Sagentia Project Proposals

Sagentia is a global science, product and technology development company. Our mission is to help companies maximise the value of their investments in R&D. We partner with clients in the consumer, industrial, medical and oil & gas sectors to help them understand the technology and market landscape, decide their future strategy, solve the complex science and technology challenges and deliver commercially successful products.

 Sagentia employs over 150 scientists, engineers and market experts and is a Science Group company. Science Group provides independent advisory and leading-edge product development services focused on science and technology initiatives. It has 12 European and North American offices, two UK-based dedicated R&D innovation centres and more than 400 employees. Other Science Group companies include OTM Consulting, Oakland Innovation, Leatherhead Food Research and TSG Consulting.

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Predicting the cell types of single-cell RNA-Seq samples
Single-cell RNA-Seq makes it possible to characterize the transcriptomes of cell types and identify their transcriptional signatures via differential analysis. The challenge is to create a machine learning program/pipeline for identifying cell types from a dataset of single-cell RNA samples. You can use the method described here:


Predicting gene expression from histone modification signals
Post-translational modifications to histone proteins can impact gene expression in different ways. The challenge is to predict (with machine learning) the level of gene expression, by analysing histone modification signals. Read the detailed description of the problem and data here:


Build a predictive model that differentiates between true and false donor sites In human
DNA, most introns start with dinucleotide GT, called the donor side of the intron. However, a gene contains many more GT dinucleotides that are not donor sites. The challenge is to build a machine learning program/pipeline that can tell apart true and false donor sites. Read the detailed description of the problem and data here:


Signatures of mutational processes in human cancer data
Somatic mutations are present in all cells of the human body and occur throughout life. Different mutational processes generate unique combinations of mutation types, termed Mutational Signatures. The challenge is to use machine learning to analyse cancer datasets and identify specific signatures. You can read more here:


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