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The Brave New World of Genomics
Published in Carlos Simón, Carmen Rubio, Handbook of Genetic Diagnostic Technologies in Reproductive Medicine, 2022
Sandra García Herrero, Blanca Simon Frances, Cristian Perez-Garcia, Javier Garcia-Planells
Due to the general idea of bioinformatic tools being seen as black boxes for clinicians focused on the diagnostic divisions, during the past few years there has been an enormous effort to share, not only new software and methods to the ecosystem, but also technical knowledge through courses and tutorials, with the final objective of teaching the basics and the advanced statistical and computational techniques to the non-bioinformatician staff. Just as equipment is connected in a factory, concatenating bioinformatic tools gives birth to a bioinformatic pipeline, and, thanks to the open-source way of thinking of the community, everyone can reach that knowledge and skills at no cost.
Gene Expression Profiling to Detect New Treatment Targets in Leukemia and Lymphoma: A Future Perspective
Published in Gertjan J. L. Kaspers, Bertrand Coiffier, Michael C. Heinrich, Elihu Estey, Innovative Leukemia and Lymphoma Therapy, 2019
Torsten Haferlach, Wolfgang Kern, Alexander Kohlmann
Increasingly, also bioinformaticians are interested in developing analytical tools that help scientists interpret experimental data especially in the context of pathways and biological systems. These analytical tools have broad application throughout research and development, from validating targets by uncovering disease-related pathways to predicting pathways perturbed by therapeutic compounds. As one example in Ingenuity, a broad genome-wide coverage of over 25,900 mammalian genes (11,100 human, 5500 rat, and 9300 mouse) can be found and millions of pathway interactions extracted from literature are managed interactively and web based.
Biostatistics
Published in Arkadiy Pitman, Oleksandr Sverdlov, L. Bruce Pearce, Mathematical and Statistical Skills in the Biopharmaceutical Industry, 2019
Arkadiy Pitman, Oleksandr Sverdlov, L. Bruce Pearce
The first step for anyone who starts working on a clinical trial/project would be to do some kind of “stakeholder mapping”—identifying key individuals with whom one should have business interaction and communication. For a biostatistician, this map will likely be multidimensional and include: Clinical trial/project team members: clinical trial leader, medical director, PK scientist, regulatory scientist, project manager, etc.Technical team members (within the company): statistical programmer, data manager, pharmacometrician, bioinformatician, etc.Outsourcing team members: CRO statistician, CRO programmer, etc.Line function management: manager, one level over manager, and direct reports (if any).
An overview of technologies for MS-based proteomics-centric multi-omics
Published in Expert Review of Proteomics, 2022
Andrew T. Rajczewski, Pratik D. Jagtap, Timothy J. Griffin
Many software applications capable of MS-based proteomics-centered multi-omics analysis were developed as a stand-alone script or bundled package in R, Python, or C++ which are run through the command line or through an interpreter program. While this is not a problem for the skilled bioinformatician, many researchers who are less computationally savvy are hindered by these software implementations. As such, many multi-omic software suites incorporate point-and-click graphical user interfaces (GUIs) that are user-friendly and accessible to a wider range of researchers (Table 2). While there are some commercial options, such as Qiagen’s Ingenuity Pathway Analysis (IPA) [96], there are a myriad of open-source options that are as powerful and simple-to-use as they are affordable.
How can bioinformatics contribute to the routine application of personalized precision medicine?
Published in Expert Review of Precision Medicine and Drug Development, 2020
Carlos Carretero-Puche, Santiago García-Martín, Rocío García-Carbonero, Gonzalo Gómez-López, Fátima Al-Shahrour
The clinical bioinformaticians can empower the healthcare providers and play major independent roles. More specifically, clinical bioinformaticians tasks will cover the needs associated with the implementation of data-based medicine. These tasks include: i) to develop platforms for big data processing and maintain computational pipelines; ii) to perform data analysis on heterogeneous biomedical data sources; iii) to access and harness EHR information systematically; iv) smartly questioning the data to provide intuitive reports; v) to fluently communicate relevant findings to other health professionals to guide clinical decision-making. Carrying out such tasks requires trained experts such as IT specialists, software developers, data miners, AI professionals, big data scientists, and data interpreters (of course not in one single person).
The design and analysis of non-randomized studies: a case study of off-label use of hydroxychloroquine in the COVID-19 pandemic
Published in Expert Opinion on Investigational Drugs, 2021
There are several key developments and challenges that need to be addressed for non-randomized studies. First, we need better text-mining extraction methods and NLP algorithms to improve the data quality of non-randomized studies for EHR. Current methods still rely on human intervention to guarantee that the data is appropriate for the research question. Even though a fully automated method is not likely, these methods need improvement in the accuracy of extracting relevant data that rely less on human intervention. Computer scientists, bioinformaticians, and clinicians will need to work together and combine their expertise to improve and develop new methods.