बायोमेट्रिक्स और बायोस्टैटिस्टिक्स जर्नल

Biostatistics, a field that specializes in the analysis of data arising from biomedical research, remains a vibrant and ever-evolving discipline. Recent breakthroughs in biomedical research have ushered in a new era of complexity and opened up fresh challenges and opportunities for statisticians and data scientists. Notable areas of advancement in biostatistics include the analysis of complex time-to-event data and addressing issues related to missing data. These challenges have become particularly prominent in application areas such as medicine, genetics, neuroscience, and engineering. Biostatistics is indeed a highly dynamic and evolving field that continually adapts to the challenges and opportunities presented by advances in biomedical research.

Quantum computing is a cutting-edge field of computing that harnesses the principles of quantum mechanics to perform certain types of calculations much faster than classical computers. Unlike classical bits, which can represent either a 0 or a 1, quantum bits or qubits can exist in a superposition of states, allowing them to represent both 0 and 1 simultaneously. Additionally, qubits can be entangled, meaning the state of one qubit is dependent on the state of another, even if they are physically separated. Qubits can exist in multiple states at once, which enables quantum computers to explore many possibilities simultaneously. Classical computers, in contrast, process data sequentially.

The reported study was motivated by the necessity to select 30 soybean lines from a total of 137 for a sophisticated 3-D phenotyping analysis of the Root System Architecture (RSA), which would not allow that all the lines be included and replicated. A representative subset of size 30 was found after performing four cluster analyses and comparing the results of two more particularly. These two cluster analyses are based on the data for 12 RSA-related traits previously collected in 2D on three replicates of the 137 soybean lines and the first six principal components representing 95% of the total dispersion after data standardization in a preliminary Principal Component Analysis (PCA). The two cluster analysis procedures provided 16 soybean lines that were the closest to the centroid of their respective cluster in both cases. Fourteen more were found to be common and at a distance from the centroid below a pre-set threshold value without being the closest. The final selection of 30 excludes two soybean lines that were the second member selected from their cluster, and includes instead two soybean lines that are the closest and second closest to their respective centroid in the cluster analysis after PCA on standardized data, but are not well represented in the other cluster analysis. In conclusion, the 93.3% overlap between the two sets of results shows a robust clustering structure in RSA 2-D phenotyping in soybean. Our statistical approaches and procedures can be followed and applied in other biological frameworks than plant phenotyping.

Artificial Intelligence (AI) and biostatistics are two distinct but interconnected fields that play a crucial role in healthcare, medical research, and the life sciences. Biostatistics is primarily concerned with collecting, analyzing, and interpreting data in the life sciences. AI techniques, such as machine learning, can be used to automate the analysis of large and complex biological and medical datasets. AI algorithms can identify patterns, correlations, and insights from these datasets that may not be apparent through traditional statistical methods.

Ethical conduct in biostatistics is essential to ensure the integrity, credibility and trustworthiness of research in the field of healthcare and biomedical sciences. Biostatisticians play a crucial role in the design, analysis, and interpretation of research studies, and they must adhere to high ethical standards. Biostatisticians should respect the principles of informed consent when working with human subjects in research. They must ensure that participants understand the purpose, risks, and benefits of the study, especially when handling sensitive or personal data.