A Machine Learning Framework for Identifying Molecular Biomarkers from Transcriptomic Cancer Data

Cancer is a complex molecular process due to abnormal changes in the genome, such as mutation and copy number variation, and epigenetic aberrations such as dysregulations of long non-coding RNA (lncRNA). These abnormal changes are reflected in transcriptome by turning oncogenes on and tumor suppressor genes off, which are considered cancer biomarkers. However, transcriptomic data is high dimensional, and finding the best subset of genes (features) related to causing cancer is computationally challenging and expensive. Thus, developing a feature selection framework to discover molecular biomarkers for cancer is critical. Traditional approaches for biomarker discovery calculate the fold change for each gene, comparing expression profiles between tumor and healthy samples, thus failing to capture the combined effect of the whole gene set. Also, these approaches do not always investigate cancer-type prediction capabilities using discovered biomarkers. In this work, we proposed a machine learning-based framework to address all of the above challenges in discovering lncRNA biomarkers. First, we developed a machine learning pipeline that takes lncRNA expression profiles of cancer samples as input and outputs a small set of key lncRNAs that can accurately predict multiple cancer types. A significant innovation of our work is its ability to identify biomarkers without using healthy samples. However, this initial framework cannot identify cancer-specific lncRNAs. Second, we extended our framework to identify cancer type and subtype-specific lncRNAs. Third, we proposed to use a state-of-the-art deep learning algorithm concrete autoencoder (CAE) in an unsupervised setting, which efficiently identifies a subset of the most informative features. However, CAE does not identify reproducible features in different runs due to its stochastic nature. Thus, we proposed a multi-run CAE (mrCAE) to identify a stable set of features to address this issue. Our deep learning-based pipeline significantly extended the previous state-of-the-art feature selection techniques. Finally, we showed that discovered biomarkers are biologically relevant using literature review and prognostically significant using survival analyses. The discovered novel biomarkers could be used as a screening tool for different cancer diagnoses and as therapeutic targets

Enhancing Reaction-based de novo Design using Machine Learning

De novo design is a branch of chemoinformatics that is concerned with the rational design of molecular structures with desired properties, which specifically aims at achieving suitable pharmacological and safety profiles when applied to drug design. Scoring, construction, and search methods are the main components that are exploited by de novo design programs to explore the chemical space to encourage the cost-effective design of new chemical entities. In particular, construction methods are concerned with providing strategies for compound generation to address issues such as drug-likeness and synthetic accessibility. Reaction-based de novo design consists of combining building blocks according to transformation rules that are extracted from collections of known reactions, intending to restrict the enumerated chemical space into a manageable number of synthetically accessible structures. The reaction vector is an example of a representation that encodes topological changes occurring in reactions, which has been integrated within a structure generation algorithm to increase the chances of generating molecules that are synthesisable. The general aim of this study was to enhance reaction-based de novo design by developing machine learning approaches that exploit publicly available data on reactions. A series of algorithms for reaction standardisation, fingerprinting, and reaction vector database validation were introduced and applied to generate new data on which the entirety of this work relies. First, these collections were applied to the validation of a new ligand-based design tool. The tool was then used in a case study to design compounds which were eventually synthesised using very similar procedures to those suggested by the structure generator. A reaction classification model and a novel hierarchical labelling system were then developed to introduce the possibility of applying transformations by class. The model was augmented with an algorithm for confidence estimation, and was used to classify two datasets from industry and the literature. Results from the classification suggest that the model can be used effectively to gain insights on the nature of reaction collections. Classified reactions were further processed to build a reaction class recommendation model capable of suggesting appropriate reaction classes to apply to molecules according to their fingerprints. The model was validated, then integrated within the reaction vector-based design framework, which was assessed on its performance against the baseline algorithm. Results from the de novo design experiments indicate that the use of the recommendation model leads to a higher synthetic accessibility and a more efficient management of computational resources