Imperial College London

Dr Joram M. Posma PhD MSc B AS MRSC

Faculty of MedicineDepartment of Metabolism, Digestion and Reproduction

Senior Lecturer in Biomedical Informatics
 
 
 
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Contact

 

j.posma11 Website

 
 
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Location

 

E305Burlington DanesHammersmith Campus

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Summary

 

Software

  • Latent Variable Stochastic Ensemble of Trees (LAVASET) is freely available from GitHub
  • Natural language processing tools for omics data are available from the omicsNLP GitHub.
  • Covariate Adjusted PLS (CAPLS) v1.5 (June 2018) is freely available from BitBucket.
  • PanViz v1.4 is freely available from BioConductor.
  • SubseT Optimization by Reference Matching (STORM) v2.0 (September 2017) is freely available from BitBucket.
  • Resolution EnhanceD STORM (RED-STORM) is freely available from BitBucket.
  • MetaboNetworks v2.1 (September 2017) is freely available from the MATLAB File Exchange.
  • MetaboNetworks v2.2 (June 2018) is available from BitBucket.
  • MetaboPathways v1.0 (June 2018) is freely available from BitBucket.

Guest Lectures

Host genomic influence on the gut microbial metabolite-blood pressure relationship, International Society of Hypertension conference, Kyoto, Japan, 2022

URINARY METABOLIC PHENOTYPE OF BLOOD PRESSURE, ESH ISH ON AIR Joint Meeting, Glasgow (online), 2021

An issue of life and breadth: how a urine test can help a nation on aslender bender, Health Data Research UK, London, 2020

Monitoring diet more accurately to aid healthy living (in a post-COVID society), Health Data Research UK, London, 2020

Integrating omics to understand blood pressure variation, MRC London Institute of Medical Sciences, London, United Kingdom, 2020

A Systems Biological Approach to the Interpretation of Multi-omics Data, Stellenbosch University, Stellenbosch, South Africa, 2019

Systemic nutriome-metabolome interactions to understand pathways to disease risk in humans, Murdoch University, Perth, Australia, 2019

Research Student Supervision

Alloula,A, Phenotypic Classification using Deep Learning in Metabolomics

Anholt,L, Leveraging geometric learning for community detection in biological networks that integrate polymorphic and metabolic data

Anholt,L, Expanding the systems view of biology and disease by integrating genome-wide association catalog data in the visualization of metabolic reaction networks

Bai,P, Topological machine learning to analyse metabolomic networks for idiopathic pulmonary fibrosis

Chaitrakulthong,N, Expanding the systems view of colorectal cancer risk by integrating genome-wide association catalog data in the visualization of metabolic reaction networks with MetaboNetworks

Cheng,Z, A framework for spatial metabolomics data analysis of fatty liver disease and liver fibrosis

Faghih Mirzaei,N, What-the-bug?! Named entity recognition of bacteria and microbiota in biomedical text using recurrent neural networks

Gao,M, Data visualization and analysis of intravital microscopy multi-layered video (4D) data to identify how splenic neutrophils are controlled in their activation, location and migration by other splenic players

Hu,Y, Natural language processing of biomedical literature: automated mining of metabolome-wide association studies

Hydyrova,L, UROP: Creation of a Multi-Task Corpus for Metabolite, Enzyme and Pathway Named Entity Recognition

Ibrahim,M, Natural language processing of biomedical literature: automated mining of genome-wide association studies

Jarosinska,O, Multi population assessment of urinary metabolome stability over time

Jin,W, Auto-CORPus: Automated Abbreviation and Definition Detection of Biomedical Literature

Kasapi,M, Integration of faecal metabolic phenotypes and gut microbiota in subtypes of irritable bowel syndrome

Krauss Souper,C, Harmonisation of UKBB data for the prediction of cardiomyopathy through machine learning

Li,ZJ, “A picture is worth a thousand words”: translating images found in biomedical literature to text

Liu,W, Can I Have Your (Un)Divided Attention? Single- versus multitasking in biomedical natural language processing

Mailijia,J, Visualization, Data Mining and Automated Molecular Identification in Spatial Metabolomics of Chronic Liver Disease

Makraduli,F, Find The Assay: named entity recognition of techniques used for genome-wide and metabolome-wide association study data generation

McQuibban,NAR, GWAS Metadata Extraction: GWAS MNER

Money-Kyrle,S, Scaling As A Hyperparameter

Ni,Y, Understanding the impact of two extreme diets on gut microbiota and microbial metabolites variability in people at risk of cardiovascular disease

Patel,D, UROP: Integrating Natural Language Processing into Bioinformatics Analysis of Idiopathic Pulmonary Fibrosis Microbiome Data

Popovici,CM, Automated Mining of Preprint and Peer-reviewed Literature for Cohort Characteristics

Scotcher,J, Understanding the Role of Host-Microbiome Interactions in the Pathophysiology of Irritable Bowel Syndrome and Its Subtypes

Son,W, NLP in Radiology Report: Auto-Label Generation for CXR Image Classification

Stotzem,N, AstraZeneca Sponsored Project: Remodelling of immunometabolism in chronic liver disease using novel data integration and visualisation tools

Sun,A, Natural language processing of biomedical literature to support automated mining of genome-wide association studies

Talay,A, Natural language processing of biomedical literature to extract genome-wide association study data

Tambini,DR, Expanding the configuration of Auto-CORPus with Scientific Journals for Broader Data Coverage in Natural Language Processing Pipelines

Truong,A, Data visualization and analysis of intravital microscopy multi-layered video data using statistical machine learning

Tsagkarakis,N, Natural Language Processing to Standardize Biomedical Scientific Literature

Wang,M, Named Entity Recognition for enzymes in biomedical text

Xu,K, UROP: Natural language processing of electronic health records to augment deep learning on lung radiology imaging

Xu,K, UROP: incorporating latent variables in ensemble methods

Yassin,SM, Class-based directional feature importance for tree-based classifiers

Yeung,C, MetaboListem and TABoLiSTM: Two Deep Learning Algorithms for Metabolite Named Entity Recognition

Yoon,G, LABP: Integrating bioinformatics and literature knowledge into models to predict 1-year mortality in Idiopathic Pulmonary Fibrosis patients using baseline genomics data