Uncovering influence links in molecular knowledge networks to streamline personalized medicine

Shin, Dmitriy; Arthur, Gerald L.; Popescu, Mihail; Korkin, Dmitry; Shyu, Chi‐Ren

doi:10.1016/j.jbi.2014.08.003

Cited by 11 publications

(7 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some authors [148] have also investigated advanced inference methods for mapping data from clinical biomarkers of key biological pathways to reproduce interactions and cooperative effects of signaling proteomic networks in each patient case [149,150]. A new framework [150] for providing case-specific rationales for theranostics has also been proposed.…”

Section: Mapping Multidimensional Cancer Datamentioning

confidence: 99%

Computational Approaches in Theranostics: Mining and Predicting Cancer Data

2019

View full text Add to dashboard Cite

The ability to understand the complexity of cancer-related data has been prompted by the applications of (1) computer and data sciences, including data mining, predictive analytics, machine learning, and artificial intelligence, and (2) advances in imaging technology and probe development. Computational modelling and simulation are systematic and cost-effective tools able to identify important temporal/spatial patterns (and relationships), characterize distinct molecular features of cancer states, and address other relevant aspects, including tumor detection and heterogeneity, progression and metastasis, and drug resistance. These approaches have provided invaluable insights for improving the experimental design of therapeutic delivery systems and for increasing the translational value of the results obtained from early and preclinical studies. The big question is: Could cancer theranostics be determined and controlled in silico? This review describes the recent progress in the development of computational models and methods used to facilitate research on the molecular basis of cancer and on the respective diagnosis and optimized treatment, with particular emphasis on the design and optimization of theranostic systems. The current role of computational approaches is providing innovative, incremental, and complementary data-driven solutions for the prediction, simplification, and characterization of cancer and intrinsic mechanisms, and to promote new data-intensive, accurate diagnostics and therapeutics.

show abstract

Section: Mapping Multidimensional Cancer Datamentioning

confidence: 99%

Computational Approaches in Theranostics: Mining and Predicting Cancer Data

2019

View full text Add to dashboard Cite

show abstract

“…It is especially evident in the direction that modern medical diagnostics and therapeutics, jointly coined as theranostics, is progressing. Pathway-based diagnostics is promising to open up a view at internal biological mechanisms of complex interplay of clinical biomarkers, diseases, signal transduction and other processes to be able to more precisely describe differences in individual patient cases [2]- [10]. Generation of a mechanistic picture of such processes can help develop combinatorial therapies utilizing novel drugs, small molecules inhibitors, cytotoxic and differentiating agents and other interventional techniques.…”

Section: Introductionmentioning

confidence: 99%

“…To this end, we have been investigating advanced inference methods to map clinical biomarkers data to biological pathways to recreate interplay of signaling proteomic networks for individual patient cases [20]. Our new computational formalism called Resource Description Framework (RDF)-induced Influgrams (RIIG) has been shown in a recent proof-of-concept study to exhibit qualities sufficient to provide case-specific reasoning for theranostics [10]. RIIG takes advantage of vast amounts of publicly available curated biological knowledge represented as the RDF format.…”

Section: Introductionmentioning

confidence: 99%

RDF Sketch Maps - Knowledge Complexity Reduction for Precision Medicine Analytics

Thanintorn¹,

Wang

Ersoy

et al. 2015

Biocomputing 2016

Self Cite

View full text Add to dashboard Cite

Realization of precision medicine ideas requires significant research effort to be able to spot subtle differences in complex diseases at the molecular level to develop personalized therapies. It is especially important in many cases of highly heterogeneous cancers. Precision diagnostics and therapeutics of such diseases demands interrogation of vast amounts of biological knowledge coupled with novel analytic methodologies. For instance, pathway-based approaches can shed light on the way tumorigenesis takes place in individual patient cases and pinpoint to novel drug targets. However, comprehensive analysis of hundreds of pathways and thousands of genes creates a combinatorial explosion, that is challenging for medical practitioners to handle at the point of care. Here we extend our previous work on mapping clinical omics data to curated Resource Description Framework (RDF) knowledge bases to derive influence diagrams of interrelationships of biomarker proteins, diseases and signal transduction pathways for personalized theranostics. We present RDF Sketch Maps-a computational method to reduce knowledge complexity for precision medicine analytics. The method of RDF Sketch Maps is inspired by the way a sketch artist conveys only important visual information and discards other unnecessary details. In our case, we compute and retain only so-called RDF Edges-places with highly important diagnostic and therapeutic information. To do this we utilize 35 maps of human signal transduction pathways by transforming 300 KEGG maps into highly processable RDF knowledge base. We have demonstrated potential clinical utility of RDF Sketch Maps in hematopoietic cancers, including analysis of pathways associated with Hairy Cell Leukemia (HCL) and Chronic Myeloid Leukemia (CML) where we achieved up to 20-fold reduction in the number of biological entities to be analyzed, while retaining most likely important entities. In experiments with pathways associated with HCL a generated RDF Sketch Map of the top 30% paths retained important information about signaling cascades leading to activation of proto-oncogene BRAF, which is usually associated with a different cancer, melanoma. Recent reports of successful treatments of HCL patients by the BRAF-targeted drug vemurafenib support the validity of the RDF Sketch Maps findings. We therefore believe that RDF Sketch Maps will be invaluable for hypothesis generation for precision diagnostics and therapeutics as well as drug repurposing studies.

show abstract

“…These pathways, presented in text and image formats, are great resources for studying biological functions and precision medicine practice. For example, the most upto-date knowledge about newly discovered non-canonical disease pathways and uncommon drug actions is vital in studying patientspecific biomolecular phenotypes for cancer treatment [4]. However, to effectively use large scale pathway information, new pathways from literature need to be carefully curated, reconciled, and transformed into a computable form [5].…”

Section: Gene Relationship Extractionmentioning

confidence: 99%

“…4. Testing performance of purely synthetically trained models using different types of structured noise.…”

mentioning

confidence: 99%

Augmenting biological pathway extraction with synthetic data and active learning

Thompson¹

View full text Add to dashboard Cite

The corpus of biomedical literature is growing rapidly as many papers are recorded in PubMed every day. These papers often contain high-quality biological pathways in their figures/text, which are great resources for studying biological mechanisms and precision medicine. However, it can take a long time for many of these works to be put into practical use as each paper's contributions need to be curated by experts. This, often lengthy, process causes professional practice to lag behind research. To speed up this process, I helped develop a pipeline that integrates NLP and object detection processing to extract gene relationships reported in articles' figures and text. This pipeline was able to extract such relationships with high precision and recall on a small, annotated set. However, extending this pipeline for improved generalization and new settings was limited by the number of high-quality annotations available. Such labeled data is very time consuming to collect and traditional augmentations were observed to generate diminishing returns. To address this shortcoming, I developed an approach for generating purely synthetic data for object detection on biological pathway diagrams based on a set of rules and domain knowledge. Our method iteratively generates each pathway relationship uniquely and is demonstrated to improve the generalization of our object detection model significantly across a variety of settings. Additionally, with the capability to generate unique and informative samples, we integrated our synthetic generation methodology into an active learning setting. While traditional active learning relies on a pool of unlabeled data to draw from with an acquisition function, our method is pool-less and does not require any acquisition function. Instead, we generate each batch of data uniquely based on the training losses from the previous batch. Pool-less Active Learning (PAL) via synthetic data generation is demonstrated to reduce the number of iterations required for model convergence during training on pathway figures.

show abstract

Uncovering influence links in molecular knowledge networks to streamline personalized medicine

Cited by 11 publications

References 63 publications

Computational Approaches in Theranostics: Mining and Predicting Cancer Data

Computational Approaches in Theranostics: Mining and Predicting Cancer Data

RDF Sketch Maps - Knowledge Complexity Reduction for Precision Medicine Analytics

Augmenting biological pathway extraction with synthetic data and active learning

Contact Info

Product

Resources

About