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Open Access Publications from the University of California

Scholarly Works (87 results)

  • Article
  • Peer Reviewed
UC Irvine Previously Published Works (2020)

Introduction:

The course of COVID-19 varies from asymptomatic to severe in patients. The basis for this range in symptoms is unknown. One possibility is that genetic variation is partly responsible for the highly variable response. We evaluated how well a genetic risk score based on chromosomal-scale length variation and machine learning classification algorithms could predict severity of response to SARS-CoV-2 infection.

Methods:

.We compared 981 patients from the UK Biobank dataset who had a severe reaction to SARS-CoV-2 infection before 27 April 2020 to a similar number of age matched patients drawn for the general UK Biobank population. For each patient, we built a profile of 88 numbers characterizing the chromosomal-scale length variability of their germ line DNA. Each number represented one quarter of the 22 autosomes. We used the machine learning algorithm XGBoost to build a classifier that could predict whether a person would have a severe reaction to COVID-19 based only on their 88-number classification.

Results:

. We found that the XGBoost classifier could differentiate between the two classes at a significant level (I as measured against a randomized control and (n as measured against the expected value of a random guessing algorithm (AUC=0.5). However, we found that the AUC of the classifier was only 0.51, too low for a clinically useful test.

Conclusion:

Genetics play a role in the severity of COVID-19, but we cannot yet develop a useful genetic test to predict severity.
    Cover page: Evaluation of a genetic risk score for severity of COVID-19 using human chromosomal-scale length variation.Creative Commons 'BY' version 4.0 license
    • Article
    • Peer Reviewed
    UC Irvine Previously Published Works (2020)

    Introduction

    The course of COVID-19 varies from asymptomatic to severe (acute respiratory distress, cytokine storms, and death) in patients. The basis for this range in symptoms is unknown. One possibility is that genetic variation is responsible for the highly variable response to infection. We evaluated how well a genetic risk score based on chromosome-scale length variation and machine learning classification algorithms could predict severity of response to SARS-CoV-2 infection.

    Methods

    We compared 981 patients from the UK Biobank dataset who had a severe reaction to SARS-COV-2 infection before 27 April 2020 to a similar number of age matched patients drawn for the general UK Biobank population. For each patient, we built a profile of 88 numbers characterizing the chromosome-scale length variability of their germ line DNA. Each number represented one quarter of the 22 autosomes. We used the machine learning algorithm XGBoost to build a classifier that could predict whether a person would have a severe reaction to Covid-19 based only on their 88-number classification.

    Results

    We found that the XGBoost classifier could differentiate between the two classes at a significant level p = 2 · 10 as measured against a randomized control and p = 3 · 10 measured against the expected value of a random guessing algorithm (AUC=0.5). However, we found that the AUC of the classifier was only 0.51, too low for a clinically useful test.

    Conclusion

      Cover page: Human chromosomal-scale length variation and severity of COVID-19 infection using the UK Biobank dataset
      • Article
      • Peer Reviewed
      UC Irvine Previously Published Works (2021)

      Introduction

      Twin studies indicate that a substantial fraction of ovarian cancers should be predictable from genetic testing. Genetic risk scores can stratify women into different classes of risk. Higher risk women can be treated or screened for ovarian cancer, which should reduce ovarian cancer death rates. However, current ovarian cancer genetic risk scores do not work that well. We developed a genetic risk score based on variations in the length of chromosomes.

      Methods

      We evaluated this genetic risk score using data collected by The Cancer Genome Atlas. We synthesized a dataset of 414 women who had ovarian serous carcinoma and 4225 women who had no form of ovarian cancer. We characterized each woman by 22 numbers, representing the length of each chromosome in their germ line DNA. We used a gradient boosting machine to build a classifier that can predict whether a woman had been diagnosed with ovarian cancer.

      Results

      The genetic risk score based on chromosomal-scale length variation could stratify women such that the highest 20% had a 160x risk (95% confidence interval 50x-450x) compared to the lowest 20%. The genetic risk score we developed had an area under the curve of the receiver operating characteristic curve of 0.88 (95% confidence interval 0.86-0.91).

      Conclusion

      A genetic risk score based on chromosomal-scale length variation of germ line DNA provides an effective means of predicting whether or not a woman will develop ovarian cancer.
        • Article
        • Peer Reviewed
        UC Irvine Previously Published Works (2020)

        Introduction

        The course of COVID-19 varies from asymptomatic to severe in patients. The basis for this range in symptoms is unknown. One possibility is that genetic variation is partly responsible for the highly variable response. We evaluated how well a genetic risk score based on chromosomal-scale length variation and machine learning classification algorithms could predict severity of response to SARS-CoV-2 infection.

        Methods

        We compared 981 patients from the UK Biobank dataset who had a severe reaction to SARS-CoV-2 infection before 27 April 2020 to a similar number of age-matched patients drawn for the general UK Biobank population. For each patient, we built a profile of 88 numbers characterizing the chromosomal-scale length variability of their germ line DNA. Each number represented one quarter of the 22 autosomes. We used the machine learning algorithm XGBoost to build a classifier that could predict whether a person would have a severe reaction to COVID-19 based only on their 88-number classification.

        Results

        We found that the XGBoost classifier could differentiate between the two classes at a significant level (p = 2 · 10-11) as measured against a randomized control and (p = 3 · 10-14) as measured against the expected value of a random guessing algorithm (AUC = 0.5). However, we found that the AUC of the classifier was only 0.51, too low for a clinically useful test.

        Conclusion

        Genetics play a role in the severity of COVID-19, but we cannot yet develop a useful genetic test to predict severity.
          Cover page: Evaluation of a genetic risk score for severity of COVID-19 using human chromosomal-scale length variation
          • Thesis
          • Peer Reviewed
          UC Berkeley Electronic Theses and Dissertations (2011)

          Robust SRAM design is one of the key challenges of process technology scaling. The steady pace of process technology scaling allows doubling memory array sizes, which requires thorough characterization of the impact of sources of process variability on SRAM operation. SRAM arrays are traditionally designed using static noise margins which are known to be optimistic in writeability and pessimistic in read stability. This work presents techniques for characterizing SRAM using dynamic stability metrics, which better represent actual SRAM operating conditions. Quantitative relationships between static and dynamic stability metrics are explored through statistical circuit simulations. Nano-scale SRAM design is traditionally complicated by sources of variability related to physical variability in the structure of the transistors, such as random dopant distribution. This work identifies temporal sources of variability in transistor intrinsic parameters, caused by random telegraph signaling (RTS) noise, which is directly correlated with fluctuation in SRAM performance. A large-scale dynamic stability characterization architecture is introduced and implemented in an early commercial low-power 45 nm CMOS process. This is used to experimentally verify the expected correlations between static and dynamic stability metrics. Outliers of up to 100X which are not correlated between static and dynamic stability metrics were observed and identified to be due to enhanced sensitivity of dynamic stability metrics to variability. Measurement techniques for characterizing temporal sources of variability caused by RTS noise, with particular emphasis on the large-signal bias change response typically encountered in SRAM operation, are used to collect large-scale statistics and to estimate the statistical impact of RTS noise on large SRAM arrays.

          An importance sampling algorithm adapted for dynamic stability metrics is developed in this work. This algorithm is used to estimate improvements in SRAM robustness expected from new process technology options such as FDSOI, different bitcell designs such as the 8T-SRAM, as well as several read-assist and write-assist techniques. An optimization framework enabled by this importance sampling algorithm is used to design SRAM arrays with maximum array efficiency through joint-optimization between process technology, bitcell design, and array organization. In conclusion, this dissertation identifies important sources of variability in nano-scale SRAM and also introduces the relevant optimization tools for performing variability-aware SRAM design.

            Cover page: Nanoscale SRAM Variability and Optimization
            • Thesis
            • Peer Reviewed
            UC Irvine Electronic Theses and Dissertations (2021)

            Next generation sequencing has created large databases of human genomic information. Utilizing this information to understand disease and genetic risks is a large engineering task. Previous studies have focused primarily on single nucleotide polymorphisms (SNPs) in assessing patient risk for diseases such as cancers and other diseases such as Schizophrenia. These SNP panels do not consider epistatic interactions in the human genome.Chromosomal scale-length variation (CSLV) is a promising approach for assessing genetic risk scores. CSLV evaluates copy number variations (CNVs), condensing genomic information into a smaller number of parameters. Reducing parameters allows the use of machine learning without the need for millions of patients’ data. Machine learning can consider epistatic interactions that might be missed by conventional genome wide association studies (GWAS). Utilizing machine learning classification algorithms, we assessed prediction of diseases such as ovarian cancer and schizophrenia using CSLV as the sole features for prediction. We have demonstrated the viability of this method in assessing germline inheritance of complex human diseases in The Cancer Genome Atlas (TCGA) and UK Biobank. We tested 33 different types of cancer from TCGA’s 11,000 patients. Glioblastoma multiforme (AUC = 0.87), ovarian cancer (AUC = 0.89), colon adenocarcinoma (AUC = 0.82), and breast invasive carcinoma (AUC = 0.75) could be distinguished greater than chance from cancers. These results were replicated the UK Biobank using 88 numbers computed from the 22 autosomes for 1,534 women with breast cancer and a control population of 4,391 women without breast cancer and found a classifier with an AUC of 0.83. 1,129 people from the UK Biobank have a diagnosis of schizophrenia. Using a randomized set of 1,129 individuals without schizophrenia we created 150 models using 92 number CSLVs as our feature set. The results provided an average AUC of 0.545 (95% CI 0.539-0.550). Our results indicate that CSLV data can provide an effective genetic risk score for schizophrenia. In conclusion, CSLV is a promising and novel way to utilize large scale human genetic information in the prediction off complex. Continued improvement of this technique can dramatically improve individualized patient care and can aid physicians in earlier diagnosis.

              Cover page: Chromosomal scale length variations as a genetic risk score for predicting complex human diseases in large scale genomic datasetsCreative Commons 'BY' version 4.0 license
              • Article
              • Peer Reviewed
              UC Irvine Previously Published Works (2021)
              Studies indicate that schizophrenia has a genetic component, however it cannot be isolated to a single gene. We aimed to determine how well one could predict that a person will develop schizophrenia based on their germ line genetics. We compared 1129 people from the UK Biobank dataset who had a diagnosis of schizophrenia to an equal number of age matched people drawn from the general UK Biobank population. For each person, we constructed a profile consisting of numbers. Each number characterized the length of segments of chromosomes. We tested several machine learning algorithms to determine which was most effective in predicting schizophrenia and if any improvement in prediction occurs by breaking the chromosomes into smaller chunks. We found that the stacked ensemble, performed best with an area under the receiver operating characteristic curve (AUC) of 0.545 (95% CI 0.539-0.550). We noted an increase in the AUC by breaking the chromosomes into smaller chunks for analysis. Using SHAP values, we identified the X chromosome as the most important contributor to the predictive model. We conclude that germ line chromosomal scale length variation data could provide an effective genetic risk score for schizophrenia which performs better than chance.
                Cover page: A genetic risk score using human chromosomal-scale length variation can predict schizophrenia
                • Article
                UCSD Molecule Pages, Volume 1, Issue 2 (2012)
                Cdc7 (Cell division cycle 7), also known as Hsk1 in fission yeast, is an important serine/threonine kinase, whose sequence is conserved from yeasts to mammals. The kinase activity of Cdc7 is regulated during the cell cycle by an activation subunit Dbf4 (also known as Dfp1/Him1 in fission yeast and ASK in mammals,) via heterodimer formation between the two. Cdc7 was first identified in budding yeast as a temperature-sensitive mutant (cdc7 ts ) defective in cell cycle progression. The budding yeast cdc7 ts cells arrest immediately before the onset of S phase at the non-permissive temperature, but resume growth and complete S phase in the absence of ongoing protein synthesis upon return to the permissive temperature. Cdc7 plays a conserved, pivotal role in triggering origin firing through phosphorylation of MCM (mini-chromosome maintenance) proteins. It facilitates the loading of Cdc45 and other replisome factors onto the pre-replicative complex, to generate active replication forks. In addition, it regulates other chromosomal transactions including DNA damage checkpoint, meiotic recombination, bypass DNA synthesis and histone functions. Selective induction of apoptosis in human cancer cells, but not in normal fibroblasts, after Cdc7 inhibition has provoked the effort in the development of Cdc7 inhibitors as potential anti-cancer drugs. Indeed, studies to date have suggested human Cdc7 as a new promising target in cancer therapy.
                • 1 supplemental PDF
                Cover page: Cdc7l1Creative Commons 'BY' version 3.0 license
                • Article
                • Peer Reviewed
                UC Irvine Previously Published Works (2020)

                Background

                The selective occurrence of hepatotoxicity observed with use of pazopanib may be attributed to its high level of plasma protein binding and low hepatic extraction ratio. The primary objective was to investigate changes in free drug concentration amongst patients with varying albumin concentrations.

                Methods

                A HPLC-MS/MS method using C18 column (4.6 × 150 mm, 5 μm) with ESI source in positive mode had been developed and validated for the quantitative determination of free pazaopanib concentration in human plasma. Prior to sample preparation, patient samples were subjected to 6-hour equilibrium dialysis with molecular weight cut-off set at 8000 Da.

                Results

                The calibration curves were linear over the range of 5-1000 ng/mL, with a lower limit of quantification of 5 ng/mL. The intra-day and inter-day precisions and accuracies were all within ± 15 %, at 3 different quality controls. Higher median fraction unbound of pazopanib were observed in patients (n = 17) with lower than normal albumin concentrations.

                Conclusion

                With the developed assay, monitoring of plasma free concentrations may be evaluated as an indicator of pazopanib exposure in patients.
                  Cover page: HPLC-MS/MS coupled with equilibrium dialysis method for quantification of free drug concentration of pazopanib in plasma
                  • Article
                  • Peer Reviewed
                  UC Berkeley Previously Published Works (2019)
                  This paper introduces an efficient algorithm for computing the best approximation of a given matrix onto the intersection of linear equalities, inequalities and the doubly nonnegative cone (the cone of all positive semidefinite matrices whose elements are nonnegative). In contrast to directly applying the block coordinate descent type methods, we propose an inexact accelerated (two-)block coordinate descent algorithm to tackle the four-block unconstrained nonsmooth dual program. The proposed algorithm hinges on the efficient semismooth Newton method to solve the subproblems, which have no closed form solutions since the original four blocks are merged into two larger blocks. The $O(1/k^2)$ iteration complexity of the proposed algorithm is established. Extensive numerical results over various large scale semidefinite programming instances from relaxations of combinatorial problems demonstrate the effectiveness of the proposed algorithm.
                    Cover page: Computing the Best Approximation over the Intersection of a Polyhedral Set and the Doubly Nonnegative ConeCreative Commons 'BY' version 4.0 license
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