Modeling disease progression in Multiple Myeloma with Hopfield networks and singlecell RNAseq Associative memories in Hopfieldu0027s neural networks are mapped to gene expression pattern to model different paths of disease progression towards Multiple Myeloma (MM). The model is built using single cell RNAseq data from bone marrow aspirates of MM patients as well as patients diagnosed with Monoclonal Gammopathy of Undetermined Significance (MGUS) and Smoldering Multiple Myeloma (SMM), two medical conditions that often progress to full MM. Results: We identify different clusters of MGUS, SMM, and MM cells, map them to Hopfield associative memory patterns, and model the dynamics of transition between the different patterns. The model is then used to identify genes that are differentialy expressed across different MM stages and whose simultaneous inhibition is associated to a delayed disease progression.

Classification of Five Cell Types from PBMC Samples using Single Cell Transcriptomics and Artificial Neural Networks We used 27 human single cell transcriptomics (SCT) data sets to develop an artificial neural network (ANN) model for classification of Peripheral Blood Mononuclear Cells (PBMC). We demonstrated that highly accurate models for the classification of PBMC subtypes can be developed by combining multiple independent data sets to form training data sets. A significant data preparation effort was needed for building predictive models. Using a data set of xe2x88xbc120,000 single cell instances we showed the accuracy of classification of PBMC call of xe2x88xbc 90%. Optimization techniques and the addition of new highquality data sets for model training are expected to improve PBMC subtype classification accuracy.