Beyond Protecting Genetic Privacy: Understanding Genetic Discrimination Through its Disparate Impact on Racial Minorities

At the very end of the last century, scientists produced the first draft of the whole human genetic sequence. But that was just the first step; the hard work of the first few decades of this century will be to learn more about how to apply genetic information to improve health. As the pace of technological development accelerates and we learn more about what genetic variations mean about individual human characteristics and health risks, so too does the risk and consequences of the misuse of such information become more significant. The principal answer to this challenge has been to safeguard privacy by constructing legal and technical barriers that conceal and anonymize genetic information. While it may be a worthwhile objective, ultimately privacy protections will likely fail in practice. If this is so, how can we prevent genetic information from being used to categorize, stigmatize, and subordinate? This Note approaches this problem by analyzing the African American experience with genetic discrimination in the United States. African Americans have confronted the adverse consequences of genetic research in ways that can serve as a foundation to understand future threats posed to racial minorities and everyone in society, as genetic testing increases in prevalence and the privacy of genetic information is unable to be protected. Studying the real history of genetic discrimination, rather than merely speculating about what may happen, can point toward policy solutions that go beyond ―genetic privacy.‖ As genetic information becomes more plentiful and valuable, policies to prevent the misuse of that information will benefit everyone, regardless of race or ethnicity

Molecular health engineering: virtual reconstruction of intracellular biomolecular dynamics in clinical samples

Poster presented at Biomedical Technology Showcase 2006, Philadelphia, PA. Retrieved 18 Aug 2006 from http://www.biomed.drexel.edu/new04/Content/Biomed_Tech_Showcase/Poster_Presentations/Sokhansanj.pdf.Clinical Problem: Chronic diseases i.e. diabetes, COPD, caused by chronic inflammation, fundamentally alters the health of a cell at the molecular and cellular level. This reduced capacity is seen especially when exposed to acute inflammation from infections and trauma. Can "cell health" of people be "imaged" for diagnostic and therapeutic development? Engineering Challenge: Accurately and comprehensively visualize the dynamics of proteins and cells? Interdisciplinary Solution: Measure key components of "cell health", associated with cellular energetics, damage, apoptosis, necrosis in samples from patients. Data obtained exposing cells to in vitro perturbations are applied to estimate a dynamic "cell health" model that can be used as a "virtual" system to analyze and predict cellular changes in response to acute stress

Estimating the impact of human base excision repair protein variants on the repair of oxidative DNA base damage

Cancer Epidemiol. Biomarkers Prev., 15(5): 1000-1008.Epidemiological studies have revealed a complex association between human genetic variance and cancer risk. Quantitative biological modeling based on experimental data can play a critical role in interpreting the impact of genetic variation on biochemical pathways relevant to cancer development and progression. Defects in human DNA base excision repair (BER) proteins can reduce cellular tolerance to oxidative DNA base damage caused by endogenous and exogenous sources, such as exposure to toxins and ionizing radiation. If not repaired, DNA base damage leads to cell dysfunction and mutagenesis, consequently leading to cancer, disease, and aging. Population screens have identified numerous single nucleotide polymorphism (SNP) variants in many BER proteins, and some have been purified and found to exhibit mild kinetic defects. Epidemiological studies have led to conflicting conclusions on the association between SNP variants in BER proteins and cancer risk. Using experimental data for cellular concentration and the kinetics of normal and variant BER proteins, we apply a previously developed and tested human BER pathway model to (i) estimate the impact of mild variants on BER of abasic sites and 8-oxoguanine, a prominent oxidative DNA base modification, (ii) identify ranges of variation associated with substantial BER capacity loss, and (iii) reveal non-intuitive consequences of multiple simultaneous variants. Our findings support previous work suggesting that mild BER variants have a minimal effect on pathway capacity, while more severe defects and simultaneous variation in several BER proteins can lead to inefficient repair and potentially deleterious consequences of cellular damage

Linear fuzzy gene network models obtained from microarray data by exhaustive search

BACKGROUND: Recent technological advances in high-throughput data collection allow for experimental study of increasingly complex systems on the scale of the whole cellular genome and proteome. Gene network models are needed to interpret the resulting large and complex data sets. Rationally designed perturbations (e.g., gene knock-outs) can be used to iteratively refine hypothetical models, suggesting an approach for high-throughput biological system analysis. We introduce an approach to gene network modeling based on a scalable linear variant of fuzzy logic: a framework with greater resolution than Boolean logic models, but which, while still semi-quantitative, does not require the precise parameter measurement needed for chemical kinetics-based modeling. RESULTS: We demonstrated our approach with exhaustive search for fuzzy gene interaction models that best fit transcription measurements by microarray of twelve selected genes regulating the yeast cell cycle. Applying an efficient, universally applicable data normalization and fuzzification scheme, the search converged to a small number of models that individually predict experimental data within an error tolerance. Because only gene transcription levels are used to develop the models, they include both direct and indirect regulation of genes. CONCLUSION: Biological relationships in the best-fitting fuzzy gene network models successfully recover direct and indirect interactions predicted from previous knowledge to result in transcriptional correlation. Fuzzy models fit on one yeast cell cycle data set robustly predict another experimental data set for the same system. Linear fuzzy gene networks and exhaustive rule search are the first steps towards a framework for an integrated modeling and experiment approach to high-throughput "reverse engineering" of complex biological systems

Visualization of protein-protein interaction network for knowledge discovery

Paper presented at the 2006 IEEE International Conference on Granular Computing, Atlanta, GA.A new visualization tool, called "Visual Concept Explorer (VCE)", was developed to visualize concept relationships in bio-medical literatura VCE integrates Pathfinder Network Scaling and Kohonen Self-organizing Feature Map Algorithm for visual mapping. As a case study, VCE was applied to visualize a chromatin protein-protein interaction (PPI) network The mapping results demonstrated that VCE could explore the semantic structure and latent domain knowledge hidden in protein-protein interaction data sets generatedfrom bio-medical literature

Accelerated search for biomolecular network models to interpret high-throughput experimental data

<p>Abstract</p> <p>Background</p> <p>The functions of human cells are carried out by biomolecular networks, which include proteins, genes, and regulatory sites within DNA that encode and control protein expression. Models of biomolecular network structure and dynamics can be inferred from high-throughput measurements of gene and protein expression. We build on our previously developed fuzzy logic method for bridging quantitative and qualitative biological data to address the challenges of noisy, low resolution high-throughput measurements, i.e., from gene expression microarrays. We employ an evolutionary search algorithm to accelerate the search for hypothetical fuzzy biomolecular network models consistent with a biological data set. We also develop a method to estimate the probability of a potential network model fitting a set of data by chance. The resulting metric provides an estimate of both model quality and dataset quality, identifying data that are too noisy to identify meaningful correlations between the measured variables.</p> <p>Results</p> <p>Optimal parameters for the evolutionary search were identified based on artificial data, and the algorithm showed scalable and consistent performance for as many as 150 variables. The method was tested on previously published human cell cycle gene expression microarray data sets. The evolutionary search method was found to converge to the results of exhaustive search. The randomized evolutionary search was able to converge on a set of similar best-fitting network models on different training data sets after 30 generations running 30 models per generation. Consistent results were found regardless of which of the published data sets were used to train or verify the quantitative predictions of the best-fitting models for cell cycle gene dynamics.</p> <p>Conclusion</p> <p>Our results demonstrate the capability of scalable evolutionary search for fuzzy network models to address the problem of inferring models based on complex, noisy biomolecular data sets. This approach yields multiple alternative models that are consistent with the data, yielding a constrained set of hypotheses that can be used to optimally design subsequent experiments.</p

DNA base excision repair nanosystem engineering: model development

Paper presented at 27th Annual International IEEE EMBS Conference (EMBS 2005), Shanghai, China.DNA base damage results from a combination of endogenous sources, (normal metabolism, increased metabolism due to obesity, stress from diseases such as arthritis and diabetes, and ischemia) and the environment (ingested toxins, ionizing radiation, etc.). If unrepaired DNA base damage can lead to diminished cell function, and potentially diseases and eventually mutations that lead to cancer. Sophisticated DNA repair mechanisms have evolved in all living cells to preserve the integrity of inherited genetic information and transcriptional control. Understanding a system like DNA repair is greatly enhanced by using engineering methods, in particular modeling interactions and using predictive simulation to analyze the impact of perturbations. We describe the use of such a “nanosystem engineering” approach to analyze the DNA base excision repair pathway in human cells, and use simulation to predict the impact of varying enzyme concentration on DNA repair capacity

Predicting Institution Outcomes for Inter Partes Review (IPR) Proceedings at the United States Patent Trial &amp; Appeal Board by Deep Learning of Patent Owner Preliminary Response Briefs

A key challenge for artificial intelligence in the legal field is to determine from the text of a party&rsquo;s litigation brief whether, and why, it will succeed or fail. This paper shows a proof-of-concept test case from the United States: predicting outcomes of post-grant inter partes review (IPR) proceedings for invalidating patents. The objectives are to compare decision-tree and deep learning methods, validate interpretability methods, and demonstrate outcome prediction based on party briefs. Specifically, this study compares and validates two distinct approaches: (1) representing documents with term frequency inverse document frequency (TF-IDF), training XGBoost gradient-boosted decision-tree models, and using SHAP for interpretation. (2) Deep learning of document text in context, using convolutional neural networks (CNN) with attention, and comparing LIME and attention visualization for interpretability. The methods are validated on the task of automatically determining case outcomes from unstructured written decision opinions, and then used to predict trial institution or denial based on the patent owner&rsquo;s preliminary response brief. The results show how interpretable deep learning architecture classifies successful/unsuccessful response briefs on temporally separated training and test sets. More accurate prediction remains challenging, likely due to the fact-specific, technical nature of patent cases and changes in applicable law and jurisprudence over time