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‎script/process_markdown.ipynb‎

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"cells": [
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{
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"execution_count": 1,
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"execution_count": 26,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd"
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"import pandas as pd\n",
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"from uuid import uuid5, NAMESPACE_URL\n"
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]
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},
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{
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{
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"for i, row in dccs.iterrows():\n",
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"\tmeta = [\"layout: ../../layouts/DCC.astro\"]\n",
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"\t\tif not k == 'description':\n",
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"\t\tdescription = ''\n",
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"\t\t\tif not k == 'description':\n",
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"\t\t\t\t\t\tv = \"https://cfde-drc.s3.amazonaws.com/assets\" + v\n",
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"\n",
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"\t\t\t\t\tmeta.append(\"%s: %s\"%(k, v))\n",
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"\t\t\telse:\n",
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"\t\t\t\tdescription = v\n",
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"\t\twith open('out/dccs/%s.md'%row['short_label'], 'w') as o:\n",
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"\t\t\to.write('---\\n')\n",
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"\t\t\to.write(\"\\n\".join(meta))\n",
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"\t\t\to.write('\\n---\\n')\n",
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"\t\t\to.write(description)"
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]
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},
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{
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"cell_type": "code",
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" <td>Computational screen to identify potential tar...</td>\n",
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" <td>Aging cell</td>\n",
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" <td>Deng EZ, Fleishman RH, Xie Z, Marino GB, Clark...</td>\n",
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" <td>2023</td>\n",
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" <td>e13809</td>\n",
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" <td>22</td>\n",
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" <td>6</td>\n",
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" <td>37082798.0</td>\n",
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" <td>[\"neoantigens\", \"Gene Expression Profiling\", \"...</td>\n",
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" <td>Contribution of Circulating Host and Microbial...</td>\n",
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" <td>International journal of tryptophan research :...</td>\n",
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" <td>Morgan EW, Dong F, Annalora AJ, Murray IA, Wol...</td>\n",
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" <td>[\"microbiome\", \"metabolomics\", \"indole\", \"Aryl...</td>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>Lactate-dependent transcriptional regulation c...</td>\n",
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" <td>Nature communications</td>\n",
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" <td>Takata N, Miska JM, Morgan MA, Patel P, Billin...</td>\n",
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" <td>Rahiminejad S, Maurya MR, Mukund K, Subramaniam S</td>\n",
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" <td></td>\n",
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" <td>False</td>\n",
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" <td>[\"Gene Expression Profiling\", \"Computational B...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>The stability of the myelinating oligodendrocy...</td>\n",
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" <td>Cell reports</td>\n",
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" <td>Pruvost M, Patzig J, Yattah C, Selcen I, Herna...</td>\n",
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" <td>2023</td>\n",
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" <td>112848</td>\n",
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" <td>42</td>\n",
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" <td>8</td>\n",
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" <td>PMC10600948</td>\n",
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" <td>10.1016/j.celrep.2023.112848</td>\n",
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" <td>...</td>\n",
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" <td>Metabolomics</td>\n",
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" <td></td>\n",
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" <td>False</td>\n",
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" <td>False</td>\n",
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" <td>[\"Chromatin\", \"Oligodendroglia\", \"progenitors\"...</td>\n",
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" </tbody>\n",
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"</table>\n",
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"<p>5 rows × 22 columns</p>\n",
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"</div>"
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],
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"text/plain": [
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" title \\\n",
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"0 Computational screen to identify potential tar... \n",
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"1 Contribution of Circulating Host and Microbial... \n",
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"2 Lactate-dependent transcriptional regulation c... \n",
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"3 Modular and mechanistic changes across stages ... \n",
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"4 The stability of the myelinating oligodendrocy... \n",
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"\n",
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" journal \\\n",
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"0 Aging cell \n",
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"1 International journal of tryptophan research :... \n",
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"2 Nature communications \n",
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"3 BMC cancer \n",
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"4 Cell reports \n",
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"\n",
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" authors year page \\\n",
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"0 Deng EZ, Fleishman RH, Xie Z, Marino GB, Clark... 2023 e13809 \n",
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"1 Morgan EW, Dong F, Annalora AJ, Murray IA, Wol... 2023 11786469231182510 \n",
415+
"2 Takata N, Miska JM, Morgan MA, Patel P, Billin... 2023 4129 \n",
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"3 Rahiminejad S, Maurya MR, Mukund K, Subramaniam S 2022 436 \n",
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"4 Pruvost M, Patzig J, Yattah C, Selcen I, Herna... 2023 112848 \n",
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"\n",
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" volume issue pmid pmcid doi ... \\\n",
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"0 22 6 37082798.0 PMC10265163 10.1111/acel.13809 ... \n",
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"1 16 37441265.0 PMC10334013 10.1177/11786469231182510 ... \n",
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"2 14 1 37452018.0 PMC10349100 10.1038/s41467-023-39672-2 ... \n",
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"3 22 1 35448980.0 PMC9022252 10.1186/s12885-022-09479-3 ... \n",
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"4 42 8 37515770.0 PMC10600948 10.1016/j.celrep.2023.112848 ... \n",
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"\n",
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" dccs partnerships carousel carousel_title carousel_link \\\n",
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"0 LINCS False \n",
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"1 Metabolomics False \n",
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"2 Metabolomics False \n",
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"3 Metabolomics False \n",
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"4 Metabolomics False \n",
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"\n",
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" carousel_description image featured r03 \\\n",
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"0 False \n",
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"1 False \n",
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"2 False \n",
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"3 False \n",
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"4 False \n",
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"\n",
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" keywords \n",
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"0 [\"neoantigens\", \"Gene Expression Profiling\", \"... \n",
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"1 [\"microbiome\", \"metabolomics\", \"indole\", \"Aryl... \n",
443+
"2 [\"Eye\", \"Gene Expression Regulation\", \"Mice\", ... \n",
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"3 [\"Gene Expression Profiling\", \"Computational B... \n",
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"4 [\"Chromatin\", \"Oligodendroglia\", \"progenitors\"... \n",
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"\n",
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"[5 rows x 22 columns]"
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]
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},
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"execution_count": 17,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"publications = pd.read_csv('data/publications.tsv', sep=\"\\t\")\n",
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"publications = publications[[i for i in publications.columns if not i == \"id\"]]\n",
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"publications = publications.fillna('')\n",
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"outputs": [
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"data": {
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"text/plain": [
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"((85, 22), 85)"
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]
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},
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"output_type": "execute_result"
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}
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],
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"source": [
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"publications.shape, publications.doi.nunique()"
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]
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{
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"outputs": [
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"np.int64(1)"
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}
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],
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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"metadata": {},
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"outputs": [],
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"source": [
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"for i, row in publications.iterrows():\n",
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"\tmeta = [\"layout: ../../layouts/Publication.astro\"]\n",
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"\tfor k,v in row.items():\n",
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"\t\tif not v == '':\n",
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"\t\t\tif k == 'dccs':\n",
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"\t\t\t\tv = v.split(\"; \")\n",
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"\n",
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"\t\t\tmeta.append(\"%s: %s\"%(k, v))\n",
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"\t\telse:\n",
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"\t\t\tdescription = description\n",
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"\twith open('out/dccs/%s.md'%row['short_label'], 'w') as o:\n",
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"\t\to.write('---')\n",
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"\tuid = str(uuid5(NAMESPACE_URL, row['title']))\n",
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"\twith open('out/publications/%s.md'%uid, 'w') as o:\n",
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"\t\to.write('---\\n')\n",
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"\t\to.write(\"\\n\".join(meta))\n",
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"\t\to.write('---')\n",
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"\t\to.write(description)"
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"\t\to.write('\\n---')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {

‎src/pages/publications/09e8de18-39c4-5e3f-b23b-90c09a03b4bb.md‎

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---
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layout: ../../layouts/Publication.astro
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title: Predictive analyses of regulatory sequences with EUGENe.
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journal: Nature Computational Science
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authors: Klie A; Laub D; Talwar JV; Stites H; Jores T; Solvason JJ; Farley EK; Carter H
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year: 2023
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page: 946-956
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volume: 3
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issue: 11
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pmid: 38177592.0
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pmcid: PMC10768637
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doi: 10.1038/s43588-023-00544-w
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landmark: False
14+
carousel: False
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featured: False
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keywords: ["Workflow", "Genome", "Genomics", "Software"]
17+
---

‎src/pages/publications/0c0e1b83-6f49-5f02-af06-20fb328c2d68.md‎

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---
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layout: ../../layouts/Publication.astro
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title: SenNet recommendations for detecting senescent cells in different tissues
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journal: Nature Reviews Molecular Cell Biology
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authors: Suryadevara V, Hudgins AD, Rajesh A, Pappalardo A, Karpova A, Dey AK, Hertzel A, Agudelo A, Rocha A, Soygur B, Schilling B, Carver CM, Aguayo-Mazzucato C, Baker DJ, Bernlohr DA, Jurk D, Mangarova DB, Quardokus EM, Enninga EAL, Schmidt EL, Chen F, Duncan FE, Cambuli F, Kaur G, Kuchel GA, Lee G, Daldrup-Link HE, Martini H, Phatnani H, Al-Naggar IM, Rahman I, Nie J, Passos JF, Silverstein JC, Campisi J, Wang J, Iwasaki K, Barbosa K, Metis K, Nernekli K, Niedernhofer LJ, Ding L, Wang L, Adams LC, Ruiyang L, Doolittle ML, Teneche MG, Schafer MJ, Xu M, Hajipour M, Boroumand M, Basisty N, Sloan N, Slavov N, Kuksenko O, Robson P, Gomez PT, Vasilikos P, Adams PD, Carapeto P, Zhu Q, Ramasamy R, Perez-Lorenzo R, Fan R, Dong R, Montgomery RR, Shaikh S, Vickovic S, Yin S, Kang S, Suvakov S, Khosla S, Garovic VD, Menon V, Xu Y, Song Y, Suh Y, Dou Z, Neretti N
6+
year: 2024
7+
pmid: 38831121.0
8+
doi: 10.1038/s41580-024-00738-8
9+
landmark: True
10+
dccs: ['SenNet']
11+
carousel: False
12+
featured: False
13+
keywords: []
14+
---

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