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We included a scPoli model with standard OHE vectors to represent batch, and a scPoli model trained without prototype loss. We found the prototype loss to be the driver of the improvement in biological how to hire a software developer<\/a> conservation (Fig. 2b). Can theory-driven machine learning approaches uncover meaningful and compact representations for complex inter-connected processes, and, subsequently, enable the cost-effective exploration of vast combinatorial spaces? While this is already pretty common in the design of bio-molecules with target properties in drug development, there many other applications in biology and biomedicine that could benefit from these technologies. Our MMSF approach contains several distinguishing and original features. It is based on new generic theoretical concepts describing the entire process, from design to execution.<\/p>\n<\/p>\n

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scPoli training<\/h2>\n<\/p>\n