BIOXPLOR is an augmented intelligence platform for data-driven hypotheses generation & novel predictions in biomarker & drug discovery. The in silico discovery platform is built on the intersection of big data, artificial intelligence and high performance computing, and works by enabling accelerated data interpretations from unstructured literature & structured multi-omics data. In house & external projects are focused on AI-assisted biomarker discovery, molecular target identification and drug repurposing, presently in cancer and rare disease.
Text & Data Mining
Test Hypotheses Generation
Multi-omics Analysis of Healthy versus Disease Datasets
Interpretations for Novel Insights
Drug & Target Prediction Models
In house research programs & collaborations are focused on AI-assisted biomarker discovery, molecular target identification and drug repurposing, presently in cancer and rare disease.
Semantic Literature & Image Search
OMICS Cloud Computing
LIMS & Procurement Integration
25 Million Abstracts
5 Million Machine readable figure images
1 Million Experiment Tools, Assay & Models
250 Thousand Clinical Trials
125 Million Patents
1 Million Omics Datasets
250 Data repositories
Natural Language Processing
Text & Data Mining
~15 million processed figure images. Machine readable text within figure images. AI-classification of image categories. Image similarity matching.
In silico hypotheses generation. Prediction of novel molecular targets & drug response. Natural Language & Image Processing.
Transcriptomics proteomics, metabolomics. Healthy versus Disease, and Treated versus Untreated datasets.
Gene, protein, drug, disease, pathway, adverse event, drug labels, biological processes, cellular components, anatomy
High Performance Computing
Secure Data Management
Materials & Methods
Partnerships & Collaborations
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Metabolomics & Cheminformatics
Digital Marketing & Design
Genomics & Transcriptomics
Background in emerging biotechnology & bio-medical research with over a decade of commercial experience in the life science research market. Previously, associate director at Merck KgaA leading the emerging biotechnology initiative in Europe. Mark co-founded Curogenix, a European commercial agency for in vitro & in vivo contract research services, and prior to that worked at Sigma-Aldrich as a field applications specialist for emerging technologies.
Pavel has a background in computer science, software engineering & applied bioinformatics. He has particular expertise in biological patterns, data classification and recognition, machine learning approaches using GPGPU based predictive modeling, code conversion & optimization, general-purpose computations on graphical processors & java technologies as well as R, C/C++, java, verilog, python, bash (linux shell), visual basic, perl, and tcl.
Background in economics & finance from Kemmy business school at University of Limerick. Anna has worked in the Life Science, Food & Banking industries, & brings over 10 years expertise in project management , operations & finance to the team. She has previously founded a company in the food industry, after working in RBS global markets in the US. Anna has also won a management award while at Aramark.
Michael has a PhD in analytical chemistry & is a lecturer in biostatistics. He has a background in cheminformatics and data science with 10 years of experience in developing machine learning, pattern recognition and biomarker discovery. His specialist interest is in developing tools to interrogate metabolomics datasets leading to the non-invasive diagnosis of cancers and diseases. Michael is skilled in statistical programming using R & Matlab.
Background in digital marketing with a proven track record in digital design, search engine optimisation, social media & automated marketing. Colum also has graphic design & animation experience for generating online media content. He is currently interested in developing skills in print design, including infographics & posters. He has several certifications in design, digital marketing & animation.
Niranjan has a background in molecular biology & applied bioinformatics. He has expertise in handling NGS HiSeq data, quality control and filtering of NGS data: de novo or reference based using assemblers, reference based genome alignment, detection of genomic variation, RNA-seq differential expression analysis, and identification of sequence binding motifs using ChIP-seq.
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Investment Support & Program Finalists
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