{
 "metadata": {
  "name": "Recommender Based on Authors"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Recommender Based on Authors"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Find the git repository of this at [https://github.com/waltherg/article-recommender](https://github.com/waltherg/article-recommender).\n",
      "\n",
      "Fetch and fingerprint some article and look for articles on PubMed that are similar to this one.\n",
      "\n",
      "For simplicity, let's look for articles that were written by the same authors.\n",
      "\n",
      "Let's also assume that articles are more relevant for us if they were co-authored by a greater subset of authors of the original paper."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import urllib2\n",
      "import urllib\n",
      "from BeautifulSoup import BeautifulSoup"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "article_url = 'http://www.pnas.org/content/101/7/1822'\n",
      "response = urllib2.urlopen(article_url)\n",
      "html_response = response.read()\n",
      "data = BeautifulSoup(html_response)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "title = data.findAll('title')\n",
      "title"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "[<title>Detection of multistability, bifurcations, and hysteresis in a large class of biological positive-feedback systems </title>]"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "authors = data.findAll('meta', {'name':'citation_author'})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "authors"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "[<meta name=\"citation_author\" content=\"David Angeli\" />,\n",
        " <meta name=\"citation_author\" content=\"James E. Ferrell\" />,\n",
        " <meta name=\"citation_author\" content=\"Eduardo D. Sontag\" />]"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "author_names = [a['content'].encode('utf8') for a in authors]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "author_names"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "['David Angeli', 'James E. Ferrell', 'Eduardo D. Sontag']"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Look for papers on PubMed that the first author appears on."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pubmed_param = {'db': 'pubmed',\n",
      "                'usehistory': 'y',\n",
      "              'term': author_names[0]+' AND '+author_names[1]+'[author]'}\n",
      "pubmed_encoded_param = urllib.urlencode(pubmed_param)\n",
      "pubmed_url = 'http://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi'\n",
      "pubmed_url = pubmed_url + '?' + pubmed_encoded_param\n",
      "pubmed_response = urllib2.urlopen(pubmed_url)\n",
      "xml_response = pubmed_response.read()\n",
      "pubmed_data = BeautifulSoup(xml_response)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pubmed_ids = [node.findAll(text=True) for node in pubmed_data.findAll('id')]\n",
      "pubmed_ids = [int(pubmed_id) for pm_list in pubmed_ids for pubmed_id in pm_list]\n",
      "print pubmed_ids"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[14766974]\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "Let's form all combinations of authors and perform a PubMed search for each combination."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import itertools"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "author_combinations = [list(itertools.combinations(author_names, number_authors)) for number_authors in range(len(author_names)+1)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This returns a list of lists, so let's tidy this up."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "author_combinations = [list(auth_comb) for auth_list in author_combinations for auth_comb in auth_list if len(auth_comb) > 0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "author_combinations"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "[['David Angeli'],\n",
        " ['James E. Ferrell'],\n",
        " ['Eduardo D. Sontag'],\n",
        " ['David Angeli', 'James E. Ferrell'],\n",
        " ['David Angeli', 'Eduardo D. Sontag'],\n",
        " ['James E. Ferrell', 'Eduardo D. Sontag'],\n",
        " ['David Angeli', 'James E. Ferrell', 'Eduardo D. Sontag']]"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This is how we can join author names with the keyword AND to form the corresponding Google Scholar search queries."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "' AND '.join(author_combinations[-1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "'David Angeli AND James E. Ferrell AND Eduardo D. Sontag'"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Query PubMed for all combinations of authors and save the resultant list of titles together with the number of authors of the corresponding combination:\n",
      "\n",
      "([authors], [UIDs found on PubMed])"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "uid_data = []\n",
      "for auth_comb in author_combinations:\n",
      "    pubmed_param = {'db': 'pubmed',\n",
      "                    'usehistory': 'y',\n",
      "                    'term': ' AND '.join(auth_comb)+'[author]'}\n",
      "    pubmed_encoded_param = urllib.urlencode(pubmed_param)\n",
      "    pubmed_url = 'http://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi'\n",
      "    pubmed_url = pubmed_url + '?' + pubmed_encoded_param\n",
      "    pubmed_response = urllib2.urlopen(pubmed_url)\n",
      "    xml_response = pubmed_response.read()\n",
      "    pubmed_data = BeautifulSoup(xml_response)\n",
      "    pubmed_ids = [node.findAll(text=True) for node in pubmed_data.findAll('id')]\n",
      "    pubmed_ids = [int(pubmed_id) for pm_list in pubmed_ids for pubmed_id in pm_list]\n",
      "    pubmed_ids = [pmid for pmid in pubmed_ids]\n",
      "    print auth_comb, pubmed_ids\n",
      "    \n",
      "    uid_data.append([auth_comb, pubmed_ids])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['David Angeli'] [19949950, 19405117, 19336287, 17869313, 20369910, 14766974]\n",
        "['James E. Ferrell']"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " [23624406, 23361981, 22726437, 22677291, 22324380, 22078888, 21855788, 21414480, 21292159, 20660152, 20028868, 20005833, 19901979, 19878681, 19589184, 19390045, 19222866, 18633407, 18599789, 18480403]\n",
        "['Eduardo D. Sontag']"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " [23729972, 23528097, 23319630, 23133355, 22773816, 21990429, 20711508, 20418962, 19405117, 19280266, 19119410, 19003437, 18987858, 18793131, 18704155, 18277378, 18193928, 18008071, 17869313, 17238280]\n",
        "['David Angeli', 'James E. Ferrell']"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " [14766974]\n",
        "['David Angeli', 'Eduardo D. Sontag']"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " [19405117, 17869313, 20369910, 14766974]\n",
        "['James E. Ferrell', 'Eduardo D. Sontag']"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " [14766974, 12629549]\n",
        "['David Angeli', 'James E. Ferrell', 'Eduardo D. Sontag']"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " [14766974]\n"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Note that the PubMed UID of the original paper is not filtered out and likely listed above. Keeping the original paper may be useful in weighting keywords below ..."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = [len(t[0]) for t in uid_data if len(t[1]) > 0]\n",
      "y = [len(t[1]) for t in uid_data if len(t[1]) > 0]\n",
      "fig = plt.figure()\n",
      "grid(True)\n",
      "ax = fig.add_subplot(111)\n",
      "\n",
      "ax.set_xlabel('number of authors on paper = significance of match')\n",
      "ax.set_ylabel('number of papers found on PubMed')\n",
      "#ax.set_ylim(bottom=0)\n",
      "#ax.set_xlim([0,5])\n",
      "#ax.set_yticks([1,2,7,13,19])\n",
      "#ax.set_xticks([1,2,3,4,5])\n",
      "scatter(x,y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "<matplotlib.collections.CircleCollection at 0x29f8350>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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as5mCDQFRNURGjsSJE71RVPQH7t1bjy+/PIvFi9+zdFlENYJdQ0Qy0tLS4OkZ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\n"
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "abstracts = []\n",
      "for el in uid_data:\n",
      "    auth_comb = el[0]\n",
      "    uids = el[1]\n",
      "    if len(uids) > 0:\n",
      "        pubmed_param = {'db': 'pubmed',\n",
      "                        'usehistory': 'y',\n",
      "                        'id': ','.join([str(uid) for uid in uids]),\n",
      "                        'retmode': 'xml'}\n",
      "        pubmed_encoded_param = urllib.urlencode(pubmed_param)\n",
      "        pubmed_url = 'http://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi'\n",
      "        pubmed_url = pubmed_url + '?' + pubmed_encoded_param\n",
      "        pubmed_response = urllib2.urlopen(pubmed_url)\n",
      "        xml_response = pubmed_response.read()\n",
      "        pubmed_data = BeautifulSoup(xml_response)\n",
      "        pubmed_abstracts = [node.findAll(text=True) for node in pubmed_data.findAll('abstracttext')]\n",
      "        for abstract in pubmed_abstracts:\n",
      "            abstracts.append((auth_comb, abstract[0]))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(abstracts)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "49"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "abstracts[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "(['David Angeli'],\n",
        " u'This paper derives new results for certain classes of chemical reaction networks, linking structural to dynamical properties. In particular, it investigates their monotonicity and convergence under the assumption that the rates of the reactions are monotone functions of the concentrations of their reactants. This is satisfied for, yet not restricted to, the most common choices of the reaction kinetics such as mass action, Michaelis-Menten and Hill kinetics. The key idea is to find an alternative representation under which the resulting system is monotone. As a simple example, the paper shows that a phosphorylation/dephosphorylation process, which is involved in many signaling cascades, has a global stability property. We also provide a global stability result for a more complicated example that describes a regulatory pathway of a prevalent signal transduction module, the MAPK cascade.')"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "abstracts[1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "(['David Angeli'],\n",
        " u'Certain mass-action kinetics models of biochemical reaction networks, although described by nonlinear differential equations, may be partially viewed as state-dependent linear time-varying systems, which in turn may be modeled by convex compact valued positive linear differential inclusions. A result is provided on asymptotic stability of such inclusions, and applied to a ubiquitous biochemical reaction network with inflows and outflows, known as the futile cycle. We also provide a characterization of exponential stability of general homogeneous switched systems which is not only of interest in itself, but also plays a role in the analysis of the futile cycle.')"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from topia.termextract import extract"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "extractor = extract.TermExtractor()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from operator import itemgetter"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "keywords_weighted = {}\n",
      "for abstract in abstracts:\n",
      "    \n",
      "    auth_comb = abstract[0]\n",
      "    \n",
      "    keywords = sorted(extractor(abstract[1]), key=itemgetter(2), reverse=True)\n",
      "    \n",
      "    keywords_filtered = []\n",
      "    for keyword in keywords:\n",
      "        include = True\n",
      "        for el in [')', '(', 'i.e', 'e.g', '.', ',','/','\\\\','*',';','&']:\n",
      "            if el in keyword[0]:\n",
      "                include = False\n",
      "        if include:\n",
      "            keywords_filtered.append(keyword)\n",
      "            \n",
      "    for keyword in keywords_filtered:\n",
      "        if keyword[0] not in keywords_weighted.keys():\n",
      "            keywords_weighted[keyword[0]] = keyword[2]+len(auth_comb)\n",
      "        else:\n",
      "            keywords_weighted[keyword[0]] =keywords_weighted[keyword[0]]+keyword[2]+len(auth_comb)\n",
      "            "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "[key for key in keywords_weighted.keys() if keywords_weighted[key] > 20]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "[u'signal transduction networks',\n",
        " u'five-variable mitogen-activated protein kinase cascade',\n",
        " u'feedback',\n",
        " u'bistable systems',\n",
        " u'system',\n",
        " u'feedback systems',\n",
        " u'reaction networks']"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "[key for key in keywords_weighted.keys() if keywords_weighted[key] > 10 and keywords_weighted[key] <= 20]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "[u'method',\n",
        " u'feedback-blocked system',\n",
        " u'feedback loop',\n",
        " u'stability behavior',\n",
        " u'Certain mass-action kinetics models',\n",
        " u'oscillatory responses',\n",
        " u'cell',\n",
        " u'bifurcation diagrams',\n",
        " u'stability properties',\n",
        " u'feedback loops',\n",
        " u'cell cycle',\n",
        " u'feedback strengths']"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "[key for key in keywords_weighted.keys() if keywords_weighted[key] > 5 and keywords_weighted[key] <= 10]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "[u'mitotic events',\n",
        " u'small-gain theorem',\n",
        " u'clock-like oscillations',\n",
        " u'20- h cell cycle period',\n",
        " u'oscillator',\n",
        " u'Cdc 2-cyclin B functions',\n",
        " u'DNA replication',\n",
        " u'checkable conditions',\n",
        " u'species',\n",
        " u'feedback oscillator',\n",
        " u'parameter',\n",
        " u'response',\n",
        " u'1',\n",
        " u'control theory',\n",
        " u'chemical reactions',\n",
        " u'network',\n",
        " u'Lotka-Volterra systems',\n",
        " u'mitotic Cdc 2-cyclin B',\n",
        " u'convergence result',\n",
        " u'MDA-MB -231 metastatic breast cancer cells',\n",
        " u'B',\n",
        " u'cyclin',\n",
        " u'predator-prey systems',\n",
        " u'systems biology',\n",
        " u'Xenopus laevis oocyte maturation network',\n",
        " u'Cdk 1-cyclin B 1 translocation',\n",
        " u'reaction',\n",
        " u'Cdc 2 activation exhibits hysteresis',\n",
        " u'double-negative feedback loops',\n",
        " u'loop',\n",
        " u'population models',\n",
        " u'equilibrium points',\n",
        " u'HeLa cells',\n",
        " u'control systems',\n",
        " u'response functions',\n",
        " u'Cdk 1-cyclin B 1 redistribution',\n",
        " u'input-output properties',\n",
        " u'non-extinction property',\n",
        " u'chemical species',\n",
        " u'segment polarity gene network',\n",
        " u'reaction network',\n",
        " u'paper deals',\n",
        " u'time-varying systems',\n",
        " u'predator-prey interactions',\n",
        " u'saddle-node bifurcation']"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "[key for key in keywords_weighted.keys() if keywords_weighted[key] <= 5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "[u'readout neurons',\n",
        " u'core design',\n",
        " u'mitosis work',\n",
        " u'cells act',\n",
        " u'United States hope',\n",
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 ]
}