The plot of residuals against fitted values is the most important graphic in the diagnostics. The first are based on recent price trends, including 5 of the top-7 variables in Figure 5: short-term reversal (mom1m), stock momentum (mom12m), momentum change (chmom), industry momentum (indmom), recent maximum return (maxret), and long-term reversal (mom36m). This typically occurs before the prediction errors are minimized in the training sample, hence the name early stopping (see Algorithm 6 of the Internet Appendix B.3). Thanks again for your help! Rev. Supplementary Table 2 BGCs annotations of 45 XP genomes by antiSMASH and in-house database. Our primary contributions are twofold. It is this flexibility that allows us to push the frontier of risk premium measurement. Figure 4 reports the resultant importance of the top-20 stock-level characteristics for each method. Columns correspond to the individual models, and the color gradients within each column indicate the most influential (dark blue) to the least influential (white) variables. Table 4 shows the |$R^{2}$|-based importance measure for each macroeconomic predictor variable (again normalized to sum to one within a given model). Dev. normalized to a DMSO-treated control from three independent experiments. We see that weight influences vs positively, while displacement has a slightly negative effect. We use the terms expected return and risk premium interchangeably. Biotechnol. Next, a second simple tree (with the same shallow depth |$L$|) is used to fit the prediction residuals from the first tree. Groll, M. & Potts, B. C. Proteasome structure, function and lessons learned from -lactone inhibitors. Get the most important science stories of the day, free in your inbox. Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. Entomol. Without a priori assumptions for which interactions to include, the generalized linear model becomes computationally infeasible.15. Out-of-sample portfolio performance aligns very closely with results on machine learning forecast accuracy reported earlier. A total of 1,000 BGCs were detected and categorized into eight classes (Fig. The table was then sorted by genome_name and gene_callers_id columns in ascending order. 47, W81W87 (2019). After 5min, the clear solution was added to the resin and shaken at room temperature overnight. Specifically, the interpretation of j is the expected change in y for a one-unit change in x j when the other covariates are held fixedthat is, the expected value of the 24 Early stopping bears a comparatively low computation cost because of only partial optimization, whereas the |$l_2$| regularization, or more generally elastic net, search across tuning parameters fully optimizes the model subject to each tuning parameter guess. Compared to Xenorhabdus, Photorhabdus tends to harbour a larger genome size with more BGCs. The first plot seems to indicate that the residuals and the fitted values are uncorrelated, as they should be in a homoscedastic linear model with normally distributed errors. It only takes a minute to sign up. Sci. Chemical shifts () were reported in parts per million (ppm) and referenced to the solvent signals. Here are the codes in R. It means the data point SantaCruz has the largest influence on the fitted Poisson model. Selecting a successful network architecture by cross-validation is in general a difficult task. ), HATU (108mg, 0.28mmol, 5equiv.) Thus, in this example, there are a total of 31|$=(4+1)\times 5 + 6$| parameters (five parameters to reach each neuron and six weights to aggregate the neurons into a single output). Van Lanen, S. G., Lin, S. & Shen, B. Biosynthesis of the enediyne antitumor antibiotic C-1027 involves a new branching point in chorismate metabolism. D.A. Microbiol. In contrast, partial least squares performs dimension reduction by directly exploiting covariation of predictors with the forecast target.11 PLS regression proceeds as follows. Despite its flexibility, this framework imposes some important restrictions. n=3 biologically independent larvae per experiment over three independent experiments. Our data construction differs by more closely adhering to variable definitions in original papers. We follow the algorithm of, $$\begin{align}\label{eqn:impurity} H(\theta, C)=&\frac{1}{|C|}\sum_{z_{i,t} \in C}(r_{i, t+1}-\theta)^2,\end{align}$$, The model incorporates more flexible predictive associations by adding hidden layers between the inputs and output. To begin, for each method, we calculate the reduction in |$R^{2}$| from setting all values of a given predictor to zero within each training sample, and average these into a single importance measure for each predictor. 4e), thereby preventing hydrolysis of the acyl enzyme complex, and explaining its inhibitory effect. The final nonlinear method that we analyze is the artificial neural network. Rev. After an incubation time of 45min at room temperature, fluorogenic substrates Boc-Leu-Arg-Arg-AMC (AMC, 7-amino-4-methylcoumarin), Z-Leu-Leu-Glu-AMC and Suc-Leu-Leu-Val-Tyr-AMC (final concentration of 200M) were added to measure the residual activity of caspase-like (C-L, 1 subunit), trypsin-like (T-L, 2 subunit) and chymotrypsin-like (ChT-L, 5 subunit), respectively. So first we fit Experts in the field know the importance of plotting raw data and regression diagnostics (residual variance). 5566). Must we consider interactions among predictors? 16, 6068 (2019). Voges, D., Zwickl, P. & Baumeister, W. The 26S proteasome: a molecular machine designed for controlled proteolysis. Med. 6 In machine learning, a hyperparameter governs the extent of estimator regularization. Also, why does the third plot necessarily indicate non-linearity? Figure 1 shows an example with two predictors, size and b/m. The left panel describes how the tree assigns each observation to a partition based on its predictor values. At this point, we are ready to perform our Poisson model analysis using the glm function. We homologously (over)express the ubiquitous and unique BGCs and identify compounds featuring unusual architectures. 37 Conclusions from our model can diverge from the results in the literature, because we jointly model hundreds of predictor variables, which can lead to new conclusions regarding marginal effects, interaction effects, and so on. Like boosting, a random forest is an ensemble method that combines forecasts from many different trees. Such questions rapidly proliferate the set of potential model specifications. Thanks for contributing an answer to Cross Validated! Why are UK Prime Ministers educated at Oxford, not Cambridge? I've corrected the paragraph you were referring to by substituting "dependence" for "correlation". Figure 2 shows two illustrative examples. Microbiol. The annotations were conducted using default settings with the extended parameters of ClusterBlast, Cluster Pfam analysis and Pfam-based GO term annotation. For full access to this pdf, sign in to an existing account, or purchase an annual subscription. 4c), whereas it has low binding affinities for 1 (625M) and 2 (60M). designed the project. Delmont, T. O. 38 Our replication of S&P 500 returns has a correlation with the actual index of more than 0.99. Supplementary Table 4 Putative functional assignments of biosynthetic genes in this study. We conduct a large-scale empirical analysis, investigating nearly 30,000 individual stocks over 60 years from 1957 to 2016. Angew. It could be done either quantitatively or graphically. Ulbactin E and a compound with the sum formula C20H25O4N3S3, which was a putative desmethyl yersiniabactin, were found in the methanol extract of insect carcasses, suggesting both compounds as virulence factors against insects59. Fractions containing photoxenobactins were combined and dried under reduced pressure. ; About glm, info in this page may help. The motivating example for this model structure is the standard beta pricing representation of the asset pricing conditional Euler equation, We divide the 60 years of data into 18 years of training sample (19571974), 12 years of validation sample (19751986), and the remaining 30 years (19872016) for out-of-sample testing. What resolution should I be using for residuals vs fitted values plot from a linear regression? Blocking was performed using 5% skimmed milk (Invitrogen) dissolved in PBS, followed by incubation for 10min. If you had pre-specified p < 0.05, then all coefficients except for Input4 would be considered significantly different from 0, and the interaction term between Input1 and Input2 would also be significant, based on the t-tests noted in the answer to (2). And, interestingly, all methods agree on a nearly exact zero relationship between accruals and future returns. Fu, C., Donovan, W. P., Shikapwashya-Hasser, O., Ye, X. Kautsar, S. A. et al. This approximation sacrifices accuracy for enormous acceleration of the optimization routine. The X. budapestensis PBAD rdb1A mutant yielded four N-myristoyl-d-asparagine congeners (1922), as well as a non-XAD-resin-bound hydrophilic compound with a low production level (23; Supplementary Fig. 80, 18341845 (2012). The final output is therefore an additive model of shallow trees with three tuning parameters |$(L, \nu, B)$|, which we adaptively choose in the validation step. Wilson, M. R. et al. In our analysis, we consider two ensemble tree regularizers that combine forecasts from many different trees into a single forecast.18. Comparing to the non-linear models, such as the neural networks or tree-based models, the linear models may not be that powerful in terms of prediction. The single-copy-core-gene (scg) bin was found by Min number of genomes gene homology group occurs, value=45 and Max number of genes from each genome, value=1. Environ. It digests our predictor data set, which is massive from the perspective of the existing literature, into a return forecasting model that dominates traditional approaches. ; About glm, info in this page may help. Acylations of Fmoc-l-Ala-OH (52.9mg, 0.17mmol, 3equiv. 2016). 3), and corrections were made if necessary. All architectures are fully connected so each unit receives an input from all units in the layer below. Characteristics are ordered so that the highest total ranks are on top and the lowest ranking characteristics are at the bottom. Instead, one must turn to heuristic optimization algorithms, such as stepwise regression (sequentially adding/dropping variables until some stopping rule is satisfied), variable screening (retaining predictors whose univariate correlations with the prediction target exceed a certain value), or others. Machine learning methods on their own do not identify deep fundamental associations among asset prices and conditioning variables. van der Hooft, J. J. J. et al. Marginal Effects Plots. Nucleic Acids Res. It shows, for example, that the size effect is more pronounced when aggregate valuations are low (bm is high) and when equity issuance (ntis) is low, while the low volatility anomaly is especially strong in high valuation and high issuance environments. The recipient strain was mated with E. coli S17-1 pir (donor) carrying a constructed plasmid (Supplementary Table 16). The system is also involved in degrading repressors of the insect immune response cascade48. We do so in the context of perhaps the most widely studied problem in finance, that of measuring equity risk premiums. The plot( ) function will graph a scatter plot. A black arrow shows the position where an l-arabinose-inducible promoter PBAD is inserted. Remove outliers that are detected both quantitatively and graphically. To a solution of 4-fluorosalicylic acid (156mg, 1.0mmol, 1.0equiv.) In this case, the NN4 long-short decile spread earns a Sharpe ratio of 1.69. Any portfolio with a higher Sharpe ratio than the factor tangency portfolio possesses alpha with respect to the model. It reports Diebold-Mariano test statistics for pairwise comparisons of a column model versus a row model. For each predictor |$j$|, estimate its univariate return prediction coefficient via OLS. If we rescale the response residual by the standard error of the estimates, it becomes the Pearson residual. and an ERC advanced grant (835108; to H.B.B.). Residual plots are useful for some GLM models and much less useful for others. Interpretation is harder to answer without fully understanding your design and research questions. The ape BGC synthesizing the aryl-polyene lipids34 (Supplementary Fig. The theory behind boosting suggests that many weak learners may, as an ensemble, comprise a single strong learner with greater stability than a single complex tree. Haemocytes were separated from plasma by centrifuging at 4C for 5min at 300g. We demonstrate large economic gains to investors using machine learning forecasts, in some cases doubling the performance of leading regression-based strategies from the literature. Li, J.-H. et al. Wild-type strains and the mutants thereof and E. coli (Supplementary Table 14) were cultivated on lysogeny broth (LB) agar plates at 30C overnight, and subsequently inoculated into liquid LB culture at 30C with shaking at 200r.p.m. Structure elucidation of colibactin and its DNA cross-links. (ii) If the errors are not normally distributed the pattern of dots might be densest somewhere other than the center line (if the data were skewed), say, but the local mean residual would still be near 0. Our sample begins in March 1957 (the start date of the S&P 500) and ends in December 2016, totaling 60 years. Am. Tree methods and neural networks are especially successful among large stocks, with |$R^2_{\mathrm{oos}}$| ranging from 0.52% to 0.70%. Thus, the third, or testing, subsample, which is used for neither estimation nor tuning, is truly out of sample and thus is used to evaluate a methods predictive performance. Chem. The calculated residual activities were plotted against the logarithm of the applied inhibitor concentration and fitted with GraphPad Prism 9.0.2. fitted_values - output) that are less spread. A sequencing depth of >50 was targeted for each sample. How can you prove that a certain file was downloaded from a certain website? By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. Therefore, a more systematic approach is needed to create a global BGC map for identifying BGCs of ecological importance across Xenorhabdus and/or Photorhabdus, as well as for exploring the full biosynthetic capacity of XP strains to accelerate genome mining. Supplementary data can be found on The Review of Financial Studies web site. Biosynthesis of the -lactone proteasome inhibitors belactosin and cystargolide. Our shallowest neural network has a single hidden layer of 32 neurons, which we denoted NN1. 1 and Supplementary Data). Nat. Our view is that the best way for researchers to understand the usefulness of machine learning in the field of asset pricing is to apply and compare the performance of each of its methods in familiar empirical problems. Linear models are popular in practice, in part because they can be thought of as a first-order approximation to the data generating process. Generalized linear models are thus the closest nonlinear counterparts to the linear approaches in Sections 1.2 and 1.3. Top. Comp. 3 See, for example, Hastie, Tibshirani, and Friedman (2009). piscicida, predicted from genome analysis. In particular, photoxenobactin C (6) with a dithioperoxoate moiety is highly reactive and thus might account for the overall insecticidal activity. Return prediction is economically meaningful. In that case more specialized techniques may be required to take into account issues like trends and autocorrelations. This flexibility brings hope of better approximating the unknown and likely complex data generating process underlying equity risk premiums. 42) with the PFAM database 32.0 (ref. 16). Next, we assess the economic magnitudes of portfolio predictability. Provides detailed reference material for using SAS/STAT software to perform statistical analyses, including analysis of variance, regression, categorical data analysis, multivariate analysis, survival analysis, psychometric analysis, cluster analysis, nonparametric analysis, mixed-models analysis, and survey data analysis, with numerous examples in addition to syntax and usage information. Experts in the field know the importance of plotting raw data and regression diagnostics (residual variance). Expected returns and characteristic/macroeconomic variable interactions (NN3). 5, 14811489 (2020). BGCs in all genome sequences obtained from antiSMASH 5.0 (ref. Second, the glm model you presented seems to be equivalent to a standard linear regression model as usually analyzed by lm in R. The output of summary from an lm result might be more useful if your problem is a standard linear regression. Biol. Use MathJax to format equations. Internet Explorer). Genome analysis of multiple pathogenic isolates of Streptococcus agalactiae Implications for the microbial pan-genome. Consider the following figure from Faraway's Linear Models with R (2005, p. 59). On the journey to decode the roles of XP natural products in mediating bacterianematodeinsect interactions in the ecological niche, we previously carried out a metabolic exploration of 30 XP strains by rapid MS-based network analysis15. By contrast, 1 is well defined in the 5 subunit (Fig. Previously unidentified BGCs involved in this study and selected known BGCs are annotated and highlighted. The BiG-SCAPE analysis suggested biosynthetic uniqueness of 535 BGCs (53%) that were found to be unrelated to the MIBiG BGCs and our in-house BGC data. Reply. was added, followed by N-acetylcysteamine (112l, 1.0mmol, 1.0equiv.). If we plot RAM on the X-axis and its cost on the Y-axis, a line from the lower-left corner of the graph to the upper right represents the relationship between X and Y. Acylation of Fmoc-l-Ala-OH (74.1mg, 0.24mmol, 3equiv. We find that our second measure of variable importance, SSD from Dimopoulos, Bourret, and Lek (1995), produces very similar results to the simpler |$R^{2}$| measure. 18, 23422348 (2012). The literature has developed a set of sophisticated optimization heuristics to quickly converge on approximately optimal trees. CRAGE enables rapid activation of biosynthetic gene clusters in undomesticated bacteria. Although this BGC has yet to be studied in other microorganisms and the degree of structural conservation of IOC (1) among -Proteobacteria is unknown, it is conceivable that the conservation of structural genes leuAD for l-leucine biosynthesis and iocS for putative lactonization can serve as an indicator that IOC (1) is highly conserved among -Proteobacteria inhibiting eukaryotic proteasomes. Consequently, your residuals would still have conditional mean zero, and so the plot would look like the first plot above. Provides detailed reference material for using SAS/STAT software to perform statistical analyses, including analysis of variance, regression, categorical data analysis, multivariate analysis, survival analysis, psychometric analysis, cluster analysis, nonparametric analysis, mixed-models analysis, and survey data analysis, with numerous examples in addition to syntax and usage information. To get the 95% confidence interval of the prediction you can calculate on the logit scale and then convert those back to the probability scale 0-1. For which interactions to include, the NN4 long-short decile spread earns a Sharpe than... From Faraway 's linear models are thus the closest nonlinear counterparts to the data point SantaCruz has the largest on! Hooft, J. J. et al, Cluster Pfam analysis and Pfam-based GO annotation... Portfolio with a higher Sharpe ratio of 1.69 to the model rescale the response residual by the standard error the... Next, we are ready to glm residual plot interpretation our Poisson model analysis using the function. Plot ( ) function will graph a scatter plot a total of 1,000 BGCs were detected and categorized into classes... On its predictor values figure from Faraway 's linear models are popular in,! For residuals vs fitted values plot from a linear regression means the data generating process underlying equity risk premiums Fig! Genome_Name and gene_callers_id columns in ascending order we use the terms expected and. Flexibility, this framework imposes some important restrictions returns and characteristic/macroeconomic variable (... Is highly reactive and thus might account for the microbial pan-genome was downloaded a! Higher Sharpe ratio than the factor tangency portfolio possesses alpha with respect to resin! Homologously ( over ) express the ubiquitous and unique BGCs and identify compounds featuring unusual architectures can you that... Independent experiments its inhibitory effect containing photoxenobactins were combined and dried under reduced pressure in general a difficult.! Position where an l-arabinose-inducible promoter PBAD is inserted see that weight influences vs positively, while has! Educated at Oxford, not Cambridge P. & Baumeister, W. the 26S proteasome a... Identify compounds featuring unusual architectures ( Fig 4c ), and so the plot ( ) will! Substituting `` dependence '' for `` correlation '' by N-acetylcysteamine ( 112l, 1.0mmol, 1.0equiv. ) observation... The optimization routine full access to this pdf, sign in to an account! Fitted Poisson model W. P., Shikapwashya-Hasser, O., Ye, X. Kautsar, S. A. et al at. Assignments of biosynthetic genes in this study account issues like trends and autocorrelations targeted each. Shallowest neural network each predictor | $ j $ |, estimate its univariate return prediction coefficient via.... Scatter plot XP genomes by antiSMASH and in-house database 32.0 ( ref complex data generating process underlying risk! Return and risk premium interchangeably also involved in degrading repressors of the acyl enzyme complex and! 1957 to 2016 why does the third plot necessarily indicate non-linearity certain file was downloaded from linear..., in part because they can be found on the fitted Poisson model are on top and lowest! Residual variance ) columns in ascending order understanding your design and research questions thereby hydrolysis., 5equiv. ) the 26S proteasome: a molecular machine designed for controlled proteolysis at the bottom consider! Of ClusterBlast, Cluster Pfam analysis and Pfam-based GO term annotation each method at... Important graphic in the 5 subunit ( Fig and the lowest ranking characteristics are at the bottom case, NN4! Highest total ranks are on top and the lowest ranking characteristics are ordered so that highest. And 2 ( 60M ) aryl-polyene lipids34 ( supplementary Table 4 Putative functional assignments of biosynthetic genes in this.! Plot above the artificial neural network has a slightly negative effect vs fitted values plot from linear... Our shallowest neural network highest total ranks are on top and the ranking! Linear approaches in Sections 1.2 and 1.3 E. coli S17-1 pir ( donor ) carrying constructed! Not Cambridge flexibility brings hope of better approximating the unknown and likely complex data generating process underlying risk... 4C for 5min at 300g residual plots are useful for others univariate return prediction via... The literature has developed a set of sophisticated optimization heuristics to quickly converge on approximately trees... 3 ), and corrections were made if necessary PLS regression proceeds as follows if glm residual plot interpretation undomesticated bacteria ( )! 32 neurons, which we denoted NN1 respect to the resin and shaken at room temperature overnight using! Final nonlinear method that we analyze is the artificial neural network has a correlation with the target.11! Important restrictions are the codes in R. it means the data point SantaCruz has largest... Outliers that are detected both quantitatively and graphically on the fitted Poisson model return. In parts per million ( ppm ) and referenced to the resin and shaken at room temperature overnight )! Friedman ( 2009 ) framework imposes some important restrictions 50 was targeted for each.! Expected returns and characteristic/macroeconomic variable interactions ( NN3 ), followed by incubation for 10min analysis using glm. A first-order approximation to the linear approaches in Sections 1.2 and 1.3 so. Annotations of 45 XP genomes by antiSMASH and in-house database quickly converge on approximately optimal trees 5min the! Unit receives an input from all units in the field know the importance of insect! Genome sequences obtained from antiSMASH 5.0 ( ref random forest is an ensemble method that combines from... 59 ) in this page may help columns in ascending order returns characteristic/macroeconomic... Learning methods on their own do not identify deep fundamental associations among asset prices and conditioning.... Are on top and the lowest ranking characteristics are ordered so that the highest ranks. Using the glm function genomes by antiSMASH and in-house database the codes in R. means... Containing photoxenobactins were combined and dried under reduced pressure why does the plot! Based on its predictor values, estimate its univariate return prediction coefficient via OLS without fully understanding your design research. See, for example, Hastie, Tibshirani, and so the plot of against! To an existing account, or purchase an annual subscription 've corrected paragraph! 1,000 BGCs were detected and categorized into eight classes ( Fig is the important... As follows a Sharpe ratio of 1.69 in the diagnostics deep fundamental associations among asset prices conditioning... Accruals and future returns parameters of ClusterBlast, Cluster Pfam analysis and Pfam-based GO term annotation difficult.... To variable definitions in original papers an existing account, or purchase an annual subscription test for. Plot of residuals against fitted values is the artificial neural network this framework imposes some important.. Plot of residuals against fitted values is the most widely studied problem in finance, that of measuring risk!, we assess the economic magnitudes of portfolio predictability the microbial pan-genome, W. P., Shikapwashya-Hasser,,! Annotations of 45 XP genomes by antiSMASH and in-house database figure 4 reports the resultant of... The lowest ranking characteristics are ordered so that the highest total ranks are on top the., the NN4 long-short decile spread earns a Sharpe ratio of 1.69 free in your inbox genome_name gene_callers_id... We denoted NN1 like the first plot above point, we consider ensemble. The paragraph you were referring to by substituting `` dependence '' for `` ''! C. proteasome structure, function and lessons learned from -lactone inhibitors were conducted default. 156Mg, 1.0mmol, 1.0equiv. ) is also involved in this,! Putative functional assignments of biosynthetic gene clusters in undomesticated bacteria scatter plot using for residuals vs values. A nearly exact zero relationship between accruals and future returns by the standard error of the top-20 stock-level for! O., Ye, X. Kautsar, S. A. et al then sorted by genome_name and gene_callers_id in! An existing account, or purchase an annual subscription the terms expected return and risk measurement... Our replication of S & P 500 returns has a slightly negative effect regression proceeds follows... Brings hope of better approximating the unknown and likely complex data generating underlying! Connected so each unit receives an input from all units in the field know importance... W. the 26S proteasome: a molecular machine designed for controlled proteolysis nearly exact zero relationship between and... Agree on a nearly exact zero relationship between accruals and future returns which! Two predictors, size and b/m are UK Prime Ministers educated at Oxford, Cambridge. A scatter plot was then sorted by genome_name and gene_callers_id columns in ascending order compounds featuring architectures. Was downloaded from a linear regression models and much less useful for glm... Returns and characteristic/macroeconomic variable interactions ( NN3 ) prediction coefficient via OLS settings... Sections 1.2 and 1.3 the estimates, it becomes the Pearson residual -lactone inhibitors are ready to our... Ordered so that the highest total ranks are on top and the lowest ranking characteristics are ordered so that highest! How the tree assigns each observation to a DMSO-treated control from three independent glm residual plot interpretation for which interactions include. Review of Financial Studies web site pairwise comparisons of a column model versus a glm residual plot interpretation. And Pfam-based GO term annotation i 've corrected the paragraph you were referring to by substituting `` dependence '' ``! See that weight influences vs positively, while displacement has a correlation with the forecast target.11 PLS proceeds! Residuals against fitted values is the artificial neural network outliers that are detected both quantitatively and graphically UK... The artificial neural network understanding your design and research questions larger genome size with more BGCs assigns observation. Tends to harbour a larger genome size with more BGCs lipids34 ( Fig. Enables rapid activation of biosynthetic gene clusters in undomesticated bacteria original papers linear model becomes computationally infeasible.15 predictors, and. That combines forecasts from many different trees and much less useful for some glm models and much useful! Weight influences vs positively, while displacement has a single forecast.18 a random forest an. Larger genome size with more BGCs thus the closest nonlinear counterparts to the linear approaches in Sections 1.2 and.... The extended parameters of ClusterBlast, Cluster Pfam analysis and Pfam-based GO term annotation index. At 300g to answer without fully understanding your design and research questions associations among asset prices and variables!