Multi-omics data analysis
Biomarker detection & validation
The identification of biomarkers for early disease screening and therapy monitoring can be enhanced through the use of predictive algorithms using single or multi-omics data. Our extensive experience in the development of artificial intelligence based algorithms supports the de novo discovery and validation of biomarkers using unique proprietary clinical diagnostic algorithms. Next to making use of single or multi-omics approaches, we are also proficient in the computational integration of additional clinical and pathological data using multi-modality methods.
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Diagnostics
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Drug development
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Cell Biology
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Proteomics
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ML/AI
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Algorithm development
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Workflow development
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Data integration
Drug candidate discovery
Protein alteration is prevalent in disease therefore they may be more or less active, have an altered function. Small molecule drugs may exist that modify a protein’s function by activating, inhibiting or altering the protein. We generate structural models of the disease altered proteins of interest, implementing a high throughput in silico workflow to screen multiple databases and score relevance of small molecules as clinical drug candidates.
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Drug development
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Transcriptomics
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Proteomics
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Algorithm development
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Data mining
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Workflow development
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Data integration
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Biostatistics
Epigenome analysis
Both global and local DNA methylation changes have been associated to disease, and in particular to cancer. While methylation is the most well studied for of epigenetic modification, other non-methylation epigenetic mechanisms include histone modifications, micro-RNA interactions and chromatin remodelling complexes. We build robust computational pipelines for full epigenetic analysis to detect differences in methylation and non-methylation mechanisms for selected treatments and link these results to gene expression.
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Diagnostics
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Therapeutics
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Genomics
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Epigenetics
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Transcriptomics
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ML/AI
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Algorithm development
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Workflow development
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Data integration
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Biostatistics
Microbiome analysis
We perform multi-omics mapping of full microbial communities to determine species composition and examine the causal role of a specific microbiome in selected disease states. Our workflows are used for human, animal, plant and aquatic systems. We implement state-of-the-art statistics to calculate causal statistics by comparative weighting of genetic, microbial and external factors to predict disease development, progression and resistance. By using a combination of a biostatistical frameworks and artificial intelligence tools we examine modifiable risk factors to establish the most viable drug targets and biomarkers in microbiome-associated illnesses as well as develop bioremediation approaches.
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Diagnostics
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Cell Biology
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Microbiology
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Genomics
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Transcriptomics
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Proteomics
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ML/AI
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Algorithm development
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Data mining
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Data integration
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Visualisation
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Biostatistics
Protein profiling & proteomics
Our proteomic data analysis workflow uses mass-spectrometry data in order to identify and quantify peptides and proteins. Our pipelines can analyse peptides and their post-translational modification as well as full proteomes. Integration of transcriptomic and proteomic data highlights correlation of specific proteins and transcripts. Using proteogenomics analysis (integration of genomic and transcriptomic data) we can build a custom protein databases.
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Drug development
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Synthetic biology
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Proteomics
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Data mining
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Data integration
Structural variant calling
Structural variants, for example fusion genes formed as a result of translocation, interstitial deletion, or chromosomal inversion have been linked to cancer development. Using targeted RNA-sequencing data our pipeline detects clinical fusion genes and by further implementing predictive algorithms we can predict cancer prognosis, patient survival and treatment responses. Additionally we can integrate publicly available drug data to screen drug candidates for potential therapeutic treatments for fusion gene inhibition.
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Therapeutics
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Genomics
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ML/AI
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Algorithm development
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Data mining
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Data integration
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Biostatistics
Transcriptomic data analysis
Exploring RNA-sequence data using tailor-made pipelines that are reproducible and robust to accurately identify transcriptional differences. Transcriptomes are investigated using a combination of in-house developed and publicly available tools to effectively assemble and align RNA-sequence reads. This can be done for samples with or without a reference genome. Differential expression is determined at a gene or transcript level and potential pathways are highlighted through gene enrichment analysis. Our pipeline, toRNAdo, is a Nextflow based pipeline that allows fast scaling processing on different cloud technologies (e.g. Google cloud, Amazon AWS). The fully automated toRNAdo provides a final output as well as a QC report based on the MultiQC tool. Due to the modular nature of the MultiQC tool, modifications can be made for seamless adaption to a range of biological data, such as variant calling data.
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Genomics
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ML/AI
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Workflow development
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Visualisation
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Biostatistics
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Reporting
Biostatistical analysis
Biological data is highly complex with multiple variables contributing to observed trends. Analysis and interpretation of this type of data requires the use of appropriate tools and methods in order to harness the most out of the data, and highlight significant correlations and causal links. We develop and apply a range of statistical methods tailored to each specific problem setting, that can be used for both clinical and non-clinical datasets. Our models take into account experimental/clinical design as well as all relevant biological data. We have specific expertise in combining multi-omics data with other data sources that can be seamlessly integrated into our AI-based algorithms.
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Genomics
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Epigenetics
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Transcriptomics
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Proteomics
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Algorithm development
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Data mining
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Data integration
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Visualisation
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Biostatistics
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Reporting
Single cell sequencing
Single cell sequencing technologies allow to study complex biological systems at an unprecedented resolution and scale. We provide an end-to-end service, from assisting with experimental design to performing advanced bioinformatic data analysis. Our pipeline supports the analysis of multi(modal) -omics single cell data (transcriptomics, immune profiling, proteomics, …) from different library preparation platforms (10x Genomics, Smart-Seq2, …). The modular nature of our pipeline platform allows to easily adapt the analysis workflow to each unique research setting. We focus on a tailored analysis, closely collaborating with our client to incorporate relevant biology. Moreover, we provide the means for our clients to interactively visualize and investigate results through intuitive interfaces.
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Genomics
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ML / AI
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Workflow development
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Visualisation
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Biostatistics
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Reporting
AlphaFold2: Protein structure prediction
BioLizard now provides easy access to AlphaFold2 through several routes using our computational resources. We can provide a complete protein structure prediction workflow to run on your proteins of interest. We help you to make the most out of the new possibilities that AlphaFold2 provides and integrate it with our other structural bioinformatics services like protein engineering, small molecule protein docking, MD simulations etc. For additional information about AlphaFold, we recommend the following talk by Prof. Dr. Andrei Lupas of the Max-Planck Institute in Tübingen The revolution in protein structure prediction
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Synthetic biology
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Proteomics
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Data mining
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Workflow development
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Data integration
Algorithm development
Biomarker detection & validation
The identification of biomarkers for early disease screening and therapy monitoring can be enhanced through the use of predictive algorithms using single or multi-omics data. Our extensive experience in the development of artificial intelligence based algorithms supports the de novo discovery and validation of biomarkers using unique proprietary clinical diagnostic algorithms. Next to making use of single or multi-omics approaches, we are also proficient in the computational integration of additional clinical and pathological data using multi-modality methods.
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Diagnostics
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Drug development
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Cell Biology
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Proteomics
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ML/AI
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Algorithm development
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Workflow development
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Data integration
Clinical data trials
Repurposing historical data and existing real-world data to build effective models able to predict clinical outcomes. We use federated learning techniques to develop artificial intelligence based trial design, improve patient enrolment, predict retention and drive artificial intelligence enabled clinical trial analytics. We can also use real-world evidence to build synthetic control arms for clinical trials.
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Diagnostics
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ML/AI
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Data mining
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Workflow development
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Data integration
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Visualisation
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Reporting
Epitope prediction
Optimise antigen prediction platforms in the area of specific oncology indications by developing artificial intelligence based bioinformatics algorithms to predict binding sites and binding efficiency. On top of this, a reporting element is incorporated into the platform developed presenting the algorithm output in a clear and concise report interpretable by medical personnel.
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Diagnostics
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Therapeutics
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Drug development
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Cell Biology
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Immunology
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Genomics
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Epigenetics
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ML/AI
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Algorithm development
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Data mining
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Workflow development
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Data integration
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Biostatistics
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Reporting
Image detection, recognition & classification
Using a combination of machine learning, deep learning and imaging data informatics we can enhance image processing and accuracy for a complete range of image driven solutions. Integrating multi-omics and histological data with imaging data we can predict tumour margins, patient prognosis, most minimally invasive treatments and therapeutic responses on a case by case basis. Artificial intelligence based algorithms can be integrated into medical devices providing real-time data analysis and an artificial intelligence aided interpretation system.
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Diagnostics
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Cell Biology
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Image analysis
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ML/AI
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Data mining
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Workflow development
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Data integration
Multimodal learning
We use multiple omics data sources to provide a holistic approach to data learning by developing learning models that effectively integrate different data features simultaneously. By doing so, we can discover trends hidden in multiple data sources that only become evident when they are included together in the learning process. Switching to multimodal predictions also enables more robust predictions than a unimodal learning system.
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Genomics
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Epigenetics
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Transcriptomics
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Proteomics
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Image analysis
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ML/AI
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Algorithm development
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Data mining
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Data integration
Personalised medicine
Personalised medicine or precision medicine makes use of an individual’s clinical, genetic, genomic, and environmental information. Using a combination of data types (multimodal testing), we develop and optimise robust models calculating patient risk score for specific diseases (eg. cancer), treatment dosing and responses, survivability as well as identifying predictive biomarkers for disease.
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Diagnostics
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Therapeutics
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Immunology
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Genomics
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Epigenetics
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Transcriptomics
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ML/AI
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Algorithm development
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Data integration
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Biostatistics
Data mining & integration
Federated learning
Balancing the predictive performance of an algorithm by feeding it more data with the concerns for data privacy is an important issue. Federated Learning solves this by bringing the algorithm itself to the data. The data never leaves the end user at all time, on-device training is performed and only the updates to the algorithm are reported back to the central server.
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ML/AI
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Algorithm development
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Workflow development
Image detection, recognition & classification
Using a combination of machine learning, deep learning and imaging data informatics we can enhance image processing and accuracy for a complete range of image driven solutions. Integrating multi-omics and histological data with imaging data we can predict tumour margins, patient prognosis, most minimally invasive treatments and therapeutic responses on a case by case basis. Artificial intelligence based algorithms can be integrated into medical devices providing real-time data analysis and an artificial intelligence aided interpretation system.
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Diagnostics
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Cell Biology
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Image analysis
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ML/AI
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Data mining
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Workflow development
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Data integration
Multimodal learning
We use multiple omics data sources to provide a holistic approach to data learning by developing learning models that effectively integrate different data features simultaneously. By doing so, we can discover trends hidden in multiple data sources that only become evident when they are included together in the learning process. Switching to multimodal predictions also enables more robust predictions than a unimodal learning system.
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Genomics
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Epigenetics
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Transcriptomics
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Proteomics
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Image analysis
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ML/AI
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Algorithm development
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Data mining
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Data integration
Personalised medicine
Personalised medicine or precision medicine makes use of an individual’s clinical, genetic, genomic, and environmental information. Using a combination of data types (multimodal testing), we develop and optimise robust models calculating patient risk score for specific diseases (eg. cancer), treatment dosing and responses, survivability as well as identifying predictive biomarkers for disease.
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Diagnostics
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Therapeutics
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Immunology
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Genomics
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Epigenetics
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Transcriptomics
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ML/AI
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Algorithm development
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Data integration
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Biostatistics
Clinical data integration and management
Capturing, integrating and storing data from clinical trials, patient reported outcomes and wearables into a database accessible through an easy to use platform. By combining artificial intelligence algorithms and wearable technology we develop easy to use apps to automate data capture and continuously monitor and manage patients.
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Diagnostics
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Therapeutics
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Drug development
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Cell Biology
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Immunology
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Genomics
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Epigenetics
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Transcriptomics
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ML/AI
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Algorithm development
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Workflow development
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Data integration
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Visualisation
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Biostatistics
Pipeline & app development
Clinical data trials
Repurposing historical data and existing real-world data to build effective models able to predict clinical outcomes. We use federated learning techniques to develop artificial intelligence based trial design, improve patient enrolment, predict retention and drive artificial intelligence enabled clinical trial analytics. We can also use real-world evidence to build synthetic control arms for clinical trials.
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Diagnostics
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ML/AI
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Data mining
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Workflow development
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Data integration
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Visualisation
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Reporting
Epitope prediction
Optimise antigen prediction platforms in the area of specific oncology indications by developing artificial intelligence based bioinformatics algorithms to predict binding sites and binding efficiency. On top of this, a reporting element is incorporated into the platform developed presenting the algorithm output in a clear and concise report interpretable by medical personnel.
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Diagnostics
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Therapeutics
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Drug development
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Cell Biology
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Immunology
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Genomics
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Epigenetics
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ML/AI
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Algorithm development
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Data mining
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Workflow development
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Data integration
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Biostatistics
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Reporting
Federated learning
Balancing the predictive performance of an algorithm by feeding it more data with the concerns for data privacy is an important issue. Federated Learning solves this by bringing the algorithm itself to the data. The data never leaves the end user at all time, on-device training is performed and only the updates to the algorithm are reported back to the central server.
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ML/AI
-
Algorithm development
-
Workflow development
Personalised medicine
Personalised medicine or precision medicine makes use of an individual’s clinical, genetic, genomic, and environmental information. Using a combination of data types (multimodal testing), we develop and optimise robust models calculating patient risk score for specific diseases (eg. cancer), treatment dosing and responses, survivability as well as identifying predictive biomarkers for disease.
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Diagnostics
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Therapeutics
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Immunology
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Genomics
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Epigenetics
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Transcriptomics
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ML/AI
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Algorithm development
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Data integration
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Biostatistics
Clinical data integration and management
Capturing, integrating and storing data from clinical trials, patient reported outcomes and wearables into a database accessible through an easy to use platform. By combining artificial intelligence algorithms and wearable technology we develop easy to use apps to automate data capture and continuously monitor and manage patients.
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Diagnostics
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Therapeutics
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Drug development
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Cell Biology
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Immunology
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Genomics
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Epigenetics
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Transcriptomics
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ML/AI
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Algorithm development
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Workflow development
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Data integration
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Visualisation
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Biostatistics
Computational pipeline integration
Our experts facilitate the development, deployment and follow up of complete bioinformatics pipeline frameworks integrated into specific platforms or devices. These can be supported by web-based applications that can not only trigger the execution of a pipeline, but also include a reporting component that renders the output in a fully customisable format that can be interpreted by non-technical users.
Find out more by reading our client case.
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Workflow development
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Data integration
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Visualisation
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Biostatistics
