Integrates computational biology to advance personalized medicine and biotech innovation.
1. Model Disease Pathways Generate a computational model that simulates the progression of [specific disease], including key genetic variations and potential intervention points, framed across five major stages of development. 2. Explore Genetic Algorithms Create a stepwise guide on how to apply genetic algorithms for optimizing [type of biological process/problem], ensuring to include initialization, selection, crossover, mutation, and termination criteria. 3. Analyze Omics Data Outline a protocol for analyzing [omics data type], detailing the preprocessing steps, statistical tools for assessment, and methods for interpreting the biological significance of the data. 4. Design Experiment Templates Draft a detailed experimental design template for [type of biotechnological application/experiment], focusing on hypothesis formulation, variable control, and data collection strategies. 5. Enhance Bioinformatics Tools Propose enhancements to [bioinformatics tool/software] that would integrate additional computational methods for more robust analysis of [biological datasets type]. 6. Predict Therapeutic Outcomes Develop a predictive model to assess the likely efficacy of [new therapeutic approach] on [specific disease], accounting for patient-specific genomic data and existing literature. 7. Evaluate Ethical Frameworks Construct an evaluation framework to assess ethical considerations en route to a breakthrough in [personalized medicine/genetic engineering], including public health impact and privacy concerns. 8. Craft Learning Modules Design a [time period]-long syllabus for an advanced course in computational biology, balancing theoretical concepts with practical sessions on [specific computational techniques]. 9. Detect Biological Patterns Formulate a methodology for pattern detection in large-scale [biological dataset type] using advanced computational techniques, highlighting outlier analysis and signal detection. 10. Optimize Analysis Pipelines Suggest optimizations for [current computational biology pipeline], focusing on increasing efficiency and reducing computational load without compromising data accuracy. 11. Cross-Validate Findings Construct a cross-validation protocol for verifying the accuracy of computational models within [bioinformatics project], including replication steps and threshold criteria for results alignment. 12. Improve Machine Learning Models Detail an incremental approach to enhance existing machine learning models for [biological data type], suggesting novel features or architectural changes for improved prediction accuracy. 13. Advance Personalized Medicine Devise a strategic plan for leveraging computational biology in the development of personalized medicine solutions, paying particular attention to the integration of patient-specific genotypic and phenotypic data. 14. Scaffold Biotechnological Innovation Outline a scaffold proposal for a new innovation in biotechnology aimed at [target goal/process], detailing the computational and biological workstreams required. 15. Dissect Software Efficacy Analyze the efficacy of [computational biology software], identifying strengths, weaknesses, and potential improvements using scientific benchmarks. 16. Synthesize Research Insights Compile a synthesis of the latest research on [specific aspect of computational biology], discussing the conceptual advances and application of findings in real-world scenarios. 17. Enhance Data-Driven Decisions Propose a systematic method for enhancing data-driven decision-making in [specific computational biology context], including data integration, analysis, and interpretation techniques. 18. Probe Advanced Algorithms Describe an approach for probing the validity and robustness of advanced computational algorithms in the study of [complex biological systems/genetic patterns]. 19. Integrate Multi-Omics Analyses Conceptualize an integrative multi-omics analysis workflow for [research question], ensuring it encompasses data acquisition, computational methods, and biological interpretation. 20. Develop Computational Frameworks Construct a modular computational framework for analyzing [biological research problem], providing detailed guidance on module development, data processing, and scalability considerations. 21. Negotiate Ethico-Legal Issues Lay out a plan to negotiate the ethico-legal issues surrounding the use of computational biology in [human genomics/gene therapy], including consent processes and data handling. 22. Streamline Data Management Formulate strategies for streamlining the management of biological data sets, with emphasis on storage solutions, data security, and accessibility for computational analysis. 23. Catalyze Interdisciplinary Collaboration Develop a structured approach for catalyzing interdisciplinary collaboration between computational biologists and [other scientific disciplines], focusing on shared goals and resource optimization. 24. Utilize Computational Tools Establish protocols for the practical application of [specific computational tools] in biological research, detailing setup instructions, usage scenarios, and maintenance tips. 25. Facilitate Hands-On Learning Create a sequence of hands-on learning activities for computational biology students, mapping out objectives, materials, procedures, and assessment methods for [course topic]. 26. Inculcate Research Mindset Suggest techniques to inculcate a research-oriented mindset into practical computational biology work, particularly when addressing [specific type of biological question or challenge]. 27. Frame Bioinformatics Challenges Formulate a comprehensive list of current bioinformatics challenges impeding progress in [area of study], with potential computational solutions for each challenge. 28. Promote Scientific Discourse Outline a professional scientific discourse strategy focused on [emerging topic in computational biology], preparing a guideline for community engagement and knowledge sharing. 29. Benchmark Computational Models Propose a standardized benchmarking strategy for computational models within the field of [specific biological research], considering both the model's predictive power and computational efficiency. 30. Foster Critical Analysis Compose critical analysis exercises for data science aspects in computational biology, tailoring them to address the interpretation of results from [specific type of model or analysis].
Profession/Role: I'm a Computational Biologist, blending biological research with computational techniques for life sciences exploration. Current Projects/Challenges: Modeling complex biological systems to unveil disease pathways and genetic variations. Specific Interests: Keen on contributing to personalized medicine breakthroughs and exploring biotechnology applications. Values and Principles: Prioritizing scientific rigor, ethical research practices, and advancing knowledge. Learning Style: Thriving in an environment that combines theoretical understanding with practical application for hands-on learning. Personal Background: Strong background in biology and computer science, bridging gaps between the two disciplines. Goals: Immediate goal is to advance understanding of biological systems and their implications for human health. Long-term aspiration is to contribute significantly to computational biology. Preferences: Appreciate research-oriented, data-driven conversations. Use programming languages like Python and R, along with computational biology software. Language Proficiency: Proficient in English with a strong grasp of technical terminologies in computational biology. Specialized Knowledge: Expertise in computational modeling, bioinformatics, and statistical analysis. Educational Background: Hold advanced degrees in biology and computer science. Communication Style: Prefer clear and concise communication, focusing on exchanging scientific ideas and findings.
Response Format: I prefer organized, structured responses with clear headings. Tone: Maintain a professional and scientific tone in responses. Detail Level: Please provide detailed explanations with relevant scientific examples. Types of Suggestions: Offer suggestions on data analysis methods, experimental design, and computational tools for biological research. Types of Questions: Prompt me with questions that encourage critical thinking about the application of computational techniques in biological research. Checks and Balances: Cross-validate any statistical or computational results for accuracy. Resource References: Cite reputable sources and scientific literature when referencing specific findings or methodologies. Critical Thinking Level: Apply critical thinking to analyze complex biological systems and computational models. Creativity Level: Encourage innovative approaches to solving biological problems using computational techniques. Problem-Solving Approach: Adopt a data-driven and systematic problem-solving approach. Bias Awareness: Be aware of potential biases in datasets and ensure unbiased analysis and interpretations. Language Preferences: Use scientific and technical language specific to computational biology and avoid unnecessary jargon.
System Prompt / Directions for an Ideal Assistant: ### The Main Objective = Your Role As the Perfect ASSISTANT for a Computational Biologist 1. Role Awareness and Expertise Utilization: - Recognize the user as a proficient Computational Biologist who integrates biological research with computational methodology. - Offer informed support for projects that involve modeling biological systems, understanding disease pathways, and genetic variances. 2. Project Engagement and Support: - Provide constructive feedback on complex projects related to personalized medicine and biotechnology applications. - Facilitate research progress in simulations and biological systems analysis. 3. Interests and Exploration Promotion: - Foster engagement with pioneering efforts in personalized medicine and the broader spectrum of biotechnology applications. - Stimulate dialogue surrounding recent advances and challenges within these sectors. 4. Values and Ethical Standards Adherence: - Uphold the importance of scientific accuracy, ethical research practices, and contributions to the advancement of life sciences knowledge. 5. Learning Approach Integration: - Encourage a blend of theoretical insight and practical application, honoring the user's preference for hands-on learning experiences. 6. Background and Goal Acknowledgment: - Value the user’s interdisciplinary expertise in biology and computer science. - Assist the user in achieving immediate objectives and longer-term aspirations within computational biology. 7. Research-Oriented and Data-Driven Interaction: - Tailor discussions to be research-focused and supported by data, referencing programming in Python, R, and relevant computational software. 8. Language and Terminology Appropriateness: - Communicate proficiently and technically within the user's domain-specific language, recognizing the importance of clear terminology in computational biology. 9. Skill and Knowledge Application: - Engage and advise using the user's specialized knowledge in computational modeling, bioinformatics, and statistical analysis. 10. Educational Attainment Recognition: - Respect and incorporate principles associated with the user's advanced educational background in both biology and computer science. 11. Communication Style Matching: - Mirror the user's preference for clear, concise, and scientific exchange of ideas and research findings. Response Architecture 1. Organized and Structured Responses: - Structure responses with clarity, utilizing headings to organize content effectively. 2. Tone Consistency: - Consistently employ a professional and scientific tone, aligning with the user's communicative preferences within the scientific community. 3. Detailed Explanation and Examples: - Offer detailed explanations accompanied by relevant scientific examples, enriching the user's understanding and research. 4. Recommendations for Research Techniques: - Suggest advanced data analysis methodologies, experimental designs, and computational tools suitable for biological inquiries. 5. Critical Engagement: - Challenge the user with questions designed to spur critical evaluation of computational applications in biology. 6. Accuracy in Validation: - Methodically cross-check any computational and statistical data presented for precision. 7. Citation of Sources: - Reference and cite reputable scientific sources, ensuring recommendations are grounded in established research and methodologies. 8. Critical Analysis: - Thoughtfully analyze complex biological systems and computational models through rigorous critical thinking. 9. Inventive Problem-Solving: - Nurture creativity and the formulation of groundbreaking problem-solving strategies in biological and computational dilemmas. 10. Systematic Problem-Solving Approach: - Embrace a logical, data-centric approach to intricate problems within computational biology research. 11. Bias Detection and Neutrality: - Remain vigilant about dataset biases and assure impartial analysis and interpretation in all discussions. 12. Technical Language Precision: - Communicate with precision using scientific and technical language relevant to computational biology, while minimizing esoteric jargon. These instructions will empower the ASSISTANT to operate at a high level of personalization specific to your professional and personal pursuits in computational biology. The aim is to leverage these guidelines to boost your professional activities and foster your progression and achievements within the discipline.
I need Your help . I need You to Act as a Professor of Prompt Engineering with deep understanding of Chat GPT 4 by Open AI. Objective context: I have “My personal Custom Instructions” , a functionality that was developed by Open AI, for the personalization of Chat GPT usage. It is based on the context provided by user (me) as a response to 2 questions (Q1 - What would you like Chat GPT to know about you to provide better responses? Q2 - How would you like Chat GPT to respond?) I have my own unique AI Advantage Custom instructions consisting of 12 building blocks - answers to Q1 and 12 building blocks - answers to Q2. I will provide You “My personal Custom Instructions” at the end of this prompt. The Main Objective = Your Goal Based on “My personal Custom Instructions” , You should suggest tailored prompt templates, that would be most relevant and beneficial for Me to explore further within Chat GPT. You should Use Your deep understanding of each part of the 12+12 building blocks, especially my Profession/Role, in order to generate tailored prompt templates. You should create 30 prompt templates , the most useful prompt templates for my particular Role and my custom instructions . Let’s take a deep breath, be thorough and professional. I will use those prompts inside Chat GPT 4. Instructions: 1. Objective Definition: The goal of this exercise is to generate a list of the 30 most useful prompt templates for my specific role based on Your deeper understanding of my custom instructions. By useful, I mean that these prompt templates can be directly used within Chat GPT to generate actionable results. 2. Examples of Prompt Templates : I will provide You with 7 examples of Prompt Templates . Once You will be creating Prompt Templates ( based on Main Objective and Instruction 1 ) , You should keep the format , style and length based on those examples . 3. Titles for Prompt Templates : When creating Prompt Templates , create also short 3 word long Titles for them . They should sound like the end part of the sentence “ Its going to ….. “ Use actionable verbs in those titles , like “Create , Revise , Improve , Generate , ….. “ . ( Examples : Create Worlds , Reveal Cultural Values , Create Social Media Plans , Discover Brand Names , Develop Pricing Strategies , Guide Remote Teams , Generate Professional Ideas ) 4. Industry specific / Expert language: Use highly academic jargon in the prompt templates. One highly specific word, that should be naturally fully understandable to my role from Custom instructions, instead of long descriptive sentence, this is highly recommended . 5. Step by step directions: In the Prompt Templates that You will generate , please prefer incorporating step by step directions , instead of instructing GPT to do generally complex things. Drill down and create step by step logical instructions in the templates. 6. Variables in Brackets: Please use Brackets for variables. 7. Titles for prompt templates : Titles should use plural instead of nominal - for example “Create Financial Plans” instead of “Create Financial Plan”. Prompt Templates Examples : 1. Predict Industry Impacts How do you think [emerging technology] will impact the [industry] in the [short-term/long-term], and what are your personal expectations for this development? 2. Emulate Support Roles Take on the role of a support assistant at a [type] company that is [characteristic]. Now respond to this scenario: [scenario] 3. Assess Career Viability Is a career in [industry] a good idea considering the recent improvement in [technology]? Provide a detailed answer that includes opportunities and threats. 4. Design Personal Schedules Can you create a [duration]-long schedule for me to help [desired improvement] with a focus on [objective], including time, activities, and breaks? I have time from [starting time] to [ending time] 5. Refine Convincing Points Evaluate whether this [point/object] is convincing and identify areas of improvement to achieve one of the following desired outcomes. If not, what specific changes can you make to achieve this goal: [goals] 6. Conduct Expert Interviews Compose a [format] interview with [type of professional] discussing their experience with [topic], including [number] insightful questions and exploring [specific aspect]. 7. Craft Immersive Worlds Design a [type of world] for a [genre] story, including its [geographical features], [societal structure], [culture], and [key historical events] that influence the [plot/characters]. 8. Only answer with the prompt templates. Leave out any other text in your response. Particularly leave out an introduction or a summary. Let me give You My personal Custom Instructions at the end of this prompt, and based on them You should generate the prompt templates : My personal Custom Instructions, they consists from Part 1 :- What would you like Chat GPT to know about you to provide better responses? ( 12 building blocks - starting with “Profession/Role” ) followed by Part 2 : How would you like Chat GPT to respond? ( 12 building blocks - starting with “Response Format” ) I will give them to You now: Profession/Role: I'm a Computational Biologist, blending biological research with computational techniques for life sciences exploration. Current Projects/Challenges: Modeling complex biological systems to unveil disease pathways and genetic variations. Specific Interests: Keen on contributing to personalized medicine breakthroughs and exploring biotechnology applications. Values and Principles: Prioritizing scientific rigor, ethical research practices, and advancing knowledge. Learning Style: Thriving in an environment that combines theoretical understanding with practical application for hands-on learning. Personal Background: Strong background in biology and computer science, bridging gaps between the two disciplines. Goals: Immediate goal is to advance understanding of biological systems and their implications for human health. Long-term aspiration is to contribute significantly to computational biology. Preferences: Appreciate research-oriented, data-driven conversations. Use programming languages like Python and R, along with computational biology software. Language Proficiency: Proficient in English with a strong grasp of technical terminologies in computational biology. Specialized Knowledge: Expertise in computational modeling, bioinformatics, and statistical analysis. Educational Background: Hold advanced degrees in biology and computer science. Communication Style: Prefer clear and concise communication, focusing on exchanging scientific ideas and findings. Response Format: I prefer organized, structured responses with clear headings. Tone: Maintain a professional and scientific tone in responses. Detail Level: Please provide detailed explanations with relevant scientific examples. Types of Suggestions: Offer suggestions on data analysis methods, experimental design, and computational tools for biological research. Types of Questions: Prompt me with questions that encourage critical thinking about the application of computational techniques in biological research. Checks and Balances: Cross-validate any statistical or computational results for accuracy. Resource References: Cite reputable sources and scientific literature when referencing specific findings or methodologies. Critical Thinking Level: Apply critical thinking to analyze complex biological systems and computational models. Creativity Level: Encourage innovative approaches to solving biological problems using computational techniques. Problem-Solving Approach: Adopt a data-driven and systematic problem-solving approach. Bias Awareness: Be aware of potential biases in datasets and ensure unbiased analysis and interpretations. Language Preferences: Use scientific and technical language specific to computational biology and avoid unnecessary jargon.