Bioinformatics Training with Certification – ArrayGen BICP Program (NGS, Python, R)

BICP-6Month
BICP-12Month

Course Information
Course	Bioinformatics Industrial Certificate Program
Duration	One Month
Slots	Our working Time is 9:00 AM to 6:00 PM Indian Time Available slots - 9:00 AM to 11:00 AM / 11:00 AM to 1:00 PM / 2:00 PM to 4:00 PM / 4:00 PM to 6:00 PM For training slots after 6 PM or before 9 am as well as weekends training kindly mention during registration accordingly it will be scheduled.
Mode	Online 👉 For online training candidate have to install ZOOM (with remote control on candidate system which makes 100% interactive) 👉 Run time video recording candidate can make as well as pdf manual will be provided for future reference. 👉 All our training is 100% practical and 100% industrial and 100% interactive which provides same as offline learning. 👉 For doubt clear there will be extra support will be provided based on the requirement. 👉 Certificate will be provided 👉 Publication Support provided
Training Fees
Module-BICP -Twelve Month	📘 1st Month - Module-1 Advance Bioinformatics and Module-2 Microarray and Module-3 NGS - Any one application 📘 2nd Month - 15 Days Python and Biopython - 15 Days R Programming 📘 3rd to 4th Month - NGS pipeline development using python and R programming 📘 5th to 10th Months Project Work and publication of project work Under Our Guidance (all the project work will be provided in same NGS application selected or custom project as requested) 📘 11th to 12th Month Company Live Project Exposure to Become Completely Independent
Course Links	- NGS : https://localhost/arraygen/RNASeq_R.php#fifteen - Microarray : https://localhost/arraygen/microarray-data-analysis-training.php (candidate can select any one NGS application during the training and project work will be assigned for the same selected NGS application)
Sample projects
Instructor	Industry Experienced
Target Audiance	This course is designed for graduate students, postdoctoral researchers, and professionals working in the fields of conservation biology, evolutionary genomics, and population genetics or any life sciences who are interested in applying genomic tools to real-world conservation challenges.
Contact	Please write us at info@arraygen.com or call or whatsapp us on mobile +91-9673625446 if you need any clarification or for any custom training based on candidate reference paper or candidate own content/tools.

Classification of protein-protein association rates based on biophysical informatics
Spatial transcriptomics analysis using unsupervised convolutional neural network
Protein remote homology detection based on bidirectional long short-term memory
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.
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.
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.
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.
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.
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.
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.
Disease Biomarker Discovery:
- Biomarker Identification: Apply ML to identify potential biomarkers associated with diseases, facilitating early diagnosis and personalized treatment.
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.
Structural Bioinformatics:
- Drug Repurposing: Utilize AI to identify existing drugs that may be repurposed for new therapeutic purposes based on their structural properties.
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.
Pharmacogenomics:
- Personalized Drug Prescriptions: Use genetic information to predict how individuals will respond to specific drugs, optimizing medication regimens.

Pri. Mobile	::	+91-9673625446
E-Mail	::	info@arraygen.com

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Bioinformatics Industrial Certificate Program

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