The modulating effect of chemical compounds and therapeutics on gene transcription is well-reported and has been intensively studied for both clinical and research purposes. Emerging research points ...toward the utility of drug-induced transcriptional alterations in de novo molecular design and highlights the idea of phenotype-matching an expression signature of interest to the structures being designed. In this work, we build an autoencoder-based generative model, BiCEV, around this concept. Our generative autoencoder has demonstrably generated a set of new molecules from gene expression input with notable validity (96%), uniqueness (98%), and internal diversity (0.77). Further, we attempted to validate BiCEV by testing the model on gene-knockdown profiles and combined signatures of synergistic drug pairs. From these investigations, we found the designed structures to be consistently high in collective quality. However, when their similarities to the supposed functional equivalents as determined by shared targets were considered, the findings were somewhat mixed. In spite of this, we believe the generative model merits further development in conjunction with in vitro corroboration to lend itself to being an assistive tool for drug discovery experts, particularly to support the initial stages of hit identification and lead optimization.
Philadelphia chromosome is a specific genetic abnormality, a reciprocal translocation between chromosome 9 and chromosome 22. This abnormality can cause Chronic Myelogenous Leukemia (CML). Although ...there are many techniques to diagnose Philadelphia chromosome such as karyotyping, Fluorescence in Situ Hybridizations (FISH), and chromosome painting, etc., these techniques are expensiye and can lead to patients' financial problem. In addition, the number of expert medical technicians who can diagnose chromosomes abnormality is yery low. Thus, it takes many days to inform the result to the patients and increases medical technicians' workloads. This paper proposes a Philadelphia chromosome detection framework using image processing and deep learning techniques. It will help medical technicians to screen the patients who haye the Philadelphia chromosome, so it can reduce the workloads of medical technicians and the time that patients haye to wait for the result. Additionally, this framework can improye the diagnosis of Philadelphia chromosome with less cost.
