BICP Program (12 Months)

Bioinformatics Industrial Certificate Program (Online)

Accelerating an automation in bioinformatics to reduce biological problem that improves Human Value and Accessible to Everyone! BICP is used mainly for life scientist and computer scientist to increase their serendipity to understand multi-disciplinary to make automate process by using bioinformatics approach.

Duration: 12 Months

• 2 Months to become an expert in Genomics Bioinformatics and Programming
• 10 Months to become an expert to run Live NGS Genomics Project

• Exposure with industry based Bioinformatics training in Next Generation Sequencing and Microarray
• Deep Understanding Programming Language for Biological Researcher/Computer Scientist.(Python and R programming)

• Work on NGS pipeline Development using python and R programing so that user will get more confidence with the first two months learning

• Get more time to discuss to resolve your query for data analysis problem.
• The live competitive project can handle and get the experience in Next Generation Sequencing.
• Became on independent to write NGS Pipeline
• Get closer experience with industrial experience.
• Get equip with 100% skills development in Bioinformatics
• Get maximum support for publication

Mode of BICP Program (Online)

For online program you need to have good internet speed in your computer and need to install Skype, AnyDesk (https://anydesk.com/download) which helps you to connect your computer to our computer for one to one interaction . Moreover, candidates will get full time support during BICP program.

Advantages of BICP Program

• This BICP will definitely help you to upgrade Bioinformatics Skills to compete market.
• This will open the door to access the chance internationally to get the job/Phd positions/Research positions.
• This would achieve 100% chance for Non-Bioinformaticians expose their real-time experience in Bioinformatics and increase their potency.

Bioinformatics sample project titles:

1. Classification of protein-protein association rates based on biophysical informatics
2. Spatial transcriptomics analysis using unsupervised convolutional neural network
3. Protein remote homology detection based on bidirectional long short-term memory
4. Early Cancer Detection:
   - Image-Based Detection: Develop ML models to detect early signs of cancer in medical images, such as mammograms for breast cancer or CT scans for lung cancer.
   - Liquid Biopsy Analysis: Use AI to analyze liquid biopsy data, including circulating tumor DNA (ctDNA) and circulating tumor cells (CTCs), for early cancer detection.
5. Genomic Sequence Analysis:
   - Genome Assembly: Develop AIML algorithms to assist in assembling short DNA sequences into complete genomes.
   - Gene Prediction: Use ML to predict the locations of genes within a genomic sequence.
6. Sequence Alignment and Comparison:
   - Multiple Sequence Alignment: Develop algorithms that align multiple sequences, aiding in the identification of conserved regions and functional elements.
   - Phylogenetic Tree Construction: Use ML to build phylogenetic trees based on sequence data to study evolutionary relationships.
7. Variant Calling and Genotype Prediction:
   - SNP Detection: Employ AI to identify Single Nucleotide Polymorphisms (SNPs) and other genetic variations from sequencing data.
   - Genotype-Phenotype Associations: Investigate relationships between genotypes and phenotypes using ML models.
8. Protein Structure and Function Prediction:
   - Protein Structure Prediction: Apply AI methods like deep learning to predict protein structures, which can be valuable for drug discovery.
   - Functional Annotation: Develop ML models to predict protein functions based on sequences, structures, and other features.
9. Metagenomics and Microbiome Analysis:
   - Taxonomic Classification: Use AIML to classify and identify microorganisms in metagenomic samples.
   - Functional Profiling: Predict the functional potential of microbial communities based on metagenomic data.
10. Drug-Target Interaction Prediction:
    - Binding Affinity Prediction: Develop ML models to predict the binding affinity between drugs and target proteins, aiding in drug discovery.
    - Side Effect Prediction: Predict potential side effects and off-target interactions of drugs using AI.
11. Disease Biomarker Discovery:
    - Biomarker Identification: Apply ML to identify potential biomarkers associated with diseases, facilitating early diagnosis and personalized treatment.
12. Biological Network Analysis:
    - Protein-Protein Interaction Prediction: Predict protein-protein interactions using AIML methods to understand cellular processes.
    - Pathway Analysis: Analyze biological pathways and their perturbations using network-based approaches.
13. Structural Bioinformatics:
    - Drug Repurposing: Utilize AI to identify existing drugs that may be repurposed for new therapeutic purposes based on their structural properties.
14. Functional Genomics and Expression Analysis:
    - Gene Expression Clustering: Use ML to cluster genes with similar expression patterns in large-scale gene expression datasets.
    - Differential Expression Analysis: Identify genes that are differentially expressed under different conditions.
15. Pharmacogenomics:
    - Personalized Drug Prescriptions: Use genetic information to predict how individuals will respond to specific drugs, optimizing medication regimens.

A student is also welcome to propose his or her own project designed on the following specialized topics:

• Identification of differentially expressed genes from RNA-seq data followed by codon usage bias analysis
• Novel miRNA prediction based on NGS data, EST and genomic sequences
• Gene expression analysis based on single cell RNA-seq data (scRNA-seq)
• Identification of over-represented and under-represented regulatory motifs
• Detection of alternatively spliced transcript variants playing role in disease progression
• Metagenomic analysis of fungi, bacteria and Bacteriophages
• CRISPR-Cas systems for editing, regulating and targeting genomes
• Vaccine design based on epitope/peptide
• Development of software tools for understanding biological data
• De novo design of inhibitors of protein-protein interactions
• Algorithm development (perl/java/python)
• Any custom topics as per candidate interest

Job Placement:

• 100% placement at ArrayGen as per company terms & condications.
• Company Experience Letter,Certificate & recommendation letter will be Provided after completion of BICP Program.
• Placement will be based on technical interview.
• All the trainings are based on industrial requirements and 100% skill development.
• After the BICP program all the student will be self suffcient to carry out independent execution which assures 100 placement to any company, or JRF/SRF/PhD.

Features of 12-Months BICP Program

• Recommendation letter will be Provided after completion of BICP Program.
• Unlimited Interview Calls.
• 100% Job Placement only with 12 months BICP completion.
• Hands on practical & Get Training from industrial experts with 11+ years of experience.
• Total industrial knowledge & One to one interactive.
• Research paper publication support.