AI ML in Biology, Bioinformatics & Computational Biology Industrial Training Program With Project Work + Paper Publication Assistance
Admissions Open For Industrial Training Program on Artificial Intelligence & Machine Learning (AI ML) in Bioinformatics, Biology & Computational Biology With Project Work
Self-Learning Hands-on Training Program with Mentor Support
ONLINE + OFFLINE
EXCLUSIVE OPPORTUNITY TO WORK IN
- AL ML IN DRUG DISCOVERY PROJECTS
- CANCER & DISEASES PREDICTION USING AI ML
- PROTEIN STRUCTURE PREDICTION USING AI ML
- GENOMICS TRANSCRIPTOMICS PROJECTS & Much More
Work on Real-Time Projects (3,6 & 12 Months)
With Paper Publication Assistance
All Participants Under 6 Months & 12 Months of Training Will get a Work Experience Letter.
LIVE INDUCTION SESSION TO BE HELD ON 11th August 2026
Welcome to our 30-day Summer Training Program on AI & ML in Biology & Bioinformatics - Computational Biology! Explore the synergy between science and technology, delving into AI and ML's applications in understanding biological complexities. Engage in real-time projects and receive guidance for paper publication. Completion of the program earns participants a Work Experience Letter, acknowledging their dedication and proficiency. Join us for an intensive journey into the forefront of AI, ML, and computational biology!
A Roadmap to Success:
Experience a 30-day immersive journey where participants will engage in hands-on projects and explore the dynamic intersection of AI, ML, and biology. Gain insights and skills to tackle complex challenges in genomics, proteomics, and drug discovery.
Program Highlights:
- Real-Time Projects: Work on practical projects to apply AI and ML concepts in biology and bioinformatics.
- Paper Publication Assistance: Receive guidance and support for transforming research findings into impactful publications.
- Work Experience Letter: Participants completing the six-month or twelve Month training will be awarded a Work Experience Letter.
Join us in this transformative journey, where innovation knows no bounds, and together, let's push the boundaries of possibility in the realm of AI, ML, and computational biology.
Program Overview
30-Day Training
- Program Type: Self-Learning (Online) for Classes with LIVE projects
- LIVE Induction Class date: 11th August 2026
- PROJECT - LIVE directly under our Scientists
- Course Access Duration: 40 Days (Project students will receive course access for the entire duration of their project.)
- Mentor Support: (Doubt Solving, guidance, feedback)
- Certification: Yes
3, 6 & 12 Months Project Guidance Sessions
- Start Date: After Completion of the training program
- Project work: 3,6, & 12 months duration
- Work Experience Letter - Under 6 & 12 Months Project
-
Reference Letter - Under 12 Months Project
Who Can Attend?
This program is ideal for students and professionals in B.Sc., M.Sc., B.Tech., M.Tech., B.Pharm, M.Pharm, Ph.D., and Post-Doc courses related to Life Sciences and Biotech who are keen on expanding their knowledge and skills in AI and ML applications in Biology & Bioinformatics. Chemistry candidates with basic understanding of biology concepts can also apply.
Check Out the Complete Details about the Training & Instructor profile below:
Scientists Who Will Train You: (Know more about them in the brochure above)
- Dr. Nilofer K Shaikh - Biotecnika Global Scientist with Expertise in AI, ML & NGS
- Mr. Prodyot Banerjee - Biotecnika CRO Scientist with Expertise in CADD, Bioinformatics, AI, ML & Genomics Expert
- Dr. Elamathi - Biotecnika CRO Scientist with Expertise in Bioinformatics, NGS, AI, ML & Coding
- Dr. Bhupender - Biotecnika Scientist With Expertise in Bioinformatics & AI ML
Project Topics Available under 3, 6 & 12 Months Training
Cancer & Oncology
| Project Topic | Duration |
|---|---|
| Drug-gene interaction prediction of ovarian cancer using Machine Learning | 6 months |
| Machine Learning based exploration of plant meroterpenoids for anti-cancer activity | 6 months |
| Machine learning classification of microarray gene expression data for cancer subtype identification | 6 months |
| Create a CNN to classify cancer types from histopathological images | 3 months |
| Potential angiogenesis drug targets in oral cancer using AI-ML | 6 months |
| Prediction of Cancer Drug Resistance - Apply supervised learning techniques to identify patterns in cancer cells that lead to drug resistance | 6 months |
| Drug Synergy Prediction for Combination Therapy in Cancer - Apply deep learning to predict effective drug combinations for cancer treatment. | 12 months |
Infectious Diseases & Antimicrobial Research
| Project Topic | Duration |
|---|---|
| Molecular docking, ADME prediction of phytochemicals from W. somnifera against 3CLpro of SARS CoV-2 | 6 months |
| Identification of phytochemicals with dual activity against DENV NS5 proteins and STAT2 host receptors through virtual screening | 6 months |
| Development of a simple AI-based model for predicting antibiotic resistance in bacteria | 3 months |
| AI-Powered De Novo Peptide Design for Antimicrobial Therapy | 6 months |
| AI-based Vaccine Design for HIV - Apply neural networks to predict epitopes for effective vaccine targets against HIV | 12 months |
| HIV Subtype Classification Using Genomic Data - Use unsupervised learning to classify HIV subtypes based on sequence variations | 3 months |
Neurological & Neurodegenerative Diseases
| Project Topic | Duration |
|---|---|
| Drug discovery through virtual screening using Bioinformatics database for Multiple Sclerosis | 6 months |
| Prediction of protein secondary structure using supervised learning (neuroscience focus) | 3 months |
| AI Model for Early Alzheimer's Detection from Blood-Based Transcriptome Data | 6 months |
Cardiovascular Disease
| Project Topic | Duration |
|---|---|
| Predictive Modeling of SNPs for Myocardial Infarction in SMuRFless Patients | 6 months |
Hair Health
| Project Topic | Duration |
|---|---|
| Predictive Modeling of Chemical Compounds for Hair Health: A Machine Learning Approach | 6 months |
Food Allergy
| Project Topic | Duration |
|---|---|
| Prediction of Allergenicity of Food Proteins Using ML Algorithms | 3 months |
Microbiome & Bacterial Genomics
| Project Topic | Duration |
|---|---|
| Clustering analysis of microbiome data to identify microbial communities using K means algorithm | 6 months |
| AI-Based Prediction of Essential Genes in Bacterial Genomes | 3 months |
General Drug Discovery & Therapeutics
| Project Topic | Duration |
|---|---|
| Potential Therapeutic drug target identification using virtual screening | 12 months |
| Natural Product Drug Discovery: Applying machine learning to discover novel drugs from natural products | 12 months |
| Machine Learning based Screening of Curcuma longa for Drug Discovery | 6 months |
| Identification of drug-target interactions using basic bioinformatics and regression model | 6 months |
| Integration of biological networks for drug repurposing using machine learning | 12 months |
| Predictive modelling of drug side effects using bioinformatics and ML | 3 months |
Genetics, Genomics & Disease Association
| Project Topic | Duration |
|---|---|
| Identification of disease associated SNP in genomic data | 3 months |
| Genetic Variance Analysis using AI/ML based tool and mutant protein modelling | 6 months |
| Exploring Disease-Associated Genes | 3 months |
| Deep Learning-Based Prediction of MicroRNA-Disease Associations | 6 months |
| AI-Based Detection of Pathogenic Mutations from Whole-Exome Sequencing Data | 3 months |
Gene Expression, Biomarkers & Pathway Analysis
| Project Topic | Duration |
|---|---|
| Analysis of single-cell RNA sequencing data using unsupervised learning method | 6 months |
| Novel Biomarker Discovery using Machine Learning | 6 months |
| Clustering analysis of gene expression data for identifying co-expression modules | 6 months |
| AI-Driven Pathway Enrichment Prediction Based on Differential Gene Expression | 3 months |
Protein Structure & Sequence Analysis
| Project Topic | Duration |
|---|---|
| Protein structure prediction by AI techniques | 3 months |
| Sequence alignment and homology modeling | 3 months |
| AI-Driven Prediction of Protein Stability Changes Upon Mutation (ΔΔG) | 3 months |
Comparative & Evolutionary Genomics
| Project Topic | Duration |
|---|---|
| Comparative genomics analysis using machine learning for evolutionary studies | 3 months |
Why Attend This Training?
Our training program is meticulously crafted to provide participants with a hands-on learning experience that goes beyond theoretical concepts. Through a combination of expert-led sessions, practical exercises, and real-world projects, attendees will gain the skills and knowledge necessary to excel in this dynamic field. Whether you're a student, researcher, or industry professional, this training offers a gateway to unlocking new opportunities and advancing your career in biology and bioinformatics.
-
Hands-On Experience: Gain practical experience through live projects and practical sessions, working with real-world datasets and tools.
-
Expert Guidance: Learn from experienced professionals and researchers who specialize in the application of AI and ML in Biology and Bioinformatics.
-
Career Enhancement: Acquire sought-after skills that are highly relevant in various industries, including pharmaceuticals, healthcare, biotechnology, and academia.
-
Networking Opportunities: Connect with peers, mentors, and industry experts, fostering valuable relationships that can aid in your professional growth.
Program Curriculum (30 Days): Unit 1: Introduction to AI and ML in Biology
- DAY-1: Overview of AI and ML in Biology
- DAY-2: Introduction to Integrated Development Environment
- DAY-3: Introduction to Python
- DAY-4: Introduction to R
- DAY-5: Data Representation and Visualization
- DAY-6: Importance of AI/ML in Biological Research
Unit 2: Machine Learning in Bioinformatics
- DAY-7: Overview of Supervised Learning Algorithms
- DAY-8: Classification and Regression Algorithms
- DAY-9: Clustering Algorithms
- DAY-10: Dimensionality Reduction Techniques
- DAY-11: Unsupervised Learning Applications
Unit 3: Deep Learning in Bioinformatics
- DAY-12: Introduction to Deep Learning
- DAY-13: Introduction to Neural Networks
- DAY-14: Convolutional Neural Networks
- DAY-15: Artificial Neural Networks
- DAY-16: Recurrent Neural Networks
Unit 4: Data Preprocessing in Bioinformatics
- DAY-17: Data Cleaning Techniques
- DAY-18: Data Normalization and Feature Selection
- DAY-19: Applications of AI/ML in Genomics
- DAY-20: Applications of AI/ML in Proteomics
- DAY-21: Applications of AI/ML in Drug Discovery
- DAY-22: Applications of AI/ML in Multi-omics
Practical Sessions:
- DAY-22 to 25: Hands-on Exercises using GitHub Repository and Relevant Tools and Datasets
- DAY-26: Doubt Solving Session
- DAY-27: Open Discussion
- DAY-28 to 30: Project Topic Discussion
Role of AI and ML in Biology, Bioinformatics & Computational Biology
Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing biology, bioinformatics, and computational biology:
-
Data Analysis: They decipher large biological datasets, revealing patterns and insights beyond human capacity.
-
Prediction: ML models forecast outcomes, aiding drug discovery and personalized medicine by tailoring treatments to individual genetic profiles.
-
Drug Development: Accelerating drug discovery, AI predicts compound efficacy and safety, streamlining development processes.
-
Personalized Medicine: ML tailors treatments to patients' unique genetic and clinical profiles, improving efficacy.
-
Image Analysis: AI automates biological image analysis, enhancing speed and accuracy in microscopy and medical imaging.
-
Network Analysis: Unveiling biological network structures, AI identifies key nodes and pathways, aiding understanding of complex systems.
-
Data Integration: ML integrates diverse biological data types, enriching insights and discoveries across research areas.
In essence, AI and ML empower researchers, accelerating progress and innovation in biological sciences, healthcare, and beyond.
Career Prospects
With the increasing reliance on data-driven approaches in biological research and industry, professionals skilled in AI and ML techniques are in high demand. Upon completing this training, you'll be equipped to pursue various career paths, including:
- Bioinformatics Scientist
- Computational Biologist
- Data Analyst/Scientist in Healthcare
- Pharmaceutical Researcher
- Academic Researcher in Life Sciences
Enroll Today and Be Part of the AI ML Revolution in Biology, Bioinformatics & computational biology
Disclaimer: We do not guarantee paper publication & job placement under this program. We will guide you throughout the training course to ensure you are ready enough to get a job & publish papers in well-reputed journals.