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In the high-throughput screening processes used for chemical and genetic screens, robotized microscopes will collect a massive quantity of images of biological samples through the analysis of thousands of wells, each containing tens to several hundreds of cells (>100,000 images per day).
Efficient image acquisition systems (machinery for automated image acquisition and large storage devices) are now available to allow practical use of these images. However, human analysis is impractically slow, thus introducing the need of machine vision algorithms for the full automation of the process.
Pattern recognition and machine vision experts, who tailor general computer vision techniques to the specific needs of biological imaging, can develop these algorithms. Critical to the successful development of machine vision algorithms is the tight collaboration between biologists, engineers and computer scientists.
We plan to establish a tight collaboration between the “ISI-GenOmics - Center of Genomic Science of IIT@SEMM” Screening Unit and the IIT computational scientists for the development of machine vision algorithms. Biologists will provide datasets of biological images that represent actual biological questions and assess the performance of elaborated computational methods. |
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The mere quantity of epigenetic data arising from genome and epigenome projects will pose a major bioinformatics challenge. According to raw estimations, the total amount of DNA sequence currently contained in the GenBank could triplicate in just one year.
In addition to developing more efficient methods for data processing and storage, it will be also necessary to develop advanced computational methods that help bench researchers to interpret genome and epigenome datasets, and clinical researchers to identify molecular markers of diseases such as cancer.
We will launch a Computational Genomic and Epigenomic Program aimed at developing new analytical tools and data management systems, machine learning techniques and prediction/analysis algorithms for the acquisition, storage, processing, and analysis of high-throughput genome and epigenome mapping data.
Particular emphasis will be given to the development of computational tools for the integration of diverse high-throughput experimental data (genome sequence, chromatin structure, transcription factor-DNA binding, gene expression, proteomics, chemogenomics). Indeed, data integration is a crucial step for reproducing and predicting interactions between biological systems and the surrounding environment. It requires the creation of computational tools including graphic-based methods for reproducing the complex network of both endogenous and exogenous molecules and reactions and their interference with biological systems.
This Section will support the activities of both the Screening Unit and the Genomic Unit, as well as carry out specific research activities. |
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Computational