No-Code & Agentic AI for Life Sciences
Master state-of-the-art Generative AI, autonomous research agents, transcriptomics pipelines, and AI drug target discovery — without writing a single line of code.
Cohort Overview & Timeline
Schedule
2 Sessions/Week · 8:30 PM – 10:30 PM (BDT)
Cohort Size
Limited to 35 interactive seats for personalized review
Capstone Project
Complete end-to-end biological research workflow
Transform Your Research with No-Code AI
Three interconnected pillars designed to empower wet-lab scientists with computational superpowers.
1. Generative AI & Prompt Engineering
Master the RTFC framework (Role, Task, Format, Context) and the P-A-E-I workflow to generate bulletproof experiment protocols, statistical summaries, and publication drafts.
2. Autonomous Literature Agents
Deploy AI agents with PubMed monitors and NotebookLM to synthesize 100+ papers, extract gene-disease associations, and generate meta-analysis matrices in minutes.
3. No-Code Omics & Target Discovery
Execute end-to-end RNA-seq, single-cell clustering, biomarker machine learning, and AlphaFold 3D drug target validations through visual GUI workflows.
4-Week Curriculum & Practical Roadmap
Each session combines conceptual foundations with hands-on live walkthroughs and concrete research deliverables.
No-Code Tools Setup & Foundations
Setting up essential no-code environments, understanding generative AI models, tokens, multimodal capabilities, and foundations of Agentic AI in Life Sciences.
Prompt & Context Engineering (RTFC & P-A-E-I)
Structuring high-precision biological directives using the RTFC framework, managing context windows, few-shot prompting, and deploying the P-A-E-I workflow for wet-lab protocols.
Agentic Literature Review & Systematic Synthesis
Building autonomous research pipelines with PubMed monitors, Consensus, Elicit, SciSpace, and source-grounded synthesis using NotebookLM.
No-Code Omics Data Analysis: Bulk RNA-Seq
No-code RNA-seq pipelines from raw count matrices to differential expression with DESeq2, volcano plots, and agentic biological pathway interpretation (GSEA).
Single-Cell (scRNA-Seq) & Spatial Exploration
Exploring cell-level heterogeneity with Scanpy and Seurat web apps, quality control, dimensional reduction, cell-type annotation, and spatial marker mapping.
Deep Learning: Biomarker & Clinical Prediction
Intuitive neural network architectures, tabular clinical modeling, biomarker classification without code, and evaluating ROC-AUC and model interpretability.
AI in Drug Discovery & Target Validation
Accelerating discovery pipelines with Open Targets, ChEMBL bioactivity filters, AlphaFold 3D structure visualization, and generative molecular design.
Biomedical Image Analysis & Capstone Presentations
Computer vision intuition for microscopy and pathology imaging, AI triage workflows, and final project defense with live peer and mentor review.
Tools You Will Master in Hands-On Labs
Industry-standard life science software and cutting-edge generative AI models.
ChatGPT & GPT-4o
Protocol design & workflow reasoning
Claude 3.5 Sonnet
Complex analytical pipelines & document synthesis
NotebookLM
Multi-paper citation & query answering
Consensus & Elicit
Evidence extraction from 200M+ papers
Scanpy Web GUI
scRNA-seq clustering & visual explorer
PyDESeq2 / Galaxy
Differential expression & volcano plotting
AlphaFold DB
3D protein structure analysis
Open Targets
Genomic target validation & genetics evidence
Md. Jubayer Hossain
Founder, DeepBio Academy · Lead AI & Computational Biology Mentor
Jubayer has mentored over 3,000+ researchers, medical doctors, and students across 20+ universities in Bioinformatics, AI Drug Discovery, and Data Science. His research spans transcriptomics, cancer genomics, and applied AI in biomedical discovery.
Frequently Asked Questions
Everything you need to know about the No-Code AI for Life Sciences cohort.
Ready to Supercharge Your Life Science Research?
Join the upcoming cohort to gain practical, publication-ready AI tools, 1-on-1 mentor feedback, and verifiable certification.