DNA polymerase is found in- cells and all viruses all viruses some vir.pdf
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DNA polymerase is found in: cells and all viruses all viruses some viruses cells cells and some viruses RNA-dependent RNA polymerase is found in: all viruses some viruses cells cells and all viruses cells and some viruses If a single new base is inserted into a gene and adds a single new base to mRNA in one location, the result will usually be: changing several amino acids in the protein a change in just one amino acid shortening of the protein because of the STOP signal no change in the amino acid cells and all viruses all viruses some viruses cells cells and some viruses .
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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "V0RhNGmBjFWt"
},
"outputs": [],
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"import numpy as np\n",
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{
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],
"metadata": {
"id": "Vq4OWcCNjJFj"
},
"execution_count": null,
"outputs": []
},
{
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"id": "EFDhIKURjPbB"
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{
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},
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{
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],
"metadata": {
"id": "ahd47p24jgrB"
},
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"outputs": []
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{
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],
"metadata": {
"id": "T-vtFS9Gjig2"
},
"execution_count": null,
"outputs": []
},
{
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"source": [
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" return torch.mean( (y_pred - y) ** 2 )"
],
"metadata": {
"id": "DpPaj2Vvjv5A"
},
"execution_count": null,
"outputs": []
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{
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"metadata": {
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"outputs": []
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{
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],
"metadata": {
"id": "Ml6UCAqujzvm"
},
"execution_count": null,
"outputs": []
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{
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"\n",
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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "V0RhNGmBjFWt"
},
"outputs": [],
"source": [
"import torch\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn"
]
},
{
"cell_type": "code",
"source": [
"# Creating a function f(X) with a slope of -5\n",
"X = torch.arange(-5, 5, 0.2).view(-1, 1)\n",
"func = -5 * X"
],
"metadata": {
"id": "Vq4OWcCNjJFj"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Plot the line in red with grids\n",
"plt.plot(X.numpy(), func.numpy(), 'r', label='func')\n",
"plt.xlabel('x')\n",
"plt.ylabel('y')\n",
"plt.legend()\n",
"plt.grid('True', color='y')\n",
"plt.show()"
],
"metadata": {
"id": "EFDhIKURjPbB"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Adding Gaussian noise to the function f(X) and saving it in Y\n",
"Y = func + 1.7 * torch.randn(X.size())"
],
"metadata": {
"id": "c2KRcFl5jRuy"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Plot and visualizing the data points in blue\n",
"plt.plot(X.numpy(), Y.numpy(), 'b+', label='Y')\n",
"plt.plot(X.numpy(), func.numpy(), 'r', label='func')\n",
"plt.xlabel('x')\n",
"plt.ylabel('y')\n",
"plt.legend()\n",
"plt.grid('True', color='y')\n",
"plt.show()"
],
"metadata": {
"id": "ahd47p24jgrB"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# defining the function for forward pass for prediction\n",
"def forward(x):\n",
" return w * x"
],
"metadata": {
"id": "T-vtFS9Gjig2"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# evaluating data points with Mean Square Error.\n",
"def criterion(y_pred, y):\n",
" return torch.mean( (y_pred - y) ** 2 )"
],
"metadata": {
"id": "DpPaj2Vvjv5A"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"w = torch.tensor(-10.0, requires_grad=True)"
],
"metadata": {
"id": "UMXfOHC0jx9G"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"step_size = 0.1\n",
"loss_list = []\n",
"iter = 20"
],
"metadata": {
"id": "Ml6UCAqujzvm"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"for i in range (iter):\n",
" # making predictions with forward pass\n",
" Y_pred = forward(X)\n",
"\n",
"\n",
" # calculating the loss between original and predicted data points\n",
" loss = criterion(Y_pred, Y)\n",
"\n",
"\n",
" # storing the calculated loss in a list\n",
" loss_list.append(loss.item())\n",
"\n",
"\n",
" # backward pass for .
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