Elucidata

Joined September 2024

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How Spatial Transcriptomics Sheds Light on Tumor Microenvironments
Cancer remains one of the leading causes of morbidity and mortality worldwide. According to the World Health Organization (WHO), there were an estimated 20 million new cancer cases and almost 10 million cancer-related deaths in 2022. The future outlook is also worrisome, with the global burden of cancer expected to rise to 33 million projected incidences (a 65% increase) and 18 million projected deaths (an 80% increase) by 2050.
By Elucidataabout a year ago in 01
Impact of Advanced Data Management Solutions on Drug Development Timelines
The significant shift in drug discovery from a reductionist approach to a systemic view, has been largely driven by our ability to measure thousands, and even millions, of molecules across various modalities in biological samples. This evolution has been enabled by advancements in high-throughput technologies, such as next-generation sequencing and mass spectrometry. These advancements transformed experimental methods and transitioned from low-throughput, capturing just a few dozen data points, to large-scale, data-rich experiments conducted today.
By Elucidataabout a year ago in 01
Healthcare Data Security: What You Don't Know Can Hurt You!
Navigating healthcare data has always been complex as it requires a high degree of trust between data managers and data controllers. The uniqueness and relative importance of information, determines how cautious we must be in trusting someone with it. Personal Health Information (PHI) or Electronic Health Records (EHR) are at the top of this list, necessitating utmost trust between the data controller and data before any work can commence.
By Elucidataabout a year ago in 01
The Data Surge: Why You Can’t Afford to Ignore Clinical Trial Insights
Clinical trial data plays a key role in ensuring the safety and effectiveness of new drugs and treatments. It is also evidence of drug efficacy. Identifying the right drug, its indication and dosage requires deep domain knowledge and diverse data modalities to assess mechanisms and safety profiles. With humongous clinical trial data being generated on an everyday basis, it is essential to utilize effective data-capture tools yielding high data quality. Access to high quality data further supplements accurate drug evaluation and accelerates the process of drug discovery and development. Due to the growing demands of the current global drug market, pharmaceutical companies globally are being persuaded to adopt innovative ways to minimize drug development timelines and improve productivity by incorporating published clinical trial data as intel.
By Elucidataabout a year ago in 01
Impact of Data Harmonization on Machine Learning Models in Drug Development
Inconsistent data formats and fragmented datasets can severely limit the effectiveness of machine learning models. These challenges necessitate data harmonization to produce effectual results. For example, the COVID-19 pandemic revealed the need for harmonized clinical and genomic data to accelerate drug discovery endeavors. AstraZeneca's AI-driven models benefited from harmonized datasets to identify new potential drugs for COVID-19. Similarly, harmonization was pivotal in Pfizer's machine learning models to improve clinical trial outcomes. Without harmonized data, these ML-driven breakthroughs in drug development would face roadblocks, and impair innovation and patient outcomes. This blog explores the role and impact of data harmonization on the efficiency and accuracy of ML models in drug development.
By Elucidataabout a year ago in 01
Navigating the Complexity of Life Science Research with Big Data as a Service
Big Data as a Service (BDaaS) has grown by leaps and bounds as unprecedented amounts of data get generated each year in all domains ranging from economics, engineering, and social media to medicine and drug discovery. The BDaaS market is expected to grow from USD 18.49 billion in 2021 to USD 124.02 billion by 2028; it is estimated to grow at a CAGR of 31.5% from 2022 to 2028.
By Elucidataabout a year ago in 01
Uniting the Omics: Integrating Proteomics and Transcriptomics Data
Proteomics and transcriptomics represent two powerful branches of omics science, offering unique perspectives on gene expression, protein function, and cellular processes. Proteomics focuses on the study of proteins and transcriptomics examines the expression levels of genes through RNA transcripts. Integrating data from these omics disciplines helps to increase the power of data because of how these data complement each other. In this blog, we discuss the power of integrating proteomics and transcriptomics data, the challenges in integrating them across data dimensions and how these challenges might be met by innovative solutions.
By Elucidataabout a year ago in 01
Single-Cell RNA-seq Data Analysis Beyond H5ad
Single cell RNA-sequencing (scRNA-seq) technology has come a long way since its first successful implementation in 2009. This technology has provided unprecedented insights into biological processes and improved our understanding at the cellular level. In the last decade, various scRNA-seq technologies have been developed and revolutionized sample collection, single-cell capture, barcoded reverse transcription, cDNA amplification, library preparation, sequencing, and streamlined bioinformatics analysis.
By Elucidataabout a year ago in 01
Role of MLOps in Biomedical Research
Biomedical Research with its cutting edge innovations and futuristic outlook can positively utilize MLOps for successful drug discovery. In sync with current times, research in this field is proactively driven by a need to offer timely and qualitative healthcare solutions. How can Machine Learning Operations (MLOps) transform the way biomedical researchers predict ADMET properties and identify promising drug candidates?
By Elucidataabout a year ago in 01
Multi-omics: Opportunities, Challenges and Applications in Biomarker Discovery
Biomarker discovery holds immense potential to revolutionize healthcare by providing insights into disease mechanisms, enabling early diagnosis, and guiding personalized treatment strategies. Traditional approaches often focus on single-omics data, which can provide a limited understanding of complex biological systems. In contrast, multi-omics integrates various omics data, ensuring a detailed and holistic view of human biology.
By Elucidataabout a year ago in 01
Data Processing Pipelines for RNA-seq Data
RNA sequencing (RNA-seq) provides insights into gene expression levels, alternative splicing events, and the discovery of novel transcripts. This technology is essential for understanding gene function, identifying biomarkers, and exploring the complexities of cellular responses along with regulatory mechanisms. Advanced pipelines are needed to effectively process and interpret this complex data type. This blog explores the significance of efficient data processing pipelines, and the challenges involved in building them. It also highlights how Elucidata has created and offers customized solutions to facilitate efficient data processing and downstream analyses.
By Elucidataabout a year ago in 01
Impact of Data Harmonization on Machine Learning Models in Drug Development
Inconsistent data formats and fragmented datasets can severely limit the effectiveness of machine learning models. These challenges necessitate data harmonization to produce effectual results. For example, the COVID-19 pandemic revealed the need for harmonized clinical and genomic data to accelerate drug discovery endeavors. AstraZeneca's AI-driven models benefited from harmonized datasets to identify new potential drugs for COVID-19. Similarly, harmonization was pivotal in Pfizer's machine learning models to improve clinical trial outcomes. Without harmonized data, these ML-driven breakthroughs in drug development would face roadblocks, and impair innovation and patient outcomes. This blog explores the role and impact of data harmonization on the efficiency and accuracy of ML models in drug development.
By Elucidataabout a year ago in 01