@Article{ijmebac1289,
AUTHOR = {Chakilam, Chaitran},
TITLE = {Leveraging AI, ML, and Generative Neural Models to Bridge Gaps in Genetic Therapy Access and Real-Time Resource Allocation},
JOURNAL = {International Journal of Mathematical, Engineering, Biological and Applied Computing},
VOLUME = {3},
YEAR = {2023},
NUMBER = {1},
PAGES = {27-43},
URL = {https://www.scipublications.com/journal/index.php/GJMCR/article/view/1289},
ISSN = {2832-5273},
DOI = {10.31586/gjmcr.2023.1289},
ABSTRACT = {This paper leverages gene and cell therapy research in diverse disorders ranging from monogenic to infectious diseases to cancer and emerging breakthroughs, where one can harness individual genes or a synthetic gene sequence designed based on a shared molecular pattern in infected cells to better fight various disorders [1]. A pivotal task is to predict the performances of candidate gene therapies to guide clinical translational research using methods such as retrospective bioinformatic analyses. Implementing them to a large-scale gene therapy database reveals that it is feasible to construct and apply well-performing interpretable, supervised learning models [2]. Preliminary evidence of machine learning approaches' statistical significance helps clinicians and biomedical researchers, market participants, and regulatory and economic experts derive relevant, practical applications, thereby enhancing the deployment of gene therapy and genomics to achieve positive, long-term growth for humanity while alleviating the ongoing worldwide economic burden precipitated by prolonged and recurring diseases. Deploying machine learning techniques to accelerate gene and cell therapy drug development and trials shall also mitigate the existing obstacle of limited patient access to emerging, transformative medical innovations such as gene therapy due to skyrocketing prices, which often herald gene therapy products as the world's most expensive medicines [3]. Moreover, in preventing patients from accessing effective, life-saving genetic medicines, there commonly exists a multidimensional access gap encompassing the availability, affordability, and quality or acceptability of these clinical treatments. The ensuing substantial gap has repeatedly been documented and mainly emanates from differential institutional and socio-political choices around resource allocation at international and domestic levels [4]. Particularly, it is also due to the stringent licensure and regulatory approval processes underpinned by insufficient evidence for novel safety and clinical efficacy profiles for genetic therapies in multiple micro-local diagnoses and subpopulations. We believe that a higher likelihood of gene therapy adoption shall result when the clinical evidence path contains adequate representation from the most diverse and relevant patient populations [5].},
}

%0 Journal Article
%A Chakilam, Chaitran
%D 2023
%J International Journal of Mathematical, Engineering, Biological and Applied Computing

%@ 2832-5273
%V 3
%N 1
%P 27-43

%T Leveraging AI, ML, and Generative Neural Models to Bridge Gaps in Genetic Therapy Access and Real-Time Resource Allocation
%M doi:10.31586/gjmcr.2023.1289
%U https://www.scipublications.com/journal/index.php/GJMCR/article/view/1289

TY  - JOUR
AU  - Chakilam, Chaitran
TI  - Leveraging AI, ML, and Generative Neural Models to Bridge Gaps in Genetic Therapy Access and Real-Time Resource Allocation
T2  - International Journal of Mathematical, Engineering, Biological and Applied Computing
PY  - 2023
VL  - 3
IS  - 1
SN  - 2832-5273
SP  - 27
EP  - 43
UR  - https://www.scipublications.com/journal/index.php/GJMCR/article/view/1289
AB  - This paper leverages gene and cell therapy research in diverse disorders ranging from monogenic to infectious diseases to cancer and emerging breakthroughs, where one can harness individual genes or a synthetic gene sequence designed based on a shared molecular pattern in infected cells to better fight various disorders [1]. A pivotal task is to predict the performances of candidate gene therapies to guide clinical translational research using methods such as retrospective bioinformatic analyses. Implementing them to a large-scale gene therapy database reveals that it is feasible to construct and apply well-performing interpretable, supervised learning models [2]. Preliminary evidence of machine learning approaches' statistical significance helps clinicians and biomedical researchers, market participants, and regulatory and economic experts derive relevant, practical applications, thereby enhancing the deployment of gene therapy and genomics to achieve positive, long-term growth for humanity while alleviating the ongoing worldwide economic burden precipitated by prolonged and recurring diseases. Deploying machine learning techniques to accelerate gene and cell therapy drug development and trials shall also mitigate the existing obstacle of limited patient access to emerging, transformative medical innovations such as gene therapy due to skyrocketing prices, which often herald gene therapy products as the world's most expensive medicines [3]. Moreover, in preventing patients from accessing effective, life-saving genetic medicines, there commonly exists a multidimensional access gap encompassing the availability, affordability, and quality or acceptability of these clinical treatments. The ensuing substantial gap has repeatedly been documented and mainly emanates from differential institutional and socio-political choices around resource allocation at international and domestic levels [4]. Particularly, it is also due to the stringent licensure and regulatory approval processes underpinned by insufficient evidence for novel safety and clinical efficacy profiles for genetic therapies in multiple micro-local diagnoses and subpopulations. We believe that a higher likelihood of gene therapy adoption shall result when the clinical evidence path contains adequate representation from the most diverse and relevant patient populations [5].
DO  - Leveraging AI, ML, and Generative Neural Models to Bridge Gaps in Genetic Therapy Access and Real-Time Resource Allocation
TI  - 10.31586/gjmcr.2023.1289
ER  -