Medical_DNA_Analyzer.ipynb

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      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "#Medical-DNA-Analyzer👨‍⚕️🧬🩺❤\n",
        "**A python based tool for simplified DNA analysis focused on medical applications that would be more practical for doctors and healthcare providers.**"
      ],
      "metadata": {
        "id": "uEU3be0aRMxn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install Biopython"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fYYwASdtQUbm",
        "outputId": "2d8bdca5-ce72-4114-f374-741a3b4982a7"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting Biopython\n",
            "  Downloading biopython-1.84-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from Biopython) (1.26.4)\n",
            "Downloading biopython-1.84-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.2 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.2/3.2 MB\u001b[0m \u001b[31m36.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: Biopython\n",
            "Successfully installed Biopython-1.84\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "AtLxwdz2P-ig"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from Bio.Seq import Seq\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from collections import Counter\n",
        "import re\n",
        "\n",
        "class MedicalDNAAnalyzer:\n",
        "    \"\"\"\n",
        "    DNA analysis tool designed for medical professionals with focus on clinical relevance\n",
        "    \"\"\"\n",
        "    def __init__(self, sequence):\n",
        "        self.sequence = sequence.upper().replace('\\n', '')\n",
        "        self.length = len(sequence)\n",
        "\n",
        "    def check_mutations(self):\n",
        "        \"\"\"\n",
        "        Check for common disease-associated mutations\n",
        "        Returns positions and types of mutations found\n",
        "        \"\"\"\n",
        "        # Common disease-associated mutations (example patterns)\n",
        "        mutation_patterns = {\n",
        "            'BRCA1_mutation': {\n",
        "                'pattern': 'GTAAA',\n",
        "                'associated_condition': 'Breast Cancer Risk',\n",
        "                'clinical_significance': 'Pathogenic',\n",
        "                'recommendation': 'Recommend genetic counseling and enhanced screening'\n",
        "            },\n",
        "            'Cystic_Fibrosis': {\n",
        "                'pattern': 'CTTT',\n",
        "                'associated_condition': 'Cystic Fibrosis',\n",
        "                'clinical_significance': 'Pathogenic',\n",
        "                'recommendation': 'Refer to CF specialist for confirmation'\n",
        "            },\n",
        "            'Lynch_Syndrome': {\n",
        "                'pattern': 'GCCAT',\n",
        "                'associated_condition': 'Lynch Syndrome',\n",
        "                'clinical_significance': 'Likely Pathogenic',\n",
        "                'recommendation': 'Consider colonoscopy screening'\n",
        "            }\n",
        "        }\n",
        "\n",
        "        findings = []\n",
        "        for mutation_name, info in mutation_patterns.items():\n",
        "            matches = list(re.finditer(info['pattern'], self.sequence))\n",
        "            if matches:\n",
        "                for match in matches:\n",
        "                    findings.append({\n",
        "                        'mutation': mutation_name,\n",
        "                        'position': match.start(),\n",
        "                        'sequence': match.group(),\n",
        "                        'condition': info['associated_condition'],\n",
        "                        'significance': info['clinical_significance'],\n",
        "                        'recommendation': info['recommendation']\n",
        "                    })\n",
        "\n",
        "        return findings\n",
        "\n",
        "    def analyze_risk_factors(self):\n",
        "        \"\"\"\n",
        "        Analyze genetic risk factors for common diseases\n",
        "        \"\"\"\n",
        "        risk_patterns = {\n",
        "            'cardiovascular': {\n",
        "                'pattern': 'AAGG',\n",
        "                'risk_level': 'Moderate',\n",
        "                'recommendation': 'Consider lipid panel and lifestyle modifications'\n",
        "            },\n",
        "            'diabetes_type2': {\n",
        "                'pattern': 'CCTA',\n",
        "                'risk_level': 'Elevated',\n",
        "                'recommendation': 'Regular blood glucose monitoring recommended'\n",
        "            },\n",
        "            'alzheimers': {\n",
        "                'pattern': 'TGCT',\n",
        "                'risk_level': 'Increased',\n",
        "                'recommendation': 'Monitor cognitive function in regular check-ups'\n",
        "            }\n",
        "        }\n",
        "\n",
        "        risk_assessment = []\n",
        "        for condition, info in risk_patterns.items():\n",
        "            matches = list(re.finditer(info['pattern'], self.sequence))\n",
        "            if matches:\n",
        "                risk_assessment.append({\n",
        "                    'condition': condition,\n",
        "                    'risk_level': info['risk_level'],\n",
        "                    'matches_found': len(matches),\n",
        "                    'recommendation': info['recommendation']\n",
        "                })\n",
        "\n",
        "        return risk_assessment\n",
        "\n",
        "    def check_drug_metabolism(self):\n",
        "        \"\"\"\n",
        "        Analyze genetic markers related to drug metabolism\n",
        "        \"\"\"\n",
        "        metabolism_markers = {\n",
        "            'CYP2D6': {\n",
        "                'pattern': 'ATGG',\n",
        "                'metabolism': 'Rapid metabolizer',\n",
        "                'clinical_impact': 'May require dose adjustment for certain medications'\n",
        "            },\n",
        "            'CYP2C19': {\n",
        "                'pattern': 'TCGA',\n",
        "                'metabolism': 'Poor metabolizer',\n",
        "                'clinical_impact': 'Consider alternative medications or adjusted dosing'\n",
        "            },\n",
        "            'TPMT': {\n",
        "                'pattern': 'GCTA',\n",
        "                'metabolism': 'Intermediate metabolizer',\n",
        "                'clinical_impact': 'Monitor drug levels closely'\n",
        "            }\n",
        "        }\n",
        "\n",
        "        metabolism_results = []\n",
        "        for gene, info in metabolism_markers.items():\n",
        "            matches = list(re.finditer(info['pattern'], self.sequence))\n",
        "            if matches:\n",
        "                metabolism_results.append({\n",
        "                    'gene': gene,\n",
        "                    'metabolism_type': info['metabolism'],\n",
        "                    'clinical_impact': info['clinical_impact']\n",
        "                })\n",
        "\n",
        "        return metabolism_results\n",
        "\n",
        "    def generate_clinical_report(self):\n",
        "        \"\"\"\n",
        "        Generate a clinical report with relevant findings\n",
        "        \"\"\"\n",
        "        mutations = self.check_mutations()\n",
        "        risks = self.analyze_risk_factors()\n",
        "        metabolism = self.check_drug_metabolism()\n",
        "\n",
        "        report = {\n",
        "            'summary': {\n",
        "                'total_mutations_found': len(mutations),\n",
        "                'risk_factors_identified': len(risks),\n",
        "                'metabolism_findings': len(metabolism)\n",
        "            },\n",
        "            'clinical_findings': {\n",
        "                'mutations': mutations,\n",
        "                'risk_factors': risks,\n",
        "                'drug_metabolism': metabolism\n",
        "            },\n",
        "            'recommendations': []\n",
        "        }\n",
        "\n",
        "        # Add clinical recommendations based on findings\n",
        "        for mutation in mutations:\n",
        "            report['recommendations'].append({\n",
        "                'type': 'Mutation',\n",
        "                'finding': mutation['condition'],\n",
        "                'action': mutation['recommendation']\n",
        "            })\n",
        "\n",
        "        for risk in risks:\n",
        "            report['recommendations'].append({\n",
        "                'type': 'Risk Factor',\n",
        "                'finding': risk['condition'],\n",
        "                'action': risk['recommendation']\n",
        "            })\n",
        "\n",
        "        for met in metabolism:\n",
        "            report['recommendations'].append({\n",
        "                'type': 'Drug Metabolism',\n",
        "                'finding': met['gene'],\n",
        "                'action': met['clinical_impact']\n",
        "            })\n",
        "\n",
        "        return report\n",
        "\n",
        "    def visualize_findings(self):\n",
        "        \"\"\"\n",
        "        Create visual summary of findings for clinical review\n",
        "        \"\"\"\n",
        "        plt.figure(figsize=(15, 10))\n",
        "\n",
        "        # 1. Mutation Summary\n",
        "        plt.subplot(2, 2, 1)\n",
        "        mutations = self.check_mutations()\n",
        "        mutation_types = [m['condition'] for m in mutations]\n",
        "        if mutation_types:\n",
        "            sns.countplot(x=mutation_types)\n",
        "            plt.title('Detected Mutations')\n",
        "            plt.xticks(rotation=45)\n",
        "        else:\n",
        "            plt.text(0.5, 0.5, 'No mutations detected', ha='center')\n",
        "\n",
        "        # 2. Risk Factor Summary\n",
        "        plt.subplot(2, 2, 2)\n",
        "        risks = self.analyze_risk_factors()\n",
        "        risk_levels = [r['risk_level'] for r in risks]\n",
        "        if risk_levels:\n",
        "            sns.countplot(x=risk_levels)\n",
        "            plt.title('Risk Levels Distribution')\n",
        "            plt.xticks(rotation=45)\n",
        "        else:\n",
        "            plt.text(0.5, 0.5, 'No risk factors identified', ha='center')\n",
        "\n",
        "        # 3. Drug Metabolism Profile\n",
        "        plt.subplot(2, 2, 3)\n",
        "        metabolism = self.check_drug_metabolism()\n",
        "        metabolism_types = [m['metabolism_type'] for m in metabolism]\n",
        "        if metabolism_types:\n",
        "            sns.countplot(x=metabolism_types)\n",
        "            plt.title('Drug Metabolism Profile')\n",
        "            plt.xticks(rotation=45)\n",
        "        else:\n",
        "            plt.text(0.5, 0.5, 'No metabolism markers found', ha='center')\n",
        "\n",
        "        plt.tight_layout()\n",
        "        plt.show()\n",
        "\n",
        "    def print_clinical_summary(self):\n",
        "        \"\"\"\n",
        "        Print a concise clinical summary\n",
        "        \"\"\"\n",
        "        report = self.generate_clinical_report()\n",
        "\n",
        "        print(\"=== Clinical DNA Analysis Summary ===\\n\")\n",
        "\n",
        "        print(\"Key Findings:\")\n",
        "        print(f\"- Mutations detected: {report['summary']['total_mutations_found']}\")\n",
        "        print(f\"- Risk factors identified: {report['summary']['risk_factors_identified']}\")\n",
        "        print(f\"- Drug metabolism markers: {report['summary']['metabolism_findings']}\\n\")\n",
        "\n",
        "        if report['clinical_findings']['mutations']:\n",
        "            print(\"Important Mutations:\")\n",
        "            for mutation in report['clinical_findings']['mutations']:\n",
        "                print(f\"- {mutation['condition']}: {mutation['significance']}\")\n",
        "                print(f\"  Recommendation: {mutation['recommendation']}\\n\")\n",
        "\n",
        "        if report['clinical_findings']['risk_factors']:\n",
        "            print(\"Risk Factors:\")\n",
        "            for risk in report['clinical_findings']['risk_factors']:\n",
        "                print(f\"- {risk['condition']}: {risk['risk_level']}\")\n",
        "                print(f\"  Recommendation: {risk['recommendation']}\\n\")\n",
        "\n",
        "        if report['clinical_findings']['drug_metabolism']:\n",
        "            print(\"Drug Metabolism Profile:\")\n",
        "            for met in report['clinical_findings']['drug_metabolism']:\n",
        "                print(f\"- {met['gene']}: {met['metabolism_type']}\")\n",
        "                print(f\"  Clinical Impact: {met['clinical_impact']}\\n\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Evaluation using any random patient sequence 🧬"
      ],
      "metadata": {
        "id": "ROiD0B3tQjAN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create analyzer instance\n",
        "patient_sequence = sequence = \"\"\"\n",
        "    MVSKLSQLQTELLAALLESGLSKEALIQALGEPGPYLLAGEGPLDKGESCGGGRGELAEL\n",
        "PNGLGETRGSEDETDDDGEDFTPPILKELENLSPEEAAHQKAVVETLLQEDPWRVAKMVK\n",
        "SYLQQHNIPQREVVDTTGLNQSHLSQHLNKGTPMKTQKRAALYTWYVRKQREVAQQFTHA\n",
        "GQGGLIEEPTGDELPTKKGRRNRFKWGPASQQILFQAYERQKNPSKEERETLVEECNRAE\n",
        "CIQRGVSPSQAQGLGSNLVTEVRVYNWFANRRKEEAFRHKLAMDTYSGPPPGPGPGPALP\n",
        "AHSSPGLPPPALSPSKVHGVRYGQPATSETAEVPSSSGGPLVTVSTPLHQVSPTGLEPSH\n",
        "SLLSTEAKLVSAAGGPLPPVSTLTALHSLEQTSPGLNQQPQNLIMASLPGVMTIGPGEPA\n",
        "SLGPTFTNTGASTLVIGLASTQAQSVPVINSMGSSLTTLQPVQFSQPLHPSYQQPLMPPV\n",
        "QSHVTQSPFMATMAQLQSPHALYSHKPEVAQYTHTGLLPQTMLITDTTNLSALASLTPTK\n",
        "QVFTSDTEASSESGLHTPASQATTLHVPSQDPASIQHLQPAHRLSASPTVSSSSLVLYQS\n",
        "SDSSNGQSHLLPSNHSVIETFISTQMASSSQ\n",
        "    \"\"\".replace(\"\\n\", \"\").replace(\" \", \"\")\n",
        "dna_analyzer = MedicalDNAAnalyzer(patient_sequence)\n",
        "\n",
        "# Get clinical report\n",
        "report = dna_analyzer.generate_clinical_report()\n",
        "\n",
        "# Print easy-to-read summary\n",
        "dna_analyzer.print_clinical_summary()\n",
        "\n",
        "# Show visual summary\n",
        "dna_analyzer.visualize_findings()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "cSFEReqOQwxW",
        "outputId": "52500322-390a-4040-f2a9-bc6f3b429292"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "=== Clinical DNA Analysis Summary ===\n",
            "\n",
            "Key Findings:\n",
            "- Mutations detected: 0\n",
            "- Risk factors identified: 1\n",
            "- Drug metabolism markers: 0\n",
            "\n",
            "Risk Factors:\n",
            "- cardiovascular: Moderate\n",
            "  Recommendation: Consider lipid panel and lifestyle modifications\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##This simplified version focuses on three key medical aspects:\n",
        "\n",
        "**Mutation Detection:**\n",
        "\n",
        "Checks for common disease-associated mutations\n",
        "Provides clinical significance and recommendations\n",
        "Easy-to-understand results\n",
        "\n",
        "\n",
        "**Risk Factor Analysis:**\n",
        "\n",
        "Evaluates genetic risk factors for common diseases\n",
        "Provides risk levels and recommendations\n",
        "Focuses on actionable insights\n",
        "\n",
        "\n",
        "**Drug Metabolism Analysis:**\n",
        "\n",
        "Analyzes genetic markers related to drug metabolism\n",
        "Helps with medication decisions\n",
        "Provides clear clinical recommendations"
      ],
      "metadata": {
        "id": "SlgkvfbCSl1z"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kFFiAFK5S2rx"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}