Generative artificial intelligence-based algorithm to increase the predictivity of preclinical studies while keeping sample sizes small

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The translation of basic science into new clinically effective substances often seems unsatisfactory. This has been attributed to inadequate data quality standards and small sample sizes in preclinical studies. The development of an artificial intelligence (AI)-based method is proposed to artificially generate valid additional data without increasing the sample size in preclinical experiments. The proposed method is based on generative models (GM), which are capable of generating valid data from a non-trivial, possibly high-dimensional distribution, which are initially unknown and often can only be described analytically to a limited extent. The generation of valid new data therefore requires an algorithm that can recognize high-dimensional structures in the data and then use this structure to generate new data that is consistent with the available data.This project aims to compare the most promising approaches in density-based clustering and data generation for their suitability to generate more but appropriate data from too few data. Based on extensive previous experience and preliminary experimental results, a method based on emergent self-organizing maps (ESOM), as structure- detecting algorithm based on artificial neural networks, will be developed. It will be compared with alternative methods of artificial data generation and distance-based clustering including but not exclusively limited to Generative Adversarial Networks (GAN), Gaussian mixture models (GMM), hidden Markov models (HMM), naïve Bayesian models, latent dirichlet allocation and (restricted) Boltzman machines and DataBoost-IM, as well as hierarchical DBSCAN, adaptive density peak clustering, and adaptive affinity propagation clustering. The most suitable approach should be developed into a feasible method for preclinical studies.The work programme will thus include (i) basic research to further develop and refine the generative ESOM method including the comparative evaluation of a possible alternative method, (ii) its comparison with alternative contemporary methods of generative machine-learning for of data generation and density based clustering, (iii) the application of the most suitable method to various different biomedical data sets including the development of a working solution for the generation of valid new cases in data from preclinical research, accompanied and followed by (iv) the implementation of the ESOM-based generative method of data completion as a freely available software solution.Thus, the primary objective of the planned project is the development of a method that uses machine-learning algorithms for valid and probabilistic generation of experimental data that, together with the original data, provides the high data density necessary to draw valid conclusions from preclinical experiments.