{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Tutorial 2: Protein DMS modeling using additive G-P maps\n", "\n", "This tutorial covers perhaps the simplest application of MAVE-NN: the modeling of DMS data using an additive genotype-phenotype (G-P) map together with a global epistasis (GE) measurement process. The code below steps users through this process, and can be used to train models similar to the following built-in models, which are accessible using `mavenn.load_example_model()`:\n", "\n", "\n", "- `'amyloid_additive_ge'`\n", "- `'tdp43_additive_ge'`\n", "- `'gb1_additive_ge'`" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2021-12-16T00:28:24.191016Z", "start_time": "2021-12-16T00:28:22.614488Z" } }, "outputs": [], "source": [ "# Standard imports\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "# Import MAVE-NN\n", "import mavenn" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training\n", "\n", "First we choose which dataset we wish to model, and we load it as a Pandas dataframe using `mavenn.load_example_dataset()`. We then compute the length of sequences in that dataset; we will need this quantity for defining the architecture of our model." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2021-12-16T00:28:24.217141Z", "start_time": "2021-12-16T00:28:24.192066Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading dataset 'tdp43' \n", "Sequence length: 84 amino acids (+ stops)\n", "data_df:\n" ] }, { "data": { "text/html": [ "
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" ], "text/plain": [ " validation dist y dy \\\n", "0 False 1 0.032210 0.037438 \n", "1 False 1 -0.009898 0.038981 \n", "2 False 1 -0.010471 0.005176 \n", "3 False 1 0.030803 0.005341 \n", "4 False 1 -0.054716 0.035752 \n", "... ... ... ... ... \n", "55160 False 2 -0.009706 0.035128 \n", "55161 True 2 -0.030744 0.029436 \n", "55162 True 2 -0.086802 0.033174 \n", "55163 False 2 -0.049587 0.029130 \n", "55164 False 2 -0.105390 0.031189 \n", "\n", " x \n", "0 NNSRGGGAGLGNNQGSNMGGGMNFGAFSINPAMMAAAQAALQSSWG... \n", "1 TNSRGGGAGLGNNQGSNMGGGMNFGAFSINPAMMAAAQAALQSSWG... \n", "2 RNSRGGGAGLGNNQGSNMGGGMNFGAFSINPAMMAAAQAALQSSWG... \n", "3 SNSRGGGAGLGNNQGSNMGGGMNFGAFSINPAMMAAAQAALQSSWG... \n", "4 INSRGGGAGLGNNQGSNMGGGMNFGAFSINPAMMAAAQAALQSSWG... \n", "... ... \n", "55160 GNSRGGGAGLGNNQGSNMGGGMNFGAFSINPAMMAAAQAALQSSWG... \n", "55161 GNSRGGGAGLGNNQGSNMGGGMNFGAFSINPAMMAAAQAALQSSWG... \n", "55162 GNSRGGGAGLGNNQGSNMGGGMNFGAFSINPAMMAAAQAALQSSWG... \n", "55163 GNSRGGGAGLGNNQGSNMGGGMNFGAFSINPAMMAAAQAALQSSWG... \n", "55164 GNSRGGGAGLGNNQGSNMGGGMNFGAFSINPAMMAAAQAALQSSWG... \n", "\n", "[55165 rows x 5 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Split dataset \n", "trainval_df, test_df = mavenn.split_dataset(data_df)\n", "\n", "# Preview trainval_df\n", "print('trainval_df:')\n", "trainval_df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we specify the architecture of the model we wish to train . To do this, we create an instance of the `mavenn.Model` class, called `model`, using the following keyword arguments:\n", "\n", "- `L=L` specifies the sequence length.\n", "- `alphabet='protein*'` specifies that the alphabet our sequences are built from consists of 21 characters representing the 20 amino acids plus a stop signal (which is represented by the character `'*'`). Other possible choices for `alphabet` are `'dna'`, `'rna'`, and `'protein'`.\n", "- `gpmap_type='additive'` specifies that we wish to infer an additive G-P map.\n", "- `regression_type='GE'` specifies that our model will have a global epistasis (GE) measurement process. We choose this because our MAVE measurements are continuous real numbers.\n", "- `ge_noise_model_type='SkewedT'` specifies the use of a skewed-t noise model in the GE measurement process. The `'SkewedT'` noise model can accommodate asymmetric noise and is thus more flexible than the default `'Gaussian'` noise model.\n", "- `ge_heteroskedasticity_order=2` specifies that the noise model parameters (the three parameters of the skewed-t distribution) are each modeled using quadratic functions of the predicted measurement $\\hat{y}$. This will allow our model of experimental noise to vary with signal intensity.\n", "\n", "We then set the training data by calling `model.set_data()`. The keyword argument `'validation_flags'` is used to specify which subset of the data in `trainval_df` will be used for validation (as opposed to stochastic gradient descent).\n", "\n", "Next we train the model by calling `model.fit()`. In doing so we specify a number of hyperparameters including the learning rate, the number of epochs, the batch size, whether to use early stopping, and the early stopping patience. We also set `verbose=False` to limit the amount of user feedback.\n", "\n", "Choosing hyperparameters is somewhat of an art, and the particular values used here were found by trial and error. In general users will have to try a number of different values for these and possibly other hyperparameters in order to find ones that work well. We recommend that users choose these hyperparameters in order to maximize the final value for `val_I_var`, the variational information of the trained model on the validation dataset." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2021-12-16T00:28:39.994843Z", "start_time": "2021-12-16T00:28:24.230012Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "N = 55,165 observations set as training data.\n", "Using 5.3% for validation.\n", "Data shuffled.\n", "Time to set data: 1.02 sec.\n", "Training time: 137.7 seconds\n" ] } ], "source": [ "# Define model\n", "model = mavenn.Model(L=L,\n", " alphabet='protein*',\n", " gpmap_type='additive', \n", " regression_type='GE',\n", " ge_noise_model_type='SkewedT',\n", " ge_heteroskedasticity_order=2)\n", "\n", "# Set training data\n", "model.set_data(x=trainval_df['x'],\n", " y=trainval_df['y'],\n", " validation_flags=trainval_df['validation'])\n", "\n", "# Train model\n", "model.fit(learning_rate=1e-3,\n", " epochs=500,\n", " batch_size=64,\n", " early_stopping=True,\n", " early_stopping_patience=25,\n", " verbose=False);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To assess the performance of our final trained model, we compute two different metrics on test data: **variational information** and **predictive information**. Variational information quantifies the performance of the full latent phenotype model (G-P map + measurement process), whereas predictive information quantifies the performance of just the G-P map. See Tareen et al. (2021) for an expanded discussion of these quantities.\n", "\n", "Note that MAVE-NN also estimates the standard errors for these quantities." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2021-12-16T00:28:40.048823Z", "start_time": "2021-12-16T00:28:39.995788Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "test_I_var: 1.841 +- 0.037 bits\n", "test_I_pred: 2.042 +- 0.023 bits\n" ] } ], "source": [ "# Compute variational information on test data\n", "I_var, dI_var = model.I_variational(x=test_df['x'], y=test_df['y'])\n", "print(f'test_I_var: {I_var:.3f} +- {dI_var:.3f} bits')\n", "\n", "# Compute predictive information on test data\n", "I_pred, dI_pred = model.I_predictive(x=test_df['x'], y=test_df['y'])\n", "print(f'test_I_pred: {I_pred:.3f} +- {dI_pred:.3f} bits')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To save the trained model we call `model.save()`. This records our model in **two separate files**: a pickle file that defines model architecture (extension `'.pickle'`), and an H5 file that records model parameters (extension `'.h5'`)." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2021-12-16T00:28:40.059965Z", "start_time": "2021-12-16T00:28:40.049653Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model saved to these files:\n", "\ttdp43_additive_ge.pickle\n", "\ttdp43_additive_ge.weights.h5\n" ] } ], "source": [ "# Save model to file\n", "model_name = f'{dataset_name}_additive_ge'\n", "model.save(model_name)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualization\n", "\n", "We now discuss how to visualize the training history, performance, and parameters of a trained model. First we then load our model using `mavenn.load`:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2021-12-16T00:28:40.113262Z", "start_time": "2021-12-16T00:28:40.061659Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model loaded from these files:\n", "\ttdp43_additive_ge.pickle\n", "\ttdp43_additive_ge.weights.h5\n" ] } ], "source": [ "# Delete model if it is present in memory\n", "try:\n", " del model\n", "except:\n", " pass\n", "\n", "# Load model from file\n", "model = mavenn.load(model_name)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `model.history` attribute is a dict that contains values for multiple model performance metrics as a function of training epoch, each evaluated on both the training data and the validation data. `'loss'` and `'val_loss'` record loss values, while `'I_var'` and `'val_I_var'` record the variational information values. As described in Tareen et al. (2021), these metrics are closely related: variational information is an affine transformation of the per-datum log likelihood, whereas loss is equal to negative log likelihood plus regularization terms." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2021-12-16T00:28:40.116769Z", "start_time": "2021-12-16T00:28:40.114766Z" } }, "outputs": [ { "data": { "text/plain": [ "dict_keys(['loss', 'val_loss', 'I_var', 'val_I_var'])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Show metrics recorded in model.history()\n", "model.history.keys()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plotting `'I_var'` and `'val_I_var'` versus epoch can often provide insight into the model training process. " ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2021-12-16T00:28:40.190144Z", "start_time": "2021-12-16T00:28:40.117563Z" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create figure and axes for plotting\n", "fig, ax = plt.subplots(1,1,figsize=[5,5])\n", "\n", "# Plot I_var_train, the variational information on training data as a function of epoch\n", "ax.plot(model.history['I_var'], \n", " label=r'I_var_train')\n", "\n", "# Plot I_var_val, the variational information on validation data as a function of epoch\n", "ax.plot(model.history['val_I_var'], \n", " label=r'val_I_var')\n", "\n", "# Show I_var_test, the variational information of the final model on test data\n", "ax.axhline(I_var, color='C2', linestyle=':', \n", " label=r'test_I_var')\n", "\n", "# Show I_pred_test, the predictive information of the final model on test data\n", "ax.axhline(I_pred, color='C3', linestyle=':', \n", " label=r'test_I_pred')\n", "\n", "# Style plot\n", "ax.set_xlabel('epochs')\n", "ax.set_ylabel('bits')\n", "ax.set_title('Training history: variational information')\n", "ax.set_ylim([0, 1.2*I_pred])\n", "ax.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Users can also plot '`loss`' and '`var_loss`' if they like, though the absolute values these quantities are be more difficult to interpret than `'I_var'` and `'val_I_var'`." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2021-12-16T00:28:40.245798Z", "start_time": "2021-12-16T00:28:40.190985Z" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create figure and axes for plotting\n", "fig, ax = plt.subplots(1,1,figsize=[5,5])\n", "\n", "# Plot loss_train, the loss computed on training data as a function of epoch\n", "ax.plot(model.history['loss'], \n", " label=r'loss_train')\n", "\n", "# Plot loss_val, the loss computed on validation data as a function of epoch\n", "ax.plot(model.history['val_loss'], \n", " label=r'val_I_var')\n", "\n", "# Style plot\n", "ax.set_xlabel(r'epochs')\n", "ax.set_ylabel(r'loss')\n", "ax.set_title(r'Training history: loss')\n", "ax.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is also useful to consider more traditional metrics of model performance. In the context of GE models, a natural choice is the squared Pearson correlation, $R^2$, between measurements $y$ and model predictions $\\hat{y}$:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2021-12-16T00:28:40.422767Z", "start_time": "2021-12-16T00:28:40.246599Z" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create figure and axes for plotting\n", "fig, ax = plt.subplots(1,1,figsize=[5,5])\n", "\n", "# Get test data y values\n", "y_test = test_df['y']\n", "\n", "# Compute yhat on test data\n", "yhat_test = model.x_to_yhat(test_df['x'])\n", "\n", "# Compute R^2 between yhat_test and y_test\n", "Rsq = np.corrcoef(yhat_test.ravel(), test_df['y'])[0, 1]**2\n", "\n", "# Plot y_test vs. yhat_test\n", "ax.scatter(yhat_test, y_test, color='C0', s=10, alpha=.3, \n", " label='test data')\n", "\n", "# Style plot\n", "xlim = [min(yhat_test), max(yhat_test)]\n", "ax.plot(xlim, xlim, '--', color='k', label='diagonal', zorder=100)\n", "ax.set_xlabel(r'model prediction ($\\hat{y}$)')\n", "ax.set_ylabel(r'measurement ($y$)')\n", "ax.set_title(rf'Standard metric of model performance:\\n$R^2$={Rsq:.3}');\n", "ax.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we visualize the GE measurement process inferred as part of our latent phenotype model. Recall from Tareen et al. (2021) that the measurement process consists of \n", " \n", "- A nonlinearity $\\hat{y} = g(\\phi)$ that deterministically maps the latent phenotype $\\phi$ to a prediction $\\hat{y}$.\n", "- A noise model $p(y|\\hat{y})$ that stochastically maps predictions $\\hat{y}$ to measurements $y$.\n", "\n", "We can conveniently visualize both of these quantities in a single \"global epistatsis plot\": " ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2021-12-16T00:28:40.508909Z", "start_time": "2021-12-16T00:28:40.423560Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create figure and axes for plotting\n", "fig, ax = plt.subplots(1,1,figsize=[5,5])\n", "\n", "# Get test data y values\n", "y_test = test_df['y']\n", "\n", "# Compute φ on test data\n", "phi_test = model.x_to_phi(test_df['x'])\n", "\n", "## Set phi lims and create a grid in phi space\n", "phi_lim = [min(phi_test)-.5, max(phi_test)+.5]\n", "phi_grid = np.linspace(phi_lim[0], phi_lim[1], 1000)\n", "\n", "# Compute yhat each phi gridpoint\n", "yhat_grid = model.phi_to_yhat(phi_grid)\n", "\n", "# Compute 95% CI for each yhat\n", "q = [0.025, 0.975]\n", "yqs_grid = model.yhat_to_yq(yhat_grid, q=q)\n", "\n", "\n", "# Plote 95% confidence interval\n", "ax.fill_between(phi_grid, yqs_grid[:, 0], yqs_grid[:, 1],\n", " alpha=0.2, color='C1', lw=0, label='95% CI')\n", "\n", "# Plot GE nonlinearity\n", "ax.plot(phi_grid, yhat_grid, \n", " linewidth=3, color='C1', label='nonlinearity')\n", "\n", "# Plot scatter of φ and y values,\n", "ax.scatter(phi_test, y_test,\n", " color='C0', s=10, alpha=.3, label='test data',\n", " zorder=-100, rasterized=True)\n", "\n", "# Style plot\n", "ax.set_xlim(phi_lim)\n", "ax.set_xlabel(r'latent phenotype ($\\phi$)')\n", "ax.set_ylabel(r'measurement ($y$)')\n", "ax.set_title(r'GE measurement process')\n", "ax.legend()\n", "fig.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To retrieve the values of our model's G-P map parameters, we use the method `model.get_theta()`. This returns a dictionary:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2021-12-16T00:28:40.528036Z", "start_time": "2021-12-16T00:28:40.509757Z" } }, "outputs": [ { "data": { "text/plain": [ "dict_keys(['L', 'C', 'alphabet', 'theta_0', 'theta_lc', 'theta_lclc', 'theta_mlp', 'logomaker_df'])" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Retrieve G-P map parameter dict and view dict keys\n", "theta_dict = model.get_theta(gauge='consensus')\n", "theta_dict.keys()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is important to appreciate that G-P maps usually have many non-identifiable directions in parameter space. These are called **gauge freedoms**. Interpreting the values of model parameters requires that we first \"pin down\" these gauge freedoms by using a clearly specified convention. Specifying `gauge='consensus'` in `model.get_theta()` accomplishes this fixing all the $\\theta_{l:c}$ parameters that contribute to the consensus sequence to zero. This convention allows all the other $\\theta_{l:c}$ parameters in the additive model to be interpreted as single-mutation effects, $\\Delta \\phi$, away from the consensus sequence.\n", "\n", "Finally, we use `mavenn.heatmap()` to visualize these additive parameters. This function takes a number of keyword arguments, which we summarize here. More information can be found in this function's docstring. \n", "\n", "- `ax=ax`: specifies the axes on which to draw both the heatmap and the colorbar. \n", "- `values=theta_dict['theta_lc']`: specifies the additive parameters in the form of a `np.array` of size `L`x`C`, where `C` is the alphabet size.\n", "- `alphabet=theta_dict['alphabet']`: provides a list of characters corresponding to the columns of `values`\n", "- `seq=model.x_stats['consensus_seq']`: causes `mavenn.heatmap()` to highlight the characters of a specific sequence of interest. In our case this is the consensus sequence, the additive parameters for which are all fixed to zero.\n", "- `seq_kwargs={'c':'gray', 's':25}`: provides a keyword dictionary to pass to `ax.scatter()`; this specifies how the characters of the sequence of interest are to be graphically indicated. \n", "- `cmap='coolwarm'`: specifies the colormap used to represent the values of the additive parameters.\n", "- `cbar=True`: specifies that a colorbar be drawn.\n", "- `cmap_size='2%'`: specifies the width of the colorbar relative to the enclosing ax object.\n", "- `cmap_pad=.3`: specifies the spacing between the heatmap and the colorbar.\n", "- `ccenter=0`: centers the colormap at zero. \n", "\n", "This function returns two objects: \n", "- `heatmap_ax` is the axes object on which the heatmap is drawn.\n", "- `cb` is the colorbar object; it's corresponding axes is given by `cb.ax`." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2021-12-16T00:28:40.643536Z", "start_time": "2021-12-16T00:28:40.529022Z" } }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Create figure\n", "fig, ax = plt.subplots(1,1, figsize=(12,5))\n", "\n", "# Draw heatmap\n", "heatmap_ax, cb = mavenn.heatmap(ax=ax,\n", " values=theta_dict['theta_lc'],\n", " alphabet=theta_dict['alphabet'],\n", " seq=model.x_stats['consensus_seq'],\n", " seq_kwargs={'c':'gray', 's':25},\n", " cmap='coolwarm',\n", " cbar=True,\n", " cmap_size='2%',\n", " cmap_pad=.3,\n", " ccenter=0)\n", "# Style heatmap (can be different between two dataset)\n", "#heatmap_ax.set_xticks()\n", "heatmap_ax.tick_params(axis='y', which='major', pad=10)\n", "heatmap_ax.set_xlabel(r'position ($l$)')\n", "heatmap_ax.set_ylabel(r'amino acid ($c$)')\n", "heatmap_ax.set_title(rf'Additive parameters: {model_name}')\n", "\n", "# Style colorbar\n", "cb.outline.set_visible(False)\n", "cb.ax.tick_params(direction='in', size=20, color='white')\n", "cb.set_label(r'mutational effect ($\\Delta \\phi$)', labelpad=5, rotation=-90, ha='center', va='center')\n", "\n", "# Adjust figure and show\n", "fig.tight_layout(w_pad=5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that many of the squares in the heatmap are white. These correspond to additive parameters whose values are `NaN`. MAVE-NN sets the values of a feature effect to `NaN` when no variant in the training set exhibits that feature. Such `NaN` parameters are common as DMS libraries often do not contain a comprehensive set of single-amino-acid mutations." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## References\n", "\n", "1. Bolognesi B, Faure AJ, Seuma M, Schmiedel JM, Tartaglia GG, Lehner B. The mutational landscape of a prion-like domain. Nat Commun 10:4162 (2019).\n", "\n", "2. Olson CA, Wu NC, Sun R. A comprehensive biophysical description of pairwise epistasis throughout an entire protein domain. Curr Biol 24:2643–2651 (2014).\n", "\n", "3. Seuma M, Faure A, Badia M, Lehner B, Bolognesi B. The genetic landscape for amyloid beta fibril nucleation accurately discriminates familial Alzheimer’s disease mutations. eLife 10:e63364 (2021).\n", "\n", "4. Tareen A, Kooskhbaghi M, Posfai A, Ireland WT, McCandlish DM, Kinney JB. MAVE-NN: learning genotype-phenotype maps from multiplex assays of variant effect. bioRxiv doi:10.1101/2020.07.14.201475 (2020)." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.8" }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 4 }