networks and show that it keeps up with topology changes , and load changes  sufficient for TCP to sustain multi-hop flows, a struggle in IEEE 802.11 networks.
Revised:  We further study ATLAS in more dynamic networks showing that it keeps up with topology changes , and load changes sufficient for TCP to sustain multi-hop flows, a struggle in IEEE 802.11 networks.
Edit intent: coherence