Welcome to

Bio-Medical Informatics and Genomics Research Group

The Bio-Medical Informatics and Genomics Research Group (BioMIRG) addresses complex biological questions by leveraging computational approaches and data analysis techniques to derive meaningful insights from vast biological datasets. BioMIRG mainly focuses on application of AI and Machine learning in wide area of genomics,proteomics,transcriptomics as well as medical and cancer genomics there by fostering the versatility of bioinformatics and computational biology in advancing our understanding of biological systems, improving healthcare, and driving innovation in various scientific disciplines .

In addition, the group concentrate on the development of computational methods to analyze genetic variations to predict individual responses to drugs and optimizing treatment strategies thereby enabling personalized medicine and targeted therapies.

People

Internal Faculty

Dr. Sujamol S

Research Interests: Bioinformatics,Computational Biology,Systems Biology, Deep learning, Soft Computing,Data Mining

Dr. Athira B

Research Interests: Health Informatics,NLP,Bioinformatics,Data Analytics

Dr. Manu Madhvan B

Research Interests: Bioinformatics,NLP,Big Data Analytics,Machine Learning

Dr. Jayakrushna Sahoo

Research Interests: Data Mining,Machine learning,Cryptography

Dr.Suchithra M S

Research Interests: Data mining,Machine learning,Deep Learning,Soft computing,AI in Agriculture

Dr. Lidiya Lilly Thampi

Research Interests: deep Learning,Underwater Imaging,Medical Image Processing

Dr.Priyadharshini S

Research Interests: Computer Vision,Deep Learning,Image Processing,Machine Learning,Multi-Objective based Optimization,Spatio-Temporal Video Summarization,Spherical Video Summarization

Dr.Kanchan Lata Kashyap

Research Interests: Medical Image Processing,Machine Learning,Natural Language processing

Dr. Sreeja M U

Research Interests: Computer Vision,Machine Learning,Deep learning,Explainable AI for healthcare

Dr. Dhakshayani J

Research Interests: Computer Vision,Machine Learning,Deep Learning,Image Processing,Precision Agriculture,High Throughput Phenotyping

Dr. Debarati Ganguly

Research Interests: Non-invasive antenna design for therapeutical modalities,Antennas for microwave ablation of solid tumors

Dr. Ravi Ajmeera

Research Interests: Deep Learning,IoT,Communications and Signal Processing,Optimization in Systems Engineering,Remote Sensing Applications

Dr. Binu M

Research Interests: Network Science,Fuzzy Graph Structures,Connectivity Analysis in Weighted Networks,Reliability Parameters,Decision Making Problems

Dr. Anu Maria Sebastian

Research Interests: Applied AI,Machine learning,Data imputation,Healthcare data analytics

Industry Experts

Dr. Sangeetha Saji

Genetic Technologist, Genetic Laboratory Hub, Cambridge University Hospitals,London

Research Interests: Cancer genomics, Bioinformatics, RNA sequencing

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Dr.Amrita Dhanya C S

AI/Data Manager, NHS North of England CSU (NECS)

Research Interests: Business Intelligence,Data Analytics,Data Architecture,Supply chain Management

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Mr.Unnikuttan V V

Head of Data, Diametos GmbH, Germany

Research Interests:Biomedical Engineering,Data Science,Signal Processing

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Mr.Prasanth Kattiparambil Kusuman

Biomedical Engineer, Senior Engineer,International Agencies , Bahrain

Research Interests:Biomedical Engineering

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Mr.Anoob C S

Electronic Test Engineer,CML MICROCIRCUITS, United Kingdom

Research Interests:Medical Electronics and Instrumentation,Signal Processing

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Research Areas Covered

Genomics

Genome sequencing and Gene Expression Analysis

Proteomics

Protein Structure Prediction

Docking

Molecular Docking

Biological Network Analysis

Network Modeling and community detection

Pharmacogenomics

Drug Discovery and Repurposing

Medical NLP

Natural Language Processing in Healthcare

Plant genome sequencing

Agrigenomics

Precision farming and Remote sensing

Precision Agriculture

Medical Image Processing and Analysis

Medical Imaging

Research Publications

  1. S, Sujamol., E R, V., & Krishnakumar, U. (2023). Improving miRNA Disease Association Prediction Accuracy Using Integrated Similarity Information and Deep Autoencoders. IEEE/ACM transactions on computational biology and bioinformatics, 20(2), 1125–1136. URL
  2. S.Sujamol, E.R.Vimina and U.Krishnakumar (2021) Improving Recurrence Prediction Accuracy of Ovarian Cancer Using Multi-phase Feature Selection Methodology, Applied Arti-ficial Intelligence,Taylor&Francis,35:3,206-226 URL
  3. Sujamol, S., Ashok, S., Krishnakumar, U., & Anoob, C. S. (2018, July). A genetically optimized method for weight updating in fuzzy cognitive maps. In 2018 international conference on emerging trends and innovations in engineering and technological research (ICETIETR) (pp. 1-5). IEEE. URL
  4. Sujamol, S., Ashok, S., & Kumar, U. K. (2017). Fuzzy based machine learning: a promising approach. CSI Commun. Knowl. Digest for IT Community, 41(8), 21-25. URL
  5. Sujamol, S., Ashok, S., & Krishnakumar, U. (2018). Study of fuzzy cognitive maps for modeling clinical support systems. International Journal of Pure and Applied Mathematics, 119(12), 15433-454. URL
  6. Athira, B., Josette Jones, Sumam Mary Idicula, Anand Kulanthaivel, and Enming Zhang. "Annotating and detecting topics in social media forum and modelling the annotation to derive directions-a case study." Journal of Big Data; Springer 2021 Jan 8(1): 1-23. SCI-10.8 URL
  7. Balakrishnan, Athira, Sumam Mary Idicula, and Josette Jones. "Deep learning based analysis of sen-timent dynamics in online cancer community forums: An experience." Health Informatics Journal 27.2 (2021, April): 14604582211007537. – SCI(IF-2.932). URL
  8. Athira, B., et al. "An answer recommendation framework for an online cancer community forum." Multimedia Tools and Applications (2023): 1-27.(SCI-2.5) URL
  9. Chinju John, Jayakrushna Sahoo, Manu Madhavan and Oommen K Mathew, Convolutional Neural Networks: A Promising Deep Learning Architecture for Biological Sequence Analysis, Current Bioinformatics (2023). URL
  10. Manu Madhavan, Gopakumar G, "DBNLDA: Deep Belief Network based representation learning for lncRNA-disease association prediction."Applied Intelligence (2021): 1-11.URL
  11. Manu Madhavan, Gopakumar G, 2021. Long Non-coding RNAs in Heart Failure: A Deep Belief Network based Cluster Analysis, Current Bioinformatics, 16-1, DOI:10.2174/1574893616666210528162945. URL
  12. Madhavan, M, Gopalakrishnan G. 2018. An Effective Sequence Structure Representation for Long Non-Coding RNA Identification and Cancer Association using Machine Learning Methods. ACM Applied Computing Review. 18(3):49-58. URL
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