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Jessica Haberer

Publications and source records attributed to Jessica Haberer.

5 recordsLinked to original sources
PubMed2007

The diagnosis and treatment of HIV-infected children in China: challenges and opportunities.

BACKGROUND: HIV-infected children in China have not been well studied. This national survey describes the demographic characteristics and the associated diagnostic and antiretroviral treatment (ART) efforts directed toward surviving HIV-infected children. METHODS: A cross-sectional study was conducted in the 6 provinces with the highest HIV prevalence: 4 former plasma donation (FPD) provinces and 2 intravenous drug use (IDU) provinces. A survey on demographics and treatment-related issues was distributed to the parents or guardians of all living HIV-infected children identified through the national case reporting system. Descriptive and bivariate analyses were performed on completed surveys. RESULTS: Six hundred ninety-two (62.4%) of the total 1108 surveys were returned, and 650 were eligible for analysis. The average age in FPD provinces (mean +/- SD: 8.1 +/- 3.2 years) was significantly older than in IDU provinces (mean +/- SD: 5.4 +/- 2.2 years; P < 0.001). The average lag time from the probable date of transmission to a diagnosis for patients with mother-to-child transmission (MTCT) was 6.7 +/- 3.1 years in the FPD provinces and 4.7 +/- 1.9 years in the IDU provinces (P < 0.001). On the basis of the CD4 cell count or World Health Organization staging, 29.8% (144 of 484) of children from all 6 provinces who were not on ART needed it. CONCLUSIONS: This first national pediatric survey indicates that the age and time required for diagnosis were greater in HIV-infected children from FPD provinces compared with those from IDU provinces. In addition, this survey highlights the prolonged delay in the diagnosis and initiation of ART for children in China. Aggressive efforts to identify HIV-positive pregnant women, scale up prevention of MTCT activities, and expand early diagnosis and treatment are urgently needed.

Analysis of Variance↗
PubMed2005

Accurate prediction of HIV-1 drug response from the reverse transcriptase and protease amino acid sequences using sparse models created by convex optimization.

MOTIVATION: Genotype-phenotype modeling problems are often overcomplete, or ill-posed, since the number of potential predictors-genes, proteins, mutations and their interactions-is large relative to the number of measured outcomes. Such datasets can still be used to train sparse parameter models that generalize accurately, by exerting a principle similar to Occam's Razor: When many possible theories can explain the observations, the most simple is most likely to be correct. We apply this philosophy to modeling the drug response of Type-1 Human Immunodeficiency Virus (HIV-1). Owing to the decreasing expense of genetic sequencing relative to in vitro phenotype testing, a statistical model that reliably predicts viral drug response from genetic data is an important tool in the selection of antiretroviral therapy (ART). The optimization techniques described will have application to many genotype-phenotype modeling problems for the purpose of enhancing clinical decisions. RESULTS: We describe two regression techniques for predicting viral phenotype in response to ART from genetic sequence data. Both techniques employ convex optimization for the continuous subset selection of a sparse set of model parameters. The first technique, the least absolute shrinkage and selection operator, uses the l(1) norm loss function to create a sparse linear model; the second, the support vector machine with radial basis kernel functions, uses the epsilon-insensitive loss function to create a sparse non-linear model. The techniques are applied to predict the response of the HIV-1 virus to 10 reverse transcriptase inhibitor and 7 protease inhibitor drugs. The genetic data are derived from the HIV coding sequences for the reverse transcriptase and protease enzymes. When tested by cross-validation with actual laboratory measurements, these models predict drug response phenotype more accurately than models previously discussed in the literature, and other canonical techniques described here. Key features of the methods that enable this performance are the tendency to generate simple models where many of the parameters are zero, and the convexity of the cost function, which assures that we can find model parameters to globally minimize the cost function for a particular training dataset. AVAILABILITY: Results, tables and figures are available at ftp://ftp.genesecurity.net. SUPPLEMENTARY INFORMATION: An Appendix to accompany this article is available at Bioinformatics online.

Amino Acid Sequence↗
PubMed2005

Use of the l1 norm for selection of sparse parameter sets that accurately predict drug response phenotype from viral genetic sequences.

We describe the use of the l1 norm for selection of a sparse set of model parameters that are used in the prediction of viral drug response, based on genetic sequence data of the Human Immunodeficiency Virus (HIV) reverse-transcriptase enzyme. We discuss the use of the l1 norm in the Least Absolute Selection and Shrinkage Operator (LASSO) regression model and the Support Vector Machine model. When tested by cross-validation with laboratory measurements, these models predict viral phenotype, or resistance, in response to Reverse-Transcriptase Inhibitors (RTIs) more accurately than other known models. The l1 norm is the most selective convex function, which sets a large proportion of the parameters to zero and also assures that a single optimal solution will be found, given a particular model formulation and training data set. A statistical model that reliably predicts viral drug response is an important tool in the selection of Anti-Retroviral Therapy. These techniques have general application to modeling phenotype from complex genetic data.

Algorithms↗