Introduction
Over the decade, the biological domain has gained significant attention because of the rising problems and contingencies. As a result, with the motive to combat healthcare and biological constraints, data science emerged as the innovative solution, Moreover; the elaborative and effective ecosystem of data science has opened doors for AI-driven biological start-ups across the world. The data science attributes ensure devising appropriate algorithms to predict accurate results. Additionally, multiple levels of experimental learning help to create top competency amongst the lab technicians, and biologists. Thus, data science is dominating the biological sphere with its extended technicalities and full-proven approaches.
What is Biological Data Science?
Biological Data Science (BDS) is a field of study, experimentation, and research where a sheen amount of scientific discipline along with analytical skills is required. As a result, it comprises three core disciplinary areas that support the biological ecosystem by facilitating biological research. The trilogy includes Biology, Computer Science, and Mathematics & Statistics.
- Biology deals with the biological origin of diseases, their cause, diagnostic utilities, etc.
- On the other hand, the Computer Science aspect of BDS is responsible for the introduction and maintenance of appropriate algorithms to eliminate constraints and develop better solutions.
- The Mathematic & Statistical segment of BDS focuses on data summarizing, modeling, normalization, and presentation part, which helps to establish strong connectivity between the existing problems and potent solutions.
Thus, Biological Data Science is an all-rounded approach where facts to figures, data to derivation, anomalies to analysis everything is duly adhered with.
Use Cases of Biological Data Science
The prominent data-driven concept creates huge differences in the biological ecosystem paving the way towards great possibilities.
| Use Case | Description |
| Bio Manufacturing | The term refers to the process of manufacturing commercially important biomaterials and biomolecules that are further used in medicines, beverage processing, etc. Here, the use of Data Science algorithms plays a remarkable role by leveraging essential technological advantages which are called biotechnology. Essentially, the concept contributes immensely in identifying the onset of diseases to allow timely cure for the same. |
| Generative Biology | Being a subject matter revolving around proteins study, generative biology considers the statistical patterns and sequence of amino acids. As a result, Data Science concepts contribute immensely to making this pattern-based study more accurate, precise, and quick. Furthermore, these research concepts are used to custom protein therapeutics. |
| Synthetic Biology | The field deals with redesigning and restructuring organisms for incorporating essential abilities into them. Throughout the world, researchers are harnessing the potential of Data Science for accurately predicting the reengineering requirements and address problems in medicine, agriculture, and manufacturing. |
| Bio- informatics | It is the concept that uses computation and analysis-driven technology like Data Science for capturing and interpreting biological data. The data-driven interdisciplinary field is best-suited for large and complex data sets to derive accurate results. Essentially, it considers the establishment of healthy evolutionary relationships and determines gene & protein functions |
| Biological Research | Besides research being the core biological function, analysis is another automated function that is driven by Data Science algorithms. Thus, biological research focuses on millions of data points relating to genes, proteins, and moieties for facilitating systematic study and establishing strategic outcomes. |
Models / Metrics used for Biological Data Science
The Biological Data Science principles are used in various formats for easing out biological constraints. Being a vast subject full of great potential and possibilities requires in-depth understanding and execution of a suitable algorithm. Such algorithms have made the AI-driven culture, the essentiality in the ecosystem of Biological Data Science.
| Model | Description |
| Hierarchical Clustering | The algorithm is used to group similar objects in distinct groups called clusters which are generally driven by hierarchal cycle. As a result, helps to study successive study and establish tree- structures based on data similarities. |
| Gaussian Mixture | It is a probabilistic model where patterns are studied representing the distributed subpopulation within the complete population. Thus, it plays a useful role in inheriting the segment properties from the normal distribution. |
| Evolutionary Trees | The evolving algorithm is used to modify, select, and move individuals/organisms in the selected environment by conducting gene study, protein restructuring, and establishing DNA relationships. It revolves around two types of evolutionary trees namely distance-based and sequence-based trees. |
| Population Genetics | The specialization of the algorithm lies in modeling evolution, also known Fisher-Wright Model. Moreover, it stimulates and understands the location of the gene. As a result, facilitate gene identification, mutation and strengthen crossover conditions. |
| Sequence Matching | It is also known as the Needleman-Wunsch algorithm and global matching which uses researched knowledge based on the protein of the organism. The model is used to compare dynamic programming to biological patterns and sequences. |
| Gene Regulation Networks | This AI network-driven algorithm allows interaction with different proteins within the organism and helps to establish better protein control. Based on the nature of protein interaction cell types are determined. |
| Hidden Markov Models | Such model plays a crucial role in modeling sequences that define the probability of an event which are dependent on the previous states. Rather it uses probabilistic Finite- State machines to determine the accurate possibility and equalize the original state with the predicted ones. |
Examples of Biological Data Science
The role of Data Science in expanding the horizon of a vast subject, Biology is beyond measure. Also, the concept has brought a big boost for the start-ups too. As a result, Biological Data Science has supported biological research projects with due diligence.
- Benevolent AI made the use of Data Science concepts to discover new solutions to orphan diseases, rare cancers, neurodegeneration, and inflammatory diseases. Moreover, using unstructured data in combination with Machine Learning and Deep Learning algorithms helped the company to identify the potential in hidden scientific knowledge and induce them into delivering prominent solutions.
- Hexagon Bio has provided several research concepts in practice by leveraging their hidden science. According to Hexagon, almost three-quarters of antibiotics, half the anti-cancer compounds which include penicillin and statins are developed from natural fungi like molds and mushrooms. Moreover, while conducting the test with custom-printed DNA parts they developed around 22 compounds to stand by clinical promise. Alongside, Data Science technology is being used for creating copies of gene clusters. With their fungal drug discovery, the company was able to raise $8 million from private investors in just 18months.
- Life Mine Therapeutics, a start-up co-founded by the Chemical Biologist of Harvard University harnessed DNN architecture to replicate with molecular architecture. As a result of which the distinct properties of two different drugs can be combined. It helped the start-up to land with a $55 million series and accelerates the ability to combat diseases. Furthermore, the molecular architecture acts as the input here and AI suggests a potential combination to support the therapeutic effect.
Professionals for Biological Data Science
Without the skilled human resource, each technology remains non-functional and so does biological data science. The wide application of biological data science creates the need for skilled data scientists and analysts to let organizations explore their complete potential. Though there lies no much difference in the skill set of both the experts yet their roles and responsibilities slightly vary based on their core-subject differences.
Data Analyst in Bioinformatics
The field of bioinformatics deals with the analysis of whole-genome-sequencing data. It involves precise use of algorithms, pipelines, software development, storage, and transfer of genomics data. Thus, bioinformatics data analysts work with large databases to provide proper descriptions and predictions about the assigned subject matter. Conducting research and generating reports about disease pathology, cure-seeking experiments, and used algorithms. While analyzing human data, an analyst focuses on three tiers of NGS (Next Generation Sequencing) Pipeline which are – sequence generation, proper alignment with the reference genome, and interpreting research results. In recent times, the role and responsibility of bioinformatics have certainly extended beyond genetic and genomic data study. An analyst is responsible for understanding the evolutionary subjects of the molecular biology and catalogue suitable pathways.
Data Scientist in Biology
The amalgamation of data science with biology has brought wonders to the biological ecosystem. Thus, to create these potent ecosystems data scientists play a crucial role. They are also referred as computational biology scientist who is responsible for developing analytical software for mapping drug interactions, analyzing large biotech data sets, and easy operations of biologists. Precisely, the key role of data scientists extends to genetic analysis services which are based on computational skillset nowadays. Thus, the biological data scientist shall have an analytical, logical, and observant approach towards the things backed by expert command over machine learning and deep learning algorithms to let the biological ecosystem become smarter and intelligent.
Final Words
The successful implementations of Data Science in the biological ecosystem have created the need for biological data scientists with an in-depth understanding of Artificial Intelligence, its sub-sets, and languages. According to George Church, a genome scientist at Harvard Medical Schools believes that horizon of Biological Data Science is expected to be bigger than the space or computer revolution. Biological Data Science being a diverse set of biological concerns deals with all subject matters like Medicine, Genomics, Physiology, Neuroscience, Pharmacology, Ecology with the inclusion of Mathematics and Computer Science. As a result, BDS leaves no field unexplored and no opportunity unchased. The extended use cases of Data Science in Biology have added an additional victory feather to the AI cap. Moreover, its improvised tools and techniques have made the adoption of technology a comfortable choice for all across the industry.
Topics in Data Science
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