Research Project in Autoimmune Genes in Head and Neck Cancer using ML & Network-Based Analysis

In order to strengthen your profile so to have better opportunities in your career, i. e, for jobs or for higher studies you need to have good publications under your belt. The only program since 2010 which is fulfilling this need is the Research Project Training Program of BDG LifeSciences which is of novel research projects on the latest technologies of Bioinformatics.

In this program, we implement the current research trend and apply unique ways of teaching plus practical application so to make you learn in the best possible way. As it is done online hence participants have the freedom of choosing the time of training sessions according to their choice and also save a huge amount of money in travel, accommodation, food, etc., As of now we have completed more than 70 research projects and all of them published at International level. This research project can be done as a Major and/or thesis project for the final year or if someone wants to strengthen their profile.

Applications are invited for 3 seats in our 86 novel research project entitled "Unveiling Autoimmune Genes and Regulatory Elements in Head and Neck Squamous Cell Carcinoma through Advanced Machine Learning and Network-Based Analysis"

FEE- In India Online: 79999 INR, Foreign Online: $1099 USD

LAST DATE OF REGISTRATION IS 31 JULY 2024 AND THE PROJECT STARTS IN AUGUST 2024

The objective of this research project is elucidation of robustautoimmune mRNAs, miRNAs, lncRNAs, TFs, and siRNAs involved in the mechanism of HNSC. The work will primarily involve retrieval of various microarray and RNA-Seq HNSC datasets followed by meta-analysis and differentially expressed genes (DEGs) screening between two age groups (i.e., Adult and Pediatric). Pearson/Spearman correlation values will be used for construction of co-expression network followed by overlapping community detection and assessment of network topological properties. Advanced machine learning technique such as adaptive lasso and elastic net regression will be used for immune cell classifier construction and identification of tumor-infiltrating genes. Multivariate and univariate cox proportional hazard will then be utilized for screening immune hub with high-risk score and effecting patients’ overall survival (OS) the most. Protein – Protein Interaction Network (PPIN) and enrichment analysis will lead us the way towards autoimmune genes. Composite FFL will lead to the identification of high degree miRNAs, lncRNAs, TFs, and siRNAs having an intricate relationship with autoimmune genes. Virtual screening of compound libraries will aid to find therapeutic lead for key autoimmune gene inhibition/targeting.

Objective 1: HNSC RNA-Seq retrieval and screening of differentially expressed genes: Primarily, TCGA, NCBI-GEO, ArrayExpress, and NCI Genomic Data Commons will be used for extracting HNSC RNASeq data. The data will then be categorized into Paediatric and adult samples for screening of DEGs. For performing meta-analysis, we would obtain at least 5-10 datasets in both the age group categories followed by pre-processing and batch correction. After obtaining the overlapping DEGs between both age groups we would be subjecting them to meta-analysis.

Objective 2: Gene co-expression network construction and significant module detection: The screened meta-DEGs between both the groups will be subjected to Gene Co-expression Network construction based on the Pearson/Spearman correlation coefficient. Then, highly connected hub communities/modules with overlapping genes will be identified.

Objective 3: Immune cell classifier using adaptive LASSO and prognostic signature model

- Immune cell classifier construction using adaptive LASSO: Elastic-net regression method which is a linear combination of L1/L2 regularization in the ridge regression and LASSO regression will be used for building an immune cell classifier. For constructing the model, total samples will be split into 70:30 ratio for training and test sets which were also stratified by clinical variates (i.e., TNM staging, gender, weight, and histopathological stage). We will place here the concept of adaptive LASSO technique where weights are assigned for regularizing different coefficients. Possible association between hub gene expression and abundance of major subtypes of tumor-infiltrating immune cells will be then done. Only the overlapping genes between our immune cell classifier and tumor-infiltrating genes will be retained.

-  Prognostic signature for survival prediction: Multivariate and univariate cox proportional hazard will be utilized for screening immune hub genes having significant relationship with patients’ OS followed by random survival forests-variable hunting algorithm application to filter HNSC prognostic genes.

Objective 4: PPIN construction and enrichment analysis

- PPIN construction and analysis: PPIN of immune DEGs with high-risk score will be constructed using protein databases. The protein interacting partners will be filtered based on highest score.

- Pathway and functional enrichment analysis: Pathway and GO term enrichment analysis will assist to filter DEGs occurring only in any autoimmune signalling pathway or term.

Objective 5: Key autoimmune DEG identification and therapeutic intervention

- Identification of driver miRNAs, TFs, lncRNAs, and siRNAs from composite FFL: Significant (based on p-value) human TFs, miRNAs, lncRNAs, and siRNAs interacting with our autoimmune DEGs will be fetched from highly validated databases. Considering the regulatory relationships between DEGs, TFs, miRNAs, lncRNAs, and siRNAs, a 5-node composite FFL will be constructed. Driver miRNAs, TFs, siRNAs, and lncRNAs will be fetched for our key autoimmune HNSC DEG using degree and clustering coefficient.

DURATION- 2-3 Months- training sessions will be conducted online on ZOOM/GOOGLE MEET/MICROSOFT TEAMS

Novel autoimmune genes along with other regulatory elements responsible for the pathogenesis of HNSC will be identified via adaptive lasso and overpaying module detection approach. The hub module will be subjected to immune cell classifier and high-risk score prognostic autoimmune genes identification. Lastly, docking and simulation will aid in selecting natural products against our autoimmune HNSC targets.

SYSTEM REQUIREMENT: System desktop or laptop with i5, i7 or i9 core processor with internet. Apple M processors will also work.

ELIGIBILITY: Anyone who is a UNDERGRADUATE/GRADUATE or HIGHER DEGREE is the minimum requirement to register for this project. This project is strictly not for HIGH SCHOOL (Class 9 & 10) / COLLEGE (Class 11 & 12) students. For them we have other projects which can be presented in Science Fair.

    Training in the technology.

    Opportunity to work on a novel research project.

    Practical application of training

    Software, which you can use for other projects.

    Tutorial and Papers of technology., so to make you understand every aspect of the technology.

    Chance of getting it published at an International Conference/Journal.

    Certificate of Training in Research Project.

    Recommendation letter for Job/PhD.

    Add the position of Research Project Trainee in your CV and LinkedIn profile. This will serve the purpose of work experience.

    We will connect you with people in our group who did projects with us which will help you in getting jobs as well as PhD positions around the world.

    Once you register and pay the fee we will send you a welcome email having details.

    Once registration is done it is NON-REFUNDABLE/NON TRANSFERABLE.

    While you will be working on the project and completing the tasks to monitor the progress of your work you have to send daily project reports to your guide.

    You will be directly connected to your guide as well as in a group where people who have completed their research project with us are present to solve your queries at the earliest.