Biological Data
Biological Data - Related Videos
07:11
Fully Autonomous Characterization and Data Collection from Crystals of Biological Macromolecules
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Cited by 2 •
2019
Here, we describe how to use the automated screening and data collection options available at some synchrotron beamlines. Scientists send cryocooled samples to the synchrotron, and the diffraction properties are screened, the data sets are collected and processed and, where possible, a structure solution is carried out—all without human intervention.
09:34
A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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2021
This tutorial describes a simple method to construct a deep learning algorithm for performing 2-class sequence classification of metagenomic data.
07:28
Synthetic Biology
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2023
This video presents synthetic biology and its role in bioengineering. Synthetic biology refers to the methods used to genetically modify organisms in order to make them capable of producing large quantities of a product. This product could be a protein that the cell already makes or a new protein that has been encoded in a newly-inserted DNA sequence. Here, we discuss how an organism's genetic material is modified using transformation or transfection. Then, the process is shown in the...
01:57
What is Conservation Biology?
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2019
Conservation biology is a scientific field that focuses on the preservation of biodiversity in order to protect ecosystems while meeting the needs of the human population. Humans require properly functioning ecosystems to maintain our supply of natural resources, including food, medicines, and building materials. Ecosystems also perform critical services, such as purifying our air and water. A large body of evidence indicates that such ecosystem services depend on biodiversity. Furthermore,...
13:19
The Use of Chemostats in Microbial Systems Biology
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Cited by 67 •
2013
Cell growth rate is a regulated process and a primary determinant of cell physiology. Continuous culturing using chemostats enables extrinsic control of cell growth rate by nutrient limitation facilitating the study of molecular networks that control cell growth and how those networks evolve to optimize cell growth.
FAQs
Researchers organize measurements so they can examine patterns, relationships, and meaningful changes across individuals or conditions. Analysis may connect observations from molecular, cellular, physiological, or clinical levels, helping investigators relate biological variation to disease mechanisms, markers, treatment responses, or patient outcomes. The resulting interpretation depends on how consistently the measurements are recorded and compared.
Standardization makes observations more consistent and comparable across samples, individuals, conditions, or studies. Careful data management preserves the organization and quality of measurements, while interpretation determines whether an observed pattern has meaningful clinical or biological relevance. Together, these practices help convert complex observations into reliable evidence rather than treating every recorded difference as a meaningful finding.
Measurements at different biological levels provide related but distinct perspectives. Molecular and cellular observations can contribute information about disease mechanisms, physiological measurements can reflect changes in body function, and clinical measurements can connect those findings with patient conditions or outcomes. Considering these levels together can support a more complete interpretation than relying on only one type of observation.
A typical workflow begins by generating observations through laboratory assays, imaging, patient records, or other measurement methods. Researchers then organize and manage the information, apply analysis to identify patterns or relationships, and interpret the findings in relation to health, disease, treatment, or outcomes. Each stage contributes to whether the final evidence is useful and dependable.
Researchers examine biological measurements for patterns associated with disease or future outcomes. A diagnostic marker can support characterization of a disease, whereas a prognostic marker can help describe expected outcomes or changes over time. Evaluating these patterns in relevant clinical data allows investigators to assess whether the measurements provide information useful for research or clinical decision-making.
Clinical researchers compare biological measurements and patient outcomes across treatment conditions or individuals to identify meaningful response patterns. These observations can help assess how disease-related measures change with treatment and whether outcomes differ among patients. When interpreted carefully, such evidence can contribute to clinical decision-making and to healthcare approaches that account for individual biological and clinical variation.