AI & Machine Learning
Accelerate Research with AI & ML Tools for Biological Data
R Programming Services
Analyze complex biological data with R. Our team helps you find important insights with clear visualizations, strong statistical tools, and bioinformatics techniques. Whether you’re working with genomic data or creating predictive models, we help turn raw data into simple, meaningful results that support your research.
We create interactive visualizations using R packages like ggplot2, plotly, and shiny. Our team designs customizable dashboards to help explore and uncover insights in genomic data, allowing researchers to find patterns, correlations, and outliers. We use techniques like dimensionality reduction and clustering to clearly present complex biological results.
We build and interpret statistical models using R’s built-in functions like lm(), glm(), and survival. Our team uses bio-statistical methods such as regression,time-to-event, and survival analysis to uncover patterns and relationships in biological data. We validate models through cross-validation, bootstrapping, and simulation, ensuring reliable results.
We use R packages like Bioconductor, edgeR, and DESeq2 for genomic data analysis. Our team combines these tools to solve problems in gene expression, variant detection, and functional annotation. We help researchers gain insights into biological processes and disease mechanisms, using specialized tools for next-generation sequencing (NGS), microarray, and genomic region enrichment analysis.
Using R libraries like caret, randomForest, and xgboost, we build predictive models and perform cross-validation. Our team improves model performance with techniques like feature selection, hyperparameter tuning, and ensemble methods for accurate predictions. We create customized machine learning pipelines for classification, regression, and clustering tasks in biological data analysis.
Python Programming Services
Let Python handle your biological data tasks! Whether you need to clean large datasets, create visualizations, or automate workflows, we’ve got you covered. We customize Python solutions to fit your needs, helping you explore genomic data, streamline processes, and make discoveries faster and easier.
We use Python libraries like Pandas and NumPy to manage and clean large biological datasets. Our team ensures data quality through profiling, normalization, and transformation for reliable analysis. We create automated data processing pipelines for high-throughput sequencing, microarray, and other biological data types.
We create interactive visualizations using Matplotlib, Seaborn, and Plotly. Our team designs custom visualizations for projects like genome-wide association studies (GWAS) and protein structure analysis, applying visual perception principles. We also use 3D visualization techniques and interactive dashboards to help explore and discover insights.
Using Python packages like BioPython, Pysam, and scikit-bio, we perform sequence analysis, genomic data processing, and biological computation. Our team combines these tools to provide solutions for genomic assembly, variant calling, and gene expression analysis, as well as specialized tools for next-generation sequencing (NGS), microarray, and genomic region enrichment analysis.
We build machine learning and deep learning models using Scikit-learn, TensorFlow, and Keras. Our team improves model performance with techniques like transfer learning, regularization, and ensemble methods. We create custom machine learning pipelines for classification, regression, and clustering tasks, applied to biological data analysis like GWAS and protein structure prediction.
- We automate biological data tasks like file parsing, batch processing, and high-throughput sequencing analysis. Our team streamlines workflows with Python scripting to ensure reproducibility, scalability, and efficiency, integrating automation tools into bioinformatics pipelines to boost productivity.
AI Services
Unlock the power of AI for your biological research! Our team uses advanced machine learning and deep learning techniques to discover patterns and predict outcomes accurately. From genomics to natural language processing, we’ll create custom AI models that turn your data into actionable insights, giving you an advantage in your research.
We use machine learning algorithms like random forests, SVMs, and neural networks on biological datasets. Our team finds patterns and relationships, interpreting results within biological processes. We create custom machine learning models for predictive analytics, classification, and regression tasks in genomics, proteomics, and transcriptomics.
Our team uses deep learning frameworks like TensorFlow and PyTorch for genomic sequence classification, variant prediction, and gene expression analysis. We create custom deep learning models that incorporate domain knowledge, using transfer learning and fine-tuning techniques to adapt pre-trained models for specific genomics tasks.
We use NLP techniques to mine biological literature, extract insights, and build biomedical text models. Our team aids knowledge discovery, hypothesis generation, and decision support through text analysis, entity recognition, and sentiment analysis. We create custom NLP pipelines for biological text processing, named entity recognition, and relationship extraction.
Ready to bring your vision to life? Let’s schedule a Free Consultation and explore how we can make your project a success!
