Literature Based Discovery (LBD): Towards Hypothesis Generation and Knowledge Discovery in Biomedical Text Mining

Biomedical knowledge is growing in an astounding pace with a majority of this knowledge is represented as scientific publications. Text mining tools and methods represents automatic approaches for extracting hidden patterns and trends from this semi structured and unstructured data. In Biomedical Text mining, Literature Based Discovery (LBD) is the process of automatically discovering novel associations between medical terms otherwise mentioned in disjoint literature sets. LBD approaches proven to be successfully reducing the discovery time of potential associations that are hidden in the vast amount of scientific literature. The process focuses on creating concept profiles for medical terms such as a disease or symptom and connecting it with a drug and treatment based on the statistical significance of the shared profiles. This knowledge discovery approach introduced in 1989 still remains as a core task in text mining. Currently the ABC principle based two approaches namely open discovery and closed discovery are mostly explored in LBD process. This review starts with general introduction about text mining followed by biomedical text mining and introduces various literature resources such as MEDLINE, UMLS, MESH, and SemMedDB. This is followed by brief introduction of the core ABC principle and its associated two approaches open discovery and closed discovery in LBD process. This review also discusses the deep learning applications in LBD by reviewing the role of transformer models and neural networks based LBD models and its future aspects. Finally, reviews the key biomedical discoveries generated through LBD approaches in biomedicine and conclude with the current limitations and future directions of LBD.Comment: 43 Pages, 5 Figures, 4 Table

Inferring a Transcriptional Regulatory Network from Gene Expression Data Using Nonlinear Manifold Embedding

Transcriptional networks consist of multiple regulatory layers corresponding to the activity of global regulators, specialized repressors and activators of transcription as well as proteins and enzymes shaping the DNA template. Such intrinsic multi-dimensionality makes uncovering connectivity patterns difficult and unreliable and it calls for adoption of methodologies commensurate with the underlying organization of the data source. Here we present a new computational method that predicts interactions between transcription factors and target genes using a compendium of microarray gene expression data and the knowledge of known interactions between genes and transcription factors. The proposed method called Kernel Embedding of REgulatory Networks (KEREN) is based on the concept of gene-regulon association and it captures hidden geometric patterns of the network via manifold embedding. We applied KEREN to reconstruct gene regulatory interactions in the model bacteria E.coli on a genome-wide scale. Our method not only yields accurate prediction of verifiable interactions, which outperforms on certain metrics comparable methodologies, but also demonstrates the utility of a geometric approach to the analysis of high-dimensional biological data. We also describe the general application of kernel embedding techniques to some other function and network discovery algorithms

Large-Scale User Modeling with Recurrent Neural Networks for Music Discovery on Multiple Time Scales

The amount of content on online music streaming platforms is immense, and most users only access a tiny fraction of this content. Recommender systems are the application of choice to open up the collection to these users. Collaborative filtering has the disadvantage that it relies on explicit ratings, which are often unavailable, and generally disregards the temporal nature of music consumption. On the other hand, item co-occurrence algorithms, such as the recently introduced word2vec-based recommenders, are typically left without an effective user representation. In this paper, we present a new approach to model users through recurrent neural networks by sequentially processing consumed items, represented by any type of embeddings and other context features. This way we obtain semantically rich user representations, which capture a user's musical taste over time. Our experimental analysis on large-scale user data shows that our model can be used to predict future songs a user will likely listen to, both in the short and long term.Comment: Author pre-print version, 20 pages, 6 figures, 4 table

Analyzing Learned Molecular Representations for Property Prediction

Advancements in neural machinery have led to a wide range of algorithmic solutions for molecular property prediction. Two classes of models in particular have yielded promising results: neural networks applied to computed molecular fingerprints or expert-crafted descriptors, and graph convolutional neural networks that construct a learned molecular representation by operating on the graph structure of the molecule. However, recent literature has yet to clearly determine which of these two methods is superior when generalizing to new chemical space. Furthermore, prior research has rarely examined these new models in industry research settings in comparison to existing employed models. In this paper, we benchmark models extensively on 19 public and 16 proprietary industrial datasets spanning a wide variety of chemical endpoints. In addition, we introduce a graph convolutional model that consistently matches or outperforms models using fixed molecular descriptors as well as previous graph neural architectures on both public and proprietary datasets. Our empirical findings indicate that while approaches based on these representations have yet to reach the level of experimental reproducibility, our proposed model nevertheless offers significant improvements over models currently used in industrial workflows