MKEM: a Multi-level Knowledge Emergence Model for mining undiscovered public knowledge

<p>Abstract</p> <p>Background</p> <p>Since Swanson proposed the Undiscovered Public Knowledge (UPK) model, there have been many approaches to uncover UPK by mining the biomedical literature. These earlier works, however, required substantial manual intervention to reduce the number of possible connections and are mainly applied to disease-effect relation. With the advancement in biomedical science, it has become imperative to extract and combine information from multiple disjoint researches, studies and articles to infer new hypotheses and expand knowledge.</p> <p>Methods</p> <p>We propose MKEM, a Multi-level Knowledge Emergence Model, to discover implicit relationships using Natural Language Processing techniques such as Link Grammar and Ontologies such as Unified Medical Language System (UMLS) MetaMap. The contribution of MKEM is as follows: First, we propose a flexible knowledge emergence model to extract implicit relationships across different levels such as molecular level for gene and protein and Phenomic level for disease and treatment. Second, we employ MetaMap for tagging biological concepts. Third, we provide an empirical and systematic approach to discover novel relationships.</p> <p>Results</p> <p>We applied our system on 5000 abstracts downloaded from PubMed database. We performed the performance evaluation as a gold standard is not yet available. Our system performed with a good precision and recall and we generated 24 hypotheses.</p> <p>Conclusions</p> <p>Our experiments show that MKEM is a powerful tool to discover hidden relationships residing in extracted entities that were represented by our Substance-Effect-Process-Disease-Body Part (SEPDB) model. </p

Integrated Bio-Entity Network: A System for Biological Knowledge Discovery

A significant part of our biological knowledge is centered on relationships between biological entities (bio-entities) such as proteins, genes, small molecules, pathways, gene ontology (GO) terms and diseases. Accumulated at an increasing speed, the information on bio-entity relationships is archived in different forms at scattered places. Most of such information is buried in scientific literature as unstructured text. Organizing heterogeneous information in a structured form not only facilitates study of biological systems using integrative approaches, but also allows discovery of new knowledge in an automatic and systematic way. In this study, we performed a large scale integration of bio-entity relationship information from both databases containing manually annotated, structured information and automatic information extraction of unstructured text in scientific literature. The relationship information we integrated in this study includes protein–protein interactions, protein/gene regulations, protein–small molecule interactions, protein–GO relationships, protein–pathway relationships, and pathway–disease relationships. The relationship information is organized in a graph data structure, named integrated bio-entity network (IBN), where the vertices are the bio-entities and edges represent their relationships. Under this framework, graph theoretic algorithms can be designed to perform various knowledge discovery tasks. We designed breadth-first search with pruning (BFSP) and most probable path (MPP) algorithms to automatically generate hypotheses—the indirect relationships with high probabilities in the network. We show that IBN can be used to generate plausible hypotheses, which not only help to better understand the complex interactions in biological systems, but also provide guidance for experimental designs

Literature-Assisted Validation of a Novel Causal Inference Graph in a Sparsely Sampled Multi-Regimen Exercise Data

Background. Causal mechanisms supporting the cardio-metabolic benefits of exercise can be identified for individuals who cannot exercise. With the use of appropriate causal discovery algorithms, the causal pathways can be found for even sparsely sampled data which will help direct drug discovery and pharmaceutical industries to create the appropriate drug to maintain muscles. Objective. The purpose of this study was to infer novel causal source-target interactions active in sparsely sampled data and embed these in a broader causal network extracted from the literature to test their alignment with community-wide prior knowledge and their mechanistic validity in the context of regulatory feedback dynamics. Methods. To this goal, emphasis was placed on the female STRRIDE1/PD dataset to see how the observed data predicts a Causal Directed Acyclic Graph (C-DAG). The analytes in the dataset with greater than 5 missing values were dropped from further analysis to retain a higher confidence among the graphs. The PC, named after its authors Peter and Clark, algorithm was executed for ten thousand iterations on randomly sampled columns of the modified dataset keeping intensity and amount constant as the first two columns to see their effect on the resultant DAG. Out of the 10,000 iterations, interactions that appeared more than 45%, 50%, 65%, 75% and 100% were observed. The interactions that appeared more than 50% of the times were then compared to the literature mined dataset using MedScan Natural Language Processing (NLP) techniques as a part of Pathway Studio. Results. Full consensus across all sub-sampled networks produced 136 interactions that were fully conserved. Of these 136 interactions, 64 were resolved as direct causal interactions, 5 were not direct causal interactions and 67 could only be described as associative. It was found that about 17% of the interactions were recovered from the text mining of the 285 peer-reviewed journals from a total of 64 that were predicted at a 50% consensus. Out of these 11, 4 were completely recovered whereas 7 were only partially recovered. A completely recovered interaction was LDL → ApoB and a partially recovered interaction was HDL → insulin sensitivity. Conclusion. Only 17% of the predicted interactions were found through literature mining, remaining 83% were a mix of novel interactions and self-interactions that need to be worked on further. Of the remaining interactions, 53 remain novel and give insight into how different clinical parameters interact with the cholesterol molecules, biological markers and how they interact with each other

Positive-Unlabeled Learning for inferring drug interactions based on heterogeneous attributes

BACKGROUND: Investigating and understanding drug-drug interactions (DDIs) is important in improving the effectiveness of clinical care. DDIs can occur when two or more drugs are administered together. Experimentally based DDI detection methods require a large cost and time. Hence, there is a great interest in developing efficient and useful computational methods for inferring potential DDIs. Standard binary classifiers require both positives and negatives for training. In a DDI context, drug pairs that are known to interact can serve as positives for predictive methods. But, the negatives or drug pairs that have been confirmed to have no interaction are scarce. To address this lack of negatives, we introduce a Positive-Unlabeled Learning method for inferring potential DDIs. RESULTS: The proposed method consists of three steps: i) application of Growing Self Organizing Maps to infer negatives from the unlabeled dataset; ii) using a pairwise similarity function to quantify the overlap between individual features of drugs and iii) using support vector machine classifier for inferring DDIs. We obtained 6036 DDIs from DrugBank database. Using the proposed approach, we inferred 589 drug pairs that are likely to not interact with each other; these drug pairs are used as representative data for the negative class in binary classification for DDI prediction. Moreover, we classify the predicted DDIs as Cytochrome P450 (CYP) enzyme-Dependent and CYP-Independent interactions invoking their locations on the Growing Self Organizing Map, due to the particular importance of these enzymes in clinically significant interaction effects. Further, we provide a case study on three predicted CYP-Dependent DDIs to evaluate the clinical relevance of this study. CONCLUSION: Our proposed approach showed an absolute improvement in F1-score of 14 and 38% in comparison to the method that randomly selects unlabeled data points as likely negatives, depending on the choice of similarity function. We inferred 5300 possible CYP-Dependent DDIs and 592 CYP-Independent DDIs with the highest posterior probabilities. Our discoveries can be used to improve clinical care as well as the research outcomes of drug development

Integrated Bio-Entity Network: A System for Biological Knowledge Discovery

A significant part of our biological knowledge is centered on relationships between biological entities (bio-entities) such as proteins, genes, small molecules, pathways, gene ontology (GO) terms and diseases. Accumulated at an increasing speed, the information on bio-entity relationships is archived in different forms at scattered places. Most of such information is buried in scientific literature as unstructured text. Organizing heterogeneous information in a structured form not only facilitates study of biological systems using integrative approaches, but also allows discovery of new knowledge in an automatic and systematic way. In this study, we performed a large scale integration of bio-entity relationship information from both databases containing manually annotated, structured information and automatic information extraction of unstructured text in scientific literature. The relationship information we integrated in this study includes protein–protein interactions, protein/gene regulations, protein–small molecule interactions, protein–GO relationships, protein–pathway relationships, and pathway–disease relationships. The relationship information is organized in a graph data structure, named integrated bio-entity network (IBN), where the vertices are the bio-entities and edges represent their relationships. Under this framework, graph theoretic algorithms can be designed to perform various knowledge discovery tasks. We designed breadth-first search with pruning (BFSP) and most probable path (MPP) algorithms to automatically generate hypotheses—the indirect relationships with high probabilities in the network. We show that IBN can be used to generate plausible hypotheses, which not only help to better understand the complex interactions in biological systems, but also provide guidance for experimental designs

Exploiting Latent Features of Text and Graphs

As the size and scope of online data continues to grow, new machine learning techniques become necessary to best capitalize on the wealth of available information. However, the models that help convert data into knowledge require nontrivial processes to make sense of large collections of text and massive online graphs. In both scenarios, modern machine learning pipelines produce embeddings --- semantically rich vectors of latent features --- to convert human constructs for machine understanding. In this dissertation we focus on information available within biomedical science, including human-written abstracts of scientific papers, as well as machine-generated graphs of biomedical entity relationships. We present the Moliere system, and our method for identifying new discoveries through the use of natural language processing and graph mining algorithms. We propose heuristically-based ranking criteria to augment Moliere, and leverage this ranking to identify a new gene-treatment target for HIV-associated Neurodegenerative Disorders. We additionally focus on the latent features of graphs, and propose a new bipartite graph embedding technique. Using our graph embedding, we advance the state-of-the-art in hypergraph partitioning quality. Having newfound intuition of graph embeddings, we present Agatha, a deep-learning approach to hypothesis generation. This system learns a data-driven ranking criteria derived from the embeddings of our large proposed biomedical semantic graph. To produce human-readable results, we additionally propose CBAG, a technique for conditional biomedical abstract generation

Demystifying security and compatibility issues in Android Apps

Never before has any OS been so popular as Android. Existing mobile phones are not simply devices for making phone calls and receiving SMS messages, but powerful communication and entertainment platforms for web surfing, social networking, etc. Even though the Android OS offers powerful communication and application execution capabilities, it is riddled with defects (e.g., security risks, and compatibility issues), new vulnerabilities come to light daily, and bugs cost the economy tens of billions of dollars annually. For example, malicious apps (e.g., back-doors, fraud apps, ransomware, spyware, etc.) are reported [Google, 2022] to exhibit malicious behaviours, including privacy stealing, unwanted programs installed, etc. To counteract these threats, many works have been proposed that rely on static analysis techniques to detect such issues. However, static techniques are not sufficient on their own to detect such defects precisely. This will likely yield false positive results as static analysis has to make some trade-offs when handling complicated cases (e.g., object-sensitive vs. object-insensitive). In addition, static analysis techniques will also likely suffer from soundness issues because some complicated features (e.g., reflection, obfuscation, and hardening) are difficult to be handled [Sun et al., 2021b, Samhi et al., 2022].Comment: Thesi