Introduction
In recent years, there has been increasing number of studies using artificial intelligence (AI) and machine learning (ML) in biological and biomedical fields. Especially in genomics, cancer research, pathology and systems biology, we can see more papers published that apply AI methods to analyze complex datasets.
For many researchers, it is exciting to read about these applications. But it is another matter to apply them on your own data. Often the methods look promising, but are not easy to run or interpret without proper experience.
At AccuraScience, we work closely with researchers who have good data and clear biological questions, and want to apply published AI methods in a proper way. Below are 10 AI use cases that have already appeared in the literature, but are still difficult for many labs to implement on their own.
1. Cancer Subtype Classification from Gene Expression Data
Machine learning models like Random Forest and XGBoost have been widely used to classify tumors into known subtypes or even identify novel ones. This is especially common in studies of breast, lung, and colorectal cancers using RNA-seq or microarray data.
However, when people try to reproduce such models, they often face problems such as batch effects, high dimensionality, or overfitting. We help build a proper modeling pipeline, with careful feature selection, training-validation split, and visualization. These steps are critical for generating results that are reliable and can be submitted for publication.
2. Biomarker Discovery from Multi-Omics and Clinical Data
Many published studies use ML models to discover biomarkers that predict patient outcome or treatment response. These models usually combine transcriptomics, methylation, proteomics, and clinical variables.
But integration of these data is not trivial. Some researchers are not familiar with how to normalize and align multi-omics data, or how to prevent information leakage during feature selection. At AccuraScience, we can assist with complete modeling pipeline, and we also use interpretability tools like SHAP to highlight important features that can be used in figures and grant proposals.
3. CNN-Based Models for Histopathology Image Analysis
Convolutional neural networks (CNNs) are often used in studies that analyze whole-slide pathology images, especially H&E-stained images. In papers, we often see tumor classification or grading based on CNN models, sometimes using ResNet or EfficientNet with transfer learning.
But in practice, it is difficult for many research groups to set up a working image pipeline. You need to tile the images, annotate labels, do data augmentation, and sometimes GPU training. We can help run such pipeline and output results including confidence heatmaps or patch-level predictions, which can be shown in a publication or poster.
4. Cell Type Classification in scRNA-seq
Large single-cell RNA-seq datasets require automated methods for cell type labeling. Tools like CellTypist, scANVI or Seurat-based classifiers are used in many recent studies.
The tool may be available, but using them properly is not always easy. For example, batch correction, choosing reference, or setting right threshold for ambiguous cells. We help tune these models and annotate cell types in a way that is both accurate and biologically meaningful.
5. Trajectory Inference Using Deep Generative Models
In development biology or cancer progression studies, trajectory analysis is a common technique. It helps infer how cells move from one state to another. Tools like Monocle 3, scVI, or Slingshot are used to estimate such paths.
However, the result of trajectory analysis is sensitive to parameters and model choice. Also, the biological meaning is not always clear. We support researchers to apply these models and generate UMAP or 3D embedding that helps illustrate the differentiation or transition process more clearly.
6. Integrating Spatial Transcriptomics and Histology Images
In spatial biology, integration of gene expression and histology image becomes important. Tools like Tangram or SpaGCN are used in papers to align spatial transcriptomics data (e.g. 10X Visium) with tissue images.
But these tools are not easy to use. The format of input files, GPU requirement, and training settings are all challenging. We help researchers set up this analysis and generate region-level expression maps for downstream interpretation.
7. Survival Prediction Using Machine Learning
In studies of patient outcome, many groups use ML models like CoxNet, DeepSurv or survival forests to predict survival time or risk group. These are used in many cancer research projects.
However, survival modeling needs to treat censored data carefully. If data is not formatted correctly or validation is improper, the result may not be trusted. We offer support for survival model building and generating KM curves with proper risk stratification.
8. Variant Effect Prediction Using Deep Learning
For non-coding or regulatory variants, deep learning models like DeepSEA or SpliceAI can predict how the variant may change gene regulation or splicing. These tools are already used in human genetics studies.
But they are difficult to run on local data. The formatting is complex, the model file is large, and output is not easy to understand. We help researchers run these models on their variants of interest and provide interpretation tables or visual output.
9. Drug Response Modeling from Gene Expression
In pharmacogenomics, ML has been used to link transcriptomic data with drug response (e.g. IC50) in datasets like CCLE or GDSC. Some groups try to reproduce this on their own data to predict which drugs may work better.
But usually the sample size is small, and data distribution is not the same as public datasets. We help apply these models carefully and guide interpretation so that the result can be presented with proper confidence.
10. Patient Stratification via Clustering and Embedding
Sometimes the goal is to stratify patients or samples based on molecular features. Unsupervised methods like k-means, UMAP, or autoencoder-based clustering are used for this purpose.
However, clustering results are very sensitive to preprocessing, scaling, and number of clusters. We help stabilize the analysis, check for robustness, and generate biologically meaningful grouping for downstream analysis.
📌 Final Thoughts
In all these examples, the methods have already appeared in published papers - some of them quite frequently. But applying the methods correctly on your own dataset is not always easy.
We do not try to invent new AI models, but we are very experienced in implementing published methods for real biological or medical data. If you are thinking to try one of these AI applications in your research, or if you already tried but got stuck, we are happy to help.
About the Author: Dr. Zack Tu is a computational biologist with over 20 years of experience in genomics, biomedical informatics, and large-scale sequencing data analysis. He received his B.S. in Biochemistry, M.S. in Software Systems, and Ph.D. in Pharmacology, and previously led the setup and operation of the core bioinformatics infrastructure at the University of Minnesota’s research sequencing center, as well as contributing to clinical genome sequencing workflows. Since joining AccuraScience in 2013 as Lead Bioinformatician, Zack has supported a wide range of research projects involving genome assembly and polishing, variant calling pipelines, bulk and single-cell RNA-seq, spatial transcriptomics, and functional annotation. His technical expertise includes de novo genome assembly, somatic/germline SNV and Indel detection, isoform-level expression analysis, batch correction and normalization strategies, supervised and unsupervised learning for high-dimensional omics data, and integration of machine learning methods - such as convolutional neural networks, XGBoost, and variational autoencoders - into biological data interpretation.
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