Natural Language-Based Scientific Analytics

Scientists face complex challenges every day. Science in general is hard. Unlike computer science, biology is filled with countless variables shaped by billions of years of evolution. Navigating this intricate landscape requires both precision and creativity.

You can’t algorithm your way out in Biology. At least not in the near future. Biology is the evolution of billions of years that requires human ingenuity and creativity along with LLMs.

Two main types of scientists play crucial roles in this field. Wet lab scientists, dressed in white coats, conduct hands-on experiments. They meticulously run assays to detect biomarkers in patient samples or perform PCR tests to identify genetic mutations. Their work is the heartbeat of biotech research, turning hypotheses into tangible results.

On the other side are computational biologists. These scientists, armed with powerful computers and sophisticated algorithms, analyze data from the lab. They might sequence genomes using bioinformatics tools or apply machine learning algorithms to predict disease progression. Their work transforms raw data into meaningful insights, bridging the gap between experimental results and real-world applications.

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Despite their vital contributions, both groups face a common enemy: time-consuming and complex data analysis. Wet lab scientists often find themselves bogged down by a plethora of tools like GraphPad, Excel, and intricate Python scripts. Each task, whether it is analyzing growth curves, calculating IC50 values, or performing multi-variable regression analyses, demands significant time and technical know-how. The constant back-and-forth with computational biologists only slows down progress.

Imagine a wet lab scientist plotting growth curves. They track the growth of cell cultures over time, which requires precise data collection and meticulous analysis. Or consider the effort involved in calculating IC50 values, which involves detailed statistical analysis to determine the concentration needed to inhibit a biological process by 50%. Multi-variable regression analyses require advanced tools to understand the relationships between different experimental variables and outcomes.

With Generative AI, wet lab scientists can go from data generation to analysis in a matter of seconds.

Scispot’s AI transforms this cumbersome process. Scientists no longer need to be coding experts to perform advanced scientific analytics. With Scispot’s Retrieval-Augmented Generation (RAG) system, they can generate detailed analyses and stunning scientific charts using simple natural language commands.

Using LLMs and proprietary transformers, Scispot converts natural language into Python scripts that generate very specific scientific charts. Wet lab scientists can eliminate the complete lifecycle of data wrangling. Their focus becomes more on validating the hypothesis with human creativity rather than QCing the data for every new metadata.

“Show me the growth curve for our latest cell culture experiment,” and within seconds, a detailed, accurate chart appears. Need to calculate IC50 values? Just ask. Want to perform a complex regression analysis? It’s as easy as a simple request.

Rather than using excel, graph pad prism, and various data management tools, scientists can now focus on input data, and validating the hypothesis. AI is becoming increasingly a middleware with great accuracy.

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Scispot’s AI works behind the scenes, powered by the RAG architecture. RAG combines two powerful technologies: information retrieval and generative models. Here’s how it works:

• Retrieval: The AI searches a large database of scientific literature, protocols, and previous experiment data to find relevant information related to the scientist’s query.

• Augmentation: The retrieved information is used to enhance the context and provide more accurate and relevant responses.

• Generation: The AI, using a scientifically trained model, generates a detailed analysis or visualization based on the enhanced context.

Embeddings play a crucial role in this process. They represent text data as numerical vectors, allowing the AI to understand the context and meaning of queries. By training on scientific data, the model grasps the specific language and nuances of biotech research.

Let’s explore some real-world use cases where Scispot’s AI has made a significant impact:

Use Case 1: Drug Discovery

In drug discovery, calculating IC50 values is a critical task. Before Scispot, scientists spent hours using complex Python scripts and statistical software to perform these calculations. With Scispot, they simply ask the AI, “Calculate the IC50 value for our new drug compound,” and receive accurate results in minutes. This efficiency accelerates the drug discovery process, allowing scientists to focus on developing new therapies.

Use Case 2: Genomic Sequencing

Genomic sequencing generates vast amounts of data that require intricate analysis. Computational biologists used to spend days analyzing sequence data to identify genetic variations. Now, they can use Scispot to ask, “Identify significant genetic variations in our latest sequencing data,” and get detailed, actionable insights quickly. This capability speeds up research in genomics and personalized medicine.

Use Case 3: High-Throughput Screening

High-throughput screening involves testing thousands of samples to identify active compounds. Managing and analyzing the resulting data is a daunting task. With Scispot, scientists can streamline this process. By asking the AI, “Analyze the high-throughput screening results and identify potential hits,” they receive comprehensive reports that highlight promising compounds, significantly reducing the time to discovery.

Use Case 4: Clinical Trials

Clinical trials generate large datasets that require meticulous analysis to assess the efficacy and safety of new treatments. Researchers can now leverage Scispot to analyze trial data efficiently. A query like, “Analyze the clinical trial data for treatment X and summarize the outcomes,” yields detailed reports, enabling faster decision-making and regulatory submissions.

Trust is crucial in scientific research, and Scispot’s AI is built with transparency at its core. Scientists can see exactly how the AI generates charts and analyses, ensuring complete data integrity. The system provides a detailed breakdown of each analysis step, from initial data input to the final chart output. This transparency builds trust and ensures accurate, reliable results. Scientists can review the AI’s steps, verify calculations, and make necessary adjustments to meet exact requirements.

Scispot’s AI-driven analytics eliminate the need for Python scripts, SQL queries, and Excel. Tasks that once took days now take minutes. The AI delivers results with 99% accuracy, maintaining a full chain of custody with ease.

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One researcher shared, “Before Scispot, I spent hours writing Python scripts to analyze my data. Now, I get the same results in minutes using simple natural language commands. It’s a game-changer.”

In biotech, maintaining a complete chain of custody for data and analyses is crucial. Scispot’s AI-driven platform documents every step of the analytical process. From data input to final output, scientists have a clear, auditable trail that meets regulatory requirements and builds confidence in results. The system logs every action taken by the AI, ensuring data integrity and compliance.

Time is precious in biotech research, and Scispot’s AI-powered platform maximizes efficiency. By automating complex tasks, the platform frees up valuable time for scientists to focus on experimental design, data interpretation, and innovation. Analysis time is reduced from days to minutes, allowing quick insights, data-driven decisions, and faster research.

Scispot’s platform enhances human expertise. Scientists interact with the AI using natural language, guiding the workflow with their knowledge and intuition. They can explore different hypotheses, adjust parameters, and refine analyses based on their judgment. This collaboration leverages the strengths of both AI and human expertise, leading to meaningful scientific insights.

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