Ding J.,Singapore Eye Research Institute |
Ding J.,Quantitative Medicine |
Ding J.,National University of Singapore |
Wong T.Y.,Singapore Eye Research Institute |
Wong T.Y.,National University of Singapore
Current Diabetes Reports | Year: 2012
With increasing global prevalence of diabetes, diabetic retinopathy (DR) is set to be the principle cause of vision impairment in many countries. DR affects a third of people with diabetes and the prevalence increases with duration of diabetes, hyperglycemia, and hypertension-the major risk factors for the onset and progression of DR. There are now increasing data on the epidemiology of diabetic macular edema (DME), an advanced complication of DR, with studies suggesting DME may affect up to 7 % of people with diabetes. The risk factors for DME are largely similar to DR, but dyslipidemia appears to play a more significant role. Early detection of DR and DME through screening programs and appropriate referral for therapy is important to preserve vision in individuals with diabetes. Future research is necessary to better understand the potential role of other risk factors such as apolipoproteins and genetic predisposition to shape public health programs. © Springer Science+Business Media, LLC 2012.
Haaland B.,Quantitative Medicine |
Haaland B.,National University of Singapore |
Tan P.S.,National University of Singapore |
De Castro Jr. G.,Clinical Oncology |
And 2 more authors.
Journal of Thoracic Oncology | Year: 2014
INTRODUCTION:: Tyrosine kinase inhibitors gefitinib, erlotinib, and afatinib have been compared with chemotherapy as first-line therapies for patients with advanced non-small-cell lung cancer harboring epidermal growth factor receptor-activating mutations. This meta-analysis compares gefitinib, erlotinib, afatinib, and chemotherapy. METHODS:: Literature search was performed using relevant keywords. Direct and indirect meta-estimates were generated using log-linear mixed-effects models, with random effects for study. Study-to-study heterogeneity was summarized using I statistics and predictive intervals (PIs). RESULTS:: Literature search yielded eight randomized phase 3 clinical trials comparing gefitinib, erlotinib, or afatinib with chemotherapy as first-line therapy in patients with advanced non-small-cell lung cancer during the last 5 years. Hazard ratio meta-estimates for progression-free survival were for gefitinib versus chemotherapy 0.44 (95% confidence interval [CI] 0.31-0.63; 95% PI, 0.22-0.88), erlotinib versus chemotherapy 0.25 (95% CI, 0.15-0.42; 95% PI, 0.11-0.55), afatinib versus chemotherapy 0.44 (95% CI, 0.26-0.75; 95% PI, 0.20-0.98), erlotinib versus gefitinib 0.57 (95% CI, 0.30-1.08; 95% PI, 0.24-1.36), afatinib versus gefitinib 1.01 (95% CI, 0.53-1.92; 95% PI, 0.41-2.42), and erlotinib versus afatinib 0.56 (95% CI, 0.27-1.18; 95% PI, 0.22-1.46). Results for overall response rate and disease control rate were similar. There was no evidence that gefitinib, erlotinib, or afatinib improved overall survival compared with chemotherapy. CONCLUSION:: Gefitinib, erlotinib, and afatinib out-performed chemotherapy in terms of progression-free survival, overall response rate, and disease control rate. Differences among gefitinib, erlotinib, and afatinib were not statistically significant. Copyright © 2014 by the International Association for the Study of Lung Cancer.
Wong W.L.,Singapore Eye Research Institute |
Wong W.L.,National University of Singapore |
Su X.,Singapore Eye Research Institute |
Su X.,National University of Singapore |
And 9 more authors.
The Lancet Global Health | Year: 2014
Background: Numerous population-based studies of age-related macular degeneration have been reported around the world, with the results of some studies suggesting racial or ethnic differences in disease prevalence. Integrating these resources to provide summarised data to establish worldwide prevalence and to project the number of people with age-related macular degeneration from 2020 to 2040 would be a useful guide for global strategies. Methods: We did a systematic literature review to identify all population-based studies of age-related macular degeneration published before May, 2013. Only studies using retinal photographs and standardised grading classifications (the Wisconsin age-related maculopathy grading system, the international classification for age-related macular degeneration, or the Rotterdam staging system) were included. Hierarchical Bayesian approaches were used to estimate the pooled prevalence, the 95% credible intervals (CrI), and to examine the difference in prevalence by ethnicity (European, African, Hispanic, Asian) and region (Africa, Asia, Europe, Latin America and the Caribbean, North America, and Oceania). UN World Population Prospects were used to project the number of people affected in 2014 and 2040. Bayes factor was calculated as a measure of statistical evidence, with a score above three indicating substantial evidence. Findings: Analysis of 129664 individuals (aged 30-97 years), with 12727 cases from 39 studies, showed the pooled prevalence (mapped to an age range of 45-85 years) of early, late, and any age-related macular degeneration to be 8·01% (95% CrI 3·98-15·49), 0·37% (0·18-0·77), and 8·69% (4·26-17·40), respectively. We found a higher prevalence of early and any age-related macular degeneration in Europeans than in Asians (early: 11·2% vs 6·8%, Bayes factor 3·9; any: 12·3% vs 7·4%, Bayes factor 4·3), and early, late, and any age-related macular degeneration to be more prevalent in Europeans than in Africans (early: 11·2% vs 7·1%, Bayes factor 12·2; late: 0·5% vs 0·3%, 3·7; any: 12·3% vs 7·5%, 31·3). There was no difference in prevalence between Asians and Africans (all Bayes factors <1). Europeans had a higher prevalence of geographic atrophy subtype (1·11%, 95% CrI 0·53-2·08) than Africans (0·14%, 0·04-0·45), Asians (0·21%, 0·04-0·87), and Hispanics (0·16%, 0·05-0·46). Between geographical regions, cases of early and any age-related macular degeneration were less prevalent in Asia than in Europe and North America (early: 6·3% vs 14.3% and 12·8% [Bayes factor 2·3 and 7·6]; any: 6·9% vs 18·3% and 14·3% [3·0 and 3·8]). No significant gender effect was noted in prevalence (Bayes factor <1·0). The projected number of people with age-related macular degeneration in 2020 is 196 million (95% CrI 140-261), increasing to 288 million in 2040 (205-399). Interpretation: These estimates indicate the substantial global burden of age-related macular degeneration. Summarised data provide information for understanding the effect of the condition and provide data towards designing eye-care strategies and health services around the world. Funding: National Medical Research Council, Singapore. © 2014 Wong et al.
Moore J.H.,Quantitative Medicine
Methods in Molecular Biology | Year: 2015
Here we introduce the multifactor dimensionality reduction (MDR) methodology and software package for detecting and characterizing epistasis in genetic association studies. We provide a general overview of the method and then highlight some of the key functions of the open-source MDR software package that is freely distributed. We end with a few examples of published studies of complex human diseases that have used MDR. © Springer Science+Business Media New York 2015.
Bush W.S.,Vanderbilt University |
Moore J.H.,Quantitative Medicine
PLoS Computational Biology | Year: 2012
Genome-wide association studies (GWAS) have evolved over the last ten years into a powerful tool for investigating the genetic architecture of human disease. In this work, we review the key concepts underlying GWAS, including the architecture of common diseases, the structure of common human genetic variation, technologies for capturing genetic information, study designs, and the statistical methods used for data analysis. We also look forward to the future beyond GWAS. © 2012 Bush, Moore.
Demidenko E.,Quantitative Medicine
BioData Mining | Year: 2015
Background: We develop a new concept that reflects how genes are connected based on microarray data using the coefficient of determination (the squared Pearson correlation coefficient). Our gene rank combines a priori knowledge about gene connectivity, say, from the Gene Ontology (GO) database, and the microarray expression data at hand, called the microarray enriched gene rank, or simply gene rank (GR). GR, similarly to Google PageRank, is defined in a recursive fashion and is computed as the left maximum eigenvector of a stochastic matrix derived from microarray expression data. An efficient algorithm is devised that allows computation of GR for 50 thousand genes with 500 samples within minutes on a personal computer using the public domain statistical package R. Results: Computation of GR is illustrated with several microarray data sets. In particular, we apply GR (1) to answer whether bad genes are more connected than good genes in relation with cancer patient survival, (2) to associate gene connectivity with cluster/subtypes in ovarian cancer tumors, and to determine whether gene connectivity changes (3) from organ to organ within the same organism and (4) between organisms. Conclusions: We have shown by examples that findings based on GR confirm biological expectations. GR may be used for hypothesis generation on gene pathways. It may be used for a homogeneous sample or for comparison of gene connectivity among cases and controls, or in longitudinal setting. © 2015 Demidenko; licensee BioMed Central.
Bechard L.J.,Childrens Hospital Boston |
Parrott J.S.,Health Science University |
Parrott J.S.,Quantitative Medicine |
Mehta N.M.,Childrens Hospital Boston
Journal of Pediatrics | Year: 2012
Objective: To examine the influence of protein and energy intakes on protein balance in children receiving mechanical ventilation in the pediatric intensive care unit. Study design: We hypothesized that higher energy and protein intakes are correlated with positive protein balance. We performed a systematic literature search to identify studies reporting protein balance in children requiring mechanical ventilation. Factors contributing to protein balance, including protein and energy intake, age, illness severity, study design, and feeding routes, were analyzed using a qualitative approach. Results: Nine studies met the entry criteria and were included in the final analysis. Positive nitrogen balance was reported in 6 of the studies, with a wide range of associated energy and protein intakes. Measures of central tendency for daily energy and protein intakes were significantly correlated with positive protein balance. A minimum intake of 57 kcal/kg/day and 1.5 g protein/kg/day were required to achieve positive protein balance. Conclusion: We found a correlation between higher energy and protein intakes and achievement of positive protein balance in children receiving mechanical ventilation in the pediatric intensive care unit. However, there is a paucity of interventional studies, and a variety of protocols have been used to determine nitrogen balance. Larger clinical trials with uniform methodology are needed to further examine the effect of energy and protein intake on protein balance, lean body mass, and clinical outcomes in children on mechanical ventilation. Copyright © 2012 Mosby Inc.
News Article | April 6, 2015
One Million Solutions in Health™ and Quantitative Medicine announced today a new opportunity for the Life Sciences industry. This unique opportunity will enable industry participants to evaluate an innovative, machine learning drug discovery platform which was conceived after decades of research at the Center for Computational Biology at Carnegie Mellon University. The Computational Research Engine™ (CoRE™) creates and leverages highly accurate, predictive models to accelerate the discovery of drugs, therapeutics and diagnostics. This machine learning drug discovery platform substantially improves pharmaceutical and biotechnology research and development productivity by efficiently directing experimentation. Fundamentally, CoRE™ recommends ‘what to do next’, thereby increasing the utility value of each experiment. This unique capability enables researchers to gather only the most essential information, at the lowest cost, and within the shortest timeframe. For scientists in academia, industry, consortia, government or those conducting research and development on behalf of patient foundations – this opportunity is unique. Through this partnership with One Million Solutions in Health, Quantitative Medicine is offering a limited number of organizations a valuable opportunity to assess – using their own data – CoRE’s ability to speed up the drug discovery process. “The Technology Evaluation Consortium™ program offered by One Million Solutions in Health has already facilitated a number of industry validations of CoRE™. These evaluations have conclusively demonstrated that CoRE™ enables a faster decision-making process and with much greater confidence,” commented Scott R. Bodine, Co-Founder and CEO of Quantitative Medicine. “To meet the needs of the broader research community, CoRE™ can simultaneously direct experimentation across all of the earlier phases of drug discovery and development – and does this very quickly and efficiently.” The opportunity to accelerate drug discovery is now available to any organization recognizing the benefits of efficiently and concurrently directing experimentation in preclinical drug discovery. While predictive modeling is not new, when a large number of potential compounds need to be tested, such as in drug discovery, active machine learning can play a crucial and central role in prioritizing the experimental work flow. "The goal is not to replace experimentation with computation, but rather to use sophisticated computation to effectively guide experimentation," explained Dr. Joshua D. Kangas, Co-Founder and Chief Scientific Officer of Quantitative Medicine. By using this approach, experimentation is directed to cost-effectively explore very large experimental spaces, predicting the effects of millions of putative drug compounds on thousands of diverse targets. In twenty-one proof-of-value studies performed for large pharmaceutical companies, Quantitative Medicine has demonstrated CoRE™ can capture cost reductions of 50% to 90%, while reducing time to accomplish those research objectives by a minimum of 50%. Industry organizations can now participate in evaluating how CoRE™ can pick up the pace of discovery, allowing scientists to accomplish their research goals in the least amount of time and at the lowest cost. As a result of One Million Solution in Health’s oversight and management of multiple pre-competitive collaborations regarding the CoRE™ machine learning drug discovery platform, Quantitative Medicine is entering into contract negotiations with several major pharmaceutical companies to help direct their drug discovery and development campaigns. As one senior-level representative from one of those companies explained, “The interest, from our point-of-view, is how much better CoRE™ can make predictions. Solving our central problem of drug discovery better and faster is very exciting. Quantitative Medicine has certainly piqued our interest.” A senior representative at a second company added, “Quantitative Medicine ‘nailed it.’ This active machine learning drug discovery platform presents a very forward-thinking paradigm for selecting compounds and assays, including expensive in vivo toxicity screens. CoRE™ enables evidence-based decision making about how best to extend into a chemical space, identifying those assays that are the most important, and directing experimentation to yield the most accurate predictive information. We envision capturing enormous in-house cost savings and accelerating time to market.” CoRE™ can further capture value for clients by leveraging historical data from prior campaigns in a novel way, which has been shown to yield significant improvements in predictive accuracy. Some of those involved in these pilot studies have shared their findings in this webinar. CoRE™ is therapeutic agnostic and a natural fit wherever a company has limited resources and is trying to prioritize the best path forward. Some obvious applications are early screening and lead series optimization. “We are very excited about this innovation and its enormous potential to help more organizations. Consequently, we look forward to having a number of groups step forward to participate in this opportunity to evaluate the CoRE™ drug discovery platform from Quantitative Medicine,” said Dawn Van Dam, President & CEO of One Million Solutions in Health. About One Million Solutions in Health The goal of One Million Solutions in Health™ is to shape health care by sharing solutions. By facilitating efforts to ensure organizations can Connect, Learn + Share, Innovate and Collaborate, our vision is to improve health care delivery, accelerate life sciences research and share patient and consumer-focused ideas and solutions. One Million Solutions in Health manages the Technology Evaluation Consortium™ (TEC), a community of pharmaceutical and biotech professionals focused on the development of better and more cost-efficient technologies. The TEC brings together life science companies and industry vendors to evaluate and validate new technologies in a non-competitive environment. This collaborative model empowers technology providers and industry end-users to collectively assess a number of technologies in a cost-effective manner, producing a depth and breadth of results that no company can achieve alone. About Quantitative Medicine Quantitative Medicine is a computational biology company commercializing a machine learning drug discovery platform that creates and leverages highly accurate, predictive models accelerating the discovery of drugs, therapeutics and diagnostics, and spanning the spectrum of human, animal and plant diseases. The CoRE™ platform, validated by industry, substantially improves R&D productivity by directing experimentation and gathering only the most essential information to accomplish research goals in the least amount of time and the lowest cost.
News Article | March 30, 2015
This “Open Call for Innovation" aims to attract promising new Technology Innovators, Entrepreneurs, Established Companies and Scientists to present their science directly to relevant Subject Matter Experts (SMEs) in the biotech and pharmaceutical industries. Through this Drug Discovery Technology Evaluation Consortium™ (TEC), One Million Solutions in Health™ can enable the collaborative evaluation and rapid adoption of promising new technologies for use in life sciences research and development. The Technology Evaluation Consortium is an innovative, collaborative program run by One Million Solutions in Health (OMSiH), a non-profit organization, dedicated to improving and accelerating life sciences R&D and outcomes. This program has a proven track record benefiting innovators, biopharmaceutical companies and healthcare, with more than a decade of pre-competitive and non-competitive analysis of promising new technologies, products and services. “As a new initiative, this Open Call for Innovation will allow technology providers and scientific innovators to get in on the ground floor of this opportunity with the Technology Evaluation Consortium,” stated Dawn Van Dam, President and CEO of One Million Solutions in Health. “Our uniqueness is that we utilize a proven, disciplined and scientific process, where we work with Subject Matter Experts to evaluate and then validate new technological innovations in a collaborative manner.” One Million Solutions in Health has been charged with the responsibility of taking the Technology Evaluation Consortium to a new level, and attracting a wider selection of new technologies. Our goal is to help the creators of new science, and the industry, realize the many benefits of these scientific advances, in an accelerated manner. From the webinar held recently on this Open Call for Innovation (click here), individuals and organizations can learn more about the opportunity for selected Technology Innovators/Providers to benefit from participation in this successful program. The webinar also presents information from one technology innovator, Quantitative Medicine, who has already participated in this program. Quantitative Medicine describes how the Technology Evaluation Consortium helped shape their technology, increase their customer base and generate market exposure. The benefits realized by participating in the Technology Evaluation Consortium are diverse and far-reaching. Beneficial outcomes reported by past Technology Innovator participants have included: > Expert Feedback: Receiving direct feedback from knowledgeable Subject Matter Experts (SMEs), who can influence adoption and use of the technology, is priceless. > Credibility: Achieving credible third-party evaluation and validation of the technology makes it more attractive and less risky to the biopharmaceutical industry and/or to investors. > Collaboration: The collaborative program builds trust, is more robust, yields better outcomes, and lays a solid foundation for future cooperation. > Market Penetration & Growth: By working collaboratively, innovators can build a deep understanding of the technology among top pharmaceutical and biotech companies. > Brand Equity: Brand knowledge in the minds of key industry players increases the strategic and financial value of the brand and, consequently, increases the value of the technology or business. “This is a significant diversion from how things have been done in the past. By bringing the industry together to ‘get on the same page’, the opportunity for more profound progress is possible,” declared Dawn Van Dam. “The goal is to be a disruptive force in the biopharmaceutical industry -- so we can all bring better solutions to patients -- with speed!” To apply for this opportunity, a Letter of Intent (LOI) for the Open Call for Innovation can be found here. For those chosen to proceed to the next step, One Million Solutions in Health will work with each Technology Provider or Scientific Innovator to help prepare the submission and presentation to the industry. Solutions are accepted in all areas which support the drug discovery and development process. To submit an application, forward it to TECprojects(at)onemillionsolutionsinhealth(dot)org OR input it at this link: https://www.surveymonkey.com/s/PV6ZKLM Contact One Million Solutions in Health today to find out how to become involved in this initiative to transform the evaluation, validation and adoption of new science and new technologies for the healthcare and life science industries. About One Million Solutions in Health Shaping Health Care by Sharing Solutions™ To transform the face of healthcare and life sciences around the world, One Million Solutions in Health™ (OMSiH) is a global movement working to stimulate ideas, innovation and solutions by connecting people and sharing high-value, innovative science. Inspired by a mutual conviction that all people around the globe deserve better health, we have crafted a number of platforms and programs that leverage collective knowledge, and the willingness to collaborate, for the purpose of accelerating the adoption of solutions that improve our health and well-being. Through a disciplined, scientific process, we work with Subject Matter Experts to evaluate and then validate new technological innovations. Mission: To Connect, Learn + Share, Innovate and Collaborate to improve health care delivery, accelerate life sciences research and share patient and consumer-focused ideas and solutions.