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As per available reports about 7 relevant Journals and more than 10 upcoming international Conferences are presently dedicated exclusively to Biostatistics and about 250 articles have been published on Biostatistics.
Biostatistics (or biometry) is the application of statistics to a wide range of topics in biology. The science of biostatistics encompasses the design of biological experiments, especially in medicine, pharmacy, agriculture and fishery; the collection, summarization, and analysis of data from those experiments; and the interpretation of, and inference from, the results. A major branch of this is medical biostatistics which is exclusively concerned with medicine and health. The advent of modern computer technology and relatively cheap computing resources has enabled computer-intensive biostatistical methods like bootstrapping and resampling methods, Modern data analysis, clinical research etc.
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Scope and Importance:
Furthermore new biomedical technologies like microarrays, next generation sequencers (for genomics) and mass spectrometry (for proteomics) generate enormous amounts of (redundant) data that can only be analyzed with biostatistical methods. For example, a microarray can measure all the genes of the human genome simultaneously, but only a fraction of them will be differentially expressed in diseased vs. non-diseased states. One might encounter the problem of multicolinearity: Due to high intercorrelation between the predictors (in this case say gene), the information of one predictor might be contained in another one. It could be that only 5% of the predictors are responsible for 90% of the variability of the response. In such a case, one would apply the biostatistical technique of dimension reduction (for example via principal component analysis). Classical statistical techniques like linear or logistic regression and linear discriminant analysis do not work well for high dimensional data (i.e. when the number of observations n is smaller than the number of features or predictors p: n < p). As a matter of fact, one can get quite high R2-values despite very low predictive power of the statistical model. These classical statistical techniques (esp. least squares linear regression) were developed for low dimensional data (i.e. where the number of observations n is much larger than the number of predictors p: n >> p). In cases of high dimensionality, one should always consider an independent validation test set and the corresponding residual sum of squares (RSS) and R2 of the validation test set, not those of the training set. In recent times, random forests have gained popularity. This technique, invented by the statistician Leo Breiman, generates a lot of decision trees randomly and uses them for classification (In classification the response is on a nominal or ordinal scale, as opposed to regression where the response is on a ratio scale). Decision trees have of course the advantage that you can draw them and interpret them (even with a very basic understanding of mathematics and statistics). Random Forrests have thus been used for clinical decision support systems. Gene Set Enrichment Analysis (GSEA) is a new method for analyzing biological high throughput experiments. With this method, one does not consider the perturbation of single genes but of whole (functionally related) gene sets. These gene sets might be known biochemical pathways or otherwise functionally related genes. The advantage of this approach is that it is more robust: It is more likely that a single gene is found to be falsely perturbed than it is that a whole pathway is falsely perturbed. Furthermore, one can integrate the accumulated knowledge about biochemical pathways (like the JAK-STAT signaling pathway) using this approach.
Use of biostatistics in biotechnology industry is currently on the rise, with over 27,600 biostatistics professional working in biotech sector in US alone. Industry experts predict, that based on the current rate of integration of technology and data mining in current biotechnology industry, by 2022, an unprecedented rise of 27% in employment of biostatisticians in biotech sector alone can be observed. Furthermore, with continued growth of biotechnology industry, the global biotech revenue is expected to increase from $90 billion in 2011 to $200 billion in 2016. Also, as the field of biostatistics is always under development and improvement, biotech companies are spending a lot of resources and capital in R&D, for example Swiss Biotech Giant, Roche, spent $10, 187 million in 2012 and in subsequent years, maintained a constant increase rate of 10% in its annual R&D funds.
Relevant Associations and Societies
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This page was last updated on January 20, 2022