Biology subjects

Hochheiser, H.

Publications and source records attributed to Hochheiser, H..

3 recordsLinked to original sources

bioRxiv2018

Longitudinal Visual Analytics for Unpacking the Cancer Journey

Retrospective cancer research requires identification of patients matching both categorical and temporal inclusion criteria, often based on factors exclusively available in clinical notes. Although natural language processing approaches for inferring higher-level concepts have shown promise for bringing structure to clinical texts, interpreting results is often challenging, involving the need to move between abstracted representations and constituent text elements. We discuss qualitative inquiry into user tasks and goals, data elements and models resulting in an innovative natural language processing pipeline and a visual analytics tool designed to facilitate interpretation of patient summaries and identification of cohorts for retrospective research.

cancer biology↗

bioRxiv2018

Systematic Discovery of the Functional Impact of Somatic Genome Alterations in Individual Tumors through Tumor-specific Causal Inference

We report a tumor-specific causal inference (TCI) framework, which discovers causative somatic genome alterations (SGAs) through inferring causal relationships between SGAs and molecular phenotypes (e.g., transcriptomic, proteomic, or metabolomic changes) within an individual tumor. We applied the TCI algorithm to tumors from The Cancer Genome Atlas (TCGA) and identified those SGAs that causally regulate the differentially expressed genes (DEGs) within each tumor. Overall, TCI identified 634 SGAs that cause cancer-related DEGs in a significant number of tumors, including most of the previously known drivers and many novel candidate cancer drivers. The inferred causal relationships are statistically robust and biologically sensible, and multiple lines of experimental evidence support the predicted functional impact of both well-known and novel candidate drivers. By identifying major candidate drivers and revealing their functional impact in a tumor, TCI shed light on disease mechanisms of each tumor, providing useful information for advancing cancer biology and precision oncology.\n\nSignificance statementsCancer is mainly caused by SGAs. Precision oncology involves identifying and targeting tumor-specific aberrations resulting from causative SGAs. TCI is a novel computational framework for discovering the causative SGAs and their impact on oncogenic processes, thus revealing tumor-specific disease mechanisms. This information can be used to guide precision oncology.

cancer biology↗

bioRxiv2016

The Monarch Initiative: Insights across species reveal human disease mechanisms

The principles of genetics apply across the whole tree of life: on a cellular level, we share mechanisms with species from which we diverged millions or even billions of years ago. We can exploit this common ancestry at the level of sequences, but also in terms of observable outcomes (phenotypes), to learn more about health and disease for humans and all other species. Applying the range of available knowledge to solve challenging disease problems requires unified data relating genomics, phenotypes, and disease; it also requires computational tools that leverage these multimodal data to inform interpretations by geneticists and to suggest experiments. However, the distribution and heterogeneity of databases is a major impediment: databases tend to focus either on a single data type across species, or on single species across data types. Although each database provides rich, high-quality information, no single one provides unified data that is comprehensive across species, biological scales, and data types. Without a big-picture view of the data, many questions in genetics are difficult or impossible to answer. The Monarch Initiative (https://monarchinitiative.org) is an international consortium dedicated to providing computational tools that leverage a computational representation of phenotypic data for genotype-phenotype analysis, genomic diagnostics, and precision medicine on the basis of a large-scale platform of multimodal data that is deeply integrated across species and covering broad areas of disease.

evolutionary biology↗