Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues

An internet-based intervention with brief nurse support to manage obesity in primary care (POWeR+): a pragmatic, parallel-group, randomised controlled trial

Background The obesity epidemic has major public health consequences. Expert dietetic and behavioural counselling with intensive follow-up is effective, but resource requirements severely restrict widespread implementation in primary care, where most patients are managed. We aimed to estimate the effectiveness and cost-effectiveness of an internet-based behavioural intervention (POWeR+) combined with brief practice nurse support in primary care. Methods We did this pragmatic, parallel-group, randomised controlled trial at 56 primary care practices in central and south England. Eligible adults aged 18 years or older with a BMI of 30 kg/m2 or more (or ≥28 kg/m2 with hypertension, hypercholesterolaemia, or diabetes) registered online with POWeR+—a 24 session, web-based, weight management intervention lasting 6 months. After registration, the website automatically randomly assigned patients (1:1:1), via computer-generated random numbers, to receive evidence-based dietetic advice to swap foods for similar, but healthier, choices and increase fruit and vegetable intake, in addition to 6 monthly nurse follow-up (control group); web-based intervention and face-to-face nurse support (POWeR+Face-to-face [POWeR+F]; up to seven nurse contacts over 6 months); or web-based intervention and remote nurse support (POWeR+Remote [POWeR+R]; up to five emails or brief phone calls over 6 months). Participants and investigators were masked to group allocation at the point of randomisation; masking of participants was not possible after randomisation. The primary outcome was weight loss averaged over 12 months. We did a secondary analysis of weight to measure maintenance of 5% weight loss at months 6 and 12. We modelled the cost-effectiveness of each intervention. We did analysis by intention to treat, with multiple imputation for missing data. This trial is registered as an International Standard Randomised Controlled Trial, number ISRCTN21244703. Findings Between Jan 30, 2013, and March 20, 2014, 818 participants were randomly assigned to the control group (n=279), the POWeR+F group (n=269), or the POWeR+R group (n=270). Weight loss averaged over 12 months was recorded in 666 (81%) participants. The control group lost almost 3 kg over 12 months (crude mean weight: baseline 104·38 kg [SD 21·11; n=279], 6 months 101·91 kg [19·35; n=136], 12 months 101·74 kg [19·57; n=227]). The primary imputed analysis showed that compared with the control group, patients in the POWeR+F group achieved an additional weight reduction of 1·5 kg (95% CI 0·6–2·4; p=0·001) averaged over 12 months, and patients in the POWeR+R group achieved an additional 1·3 kg (0·34–2·2; p=0·007). 21% of patients in the control group had maintained a clinically important 5% weight reduction at month 12, compared with 29% of patients in the POWeR+F group (risk ratio 1·56, 0·96–2·51; p=0·070) and 32% of patients in the POWeR+R group (1·82, 1·31–2·74; p=0·004). The incremental overall cost to the health service per kg weight lost with the POWeR+ interventions versus the control strategy was £18 (95% CI −129 to 195) for POWeR+F and –£25 (−268 to 157) for POWeR+R; the probability of being cost-effective at a threshold of £100 per kg lost was 88% and 98%, respectively. No adverse events were reported. Interpretation Weight loss can be maintained in some individuals by use of novel written material with occasional brief nurse follow-up. However, more people can maintain clinically important weight reductions with a web-based behavioural program and brief remote follow-up, with no increase in health service costs. Future research should assess the extent to which clinically important weight loss can be maintained beyond 1 year

Next Generation of Genotype Imputation Methods

In the past several years, we have witnessed numerous human genetic studies that have systematically evaluated the contribution of genetic polymorphisms to various complex diseases, and enabled the evolution of multiple treatment strategies, particularly pharmaceutical therapies. Genotype imputation has been a key step in such studies - increasing the power of gene mapping analyses, facilitating harmonization of results across studies, and accelerating fine-mapping efforts. Imputation requires access to a reference panel of densely sequenced genomes and is a computationally intensive process, even with modern high performance computing. Furthermore, reference panels often have data privacy issues that inhibit users from having direct access to the data. The goal of this dissertation is to design novel strategies to address these challenges for the next generation of imputation methods. In the first project, I describe our efforts to create a reference panel of ~32,000 individuals with ~40M variants by combining genetic information obtained across 20 whole genome sequencing studies (Haplotype Reference Consortium). In the second project, I describe a novel idea called ‘state space reduction’ that reduces computational requirements of genotype imputation by orders of magnitude without any loss of accuracy (minimac3). I also present a web-based platform for imputation that greatly improves user experience and productivity. In the third project, I extend the idea of state space reduction by implementing a more complex version of the strategy that produces additional cost savings (minimac4). In the fourth project, I introduce the idea of meta-imputation: a novel approach that integrates imputed data from multiple reference panels at overlapping sites without interfering in the imputation algorithm (MetaMinimac). In summary, the purpose of this dissertation research is to develop statistical methods and computational tools that will benefit other researchers in the next generation of human gene mapping studies. These imputation tools will detect rare variants with higher accuracy, consequently increasing the power of association studies.PHDBiostatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/138466/1/sayantan_1.pd

The Rate of Return to the High/Scope Perry Preschool Program

This paper estimates the rate of return to the High/Scope Perry Preschool Program, an early intervention program targeted toward disadvantaged African-American youth. Estimates of the rate of return to the Perry program are widely cited to support the claim of substantial economic benefits from preschool education programs. Previous studies of the rate of return to this program ignore the compromises that occurred in the randomization protocol. They do not report standard errors. The rates of return estimated in this paper account for these factors. We conduct an extensive analysis of sensitivity to alternative plausible assumptions. Estimated social rates of return generally fall between 7-10 percent, with most estimates substantially lower than those previously reported in the literature. However, returns are generally statistically significantly different from zero for both males and females and are above the historical return on equity. Estimated benefit-to-cost ratios support this conclusion.early childhood intervention programs, compromised randomization, Perry Preschool Program, standard errors, cost-benefit analysis, rate of return, deadweight costs