Purpose: Single-cell RNA sequencing (scRNA-seq) is producing vast amounts of individual cell profiling data. Analysis of such datasets presents a significant challenge in accurately annotating cell types and their associated biomarkers. scRNA-seq datasets analysis will help us understand diseases such as Alzheimer’s, Cancer, Diabetes, Coronavirus disease 2019 (COVID-19), Systemic Lupus Ery-thematosus (SLE), etc. Recently different pipelines based on machine learning (ML) and Deep Neural Network (DNN) methods have been employed to tackle these issues utilizing scRNA-seq datasets. These pipelines have arisen as a promising resource and are capable of extracting meaningful and concise features from noisy, diverse, and high-dimensional data to enhance annotations and subsequent analysis. Existing tools require high computational resources to execute large sample datasets. Methods: We have developed a cutting-edge platform known as scaLR (Single Cell Analysis using Low Resource) that efficiently processes data in batches, and reduces the required resources for processing large datasets and running NN models. scaLR is equipped with data processing, feature extraction, training, evaluation, and downstream analysis. The data processing module consists of sample-wise & standard scaler normalization and splitting of data. Its novel feature extraction algorithm, first trains the model on a feature subset and stores feature importance for all the features in that subset. At the end of this process, top K features are selected based on their importance. The model is trained on top K features, its performance evaluation and associated downstream analysis provide significant biomarkers for different cell types and diseases/traits. Results: To showcase the capabilities of scaLR, we utilized several scRNA-seq datasets of Peripheral Blood Mononuclear Cells (PBMCs), Alzheimer’s patients, and large datasets from human and mouse embryonic development. Our findings indicate that scaLR offers comparable prediction accuracy and requires less model training time and compute resources than existing Python-based pipelines and frameworks. Moreover, scaLR efficiently handles large sample datasets ( > 11.4 million cells) with minimal resource usage (29GB RAM, 12GB GPU, and 8 CPUs) while maintaining high prediction accuracy and being capable of ranking the biomarker association with specific cell types and diseases. Conclusion: We present scaLR a Python-based platform , engineered to utilize minimal computational resources while maintaining comparable execution times to existing frameworks. It is highly scalable and capable of efficiently handling datasets containing millions of cell samples and providing their classification and important biomarkers.

Only about 10% of clinical programmes reach approval, yet drug mechanisms backed by human genetic evidence succeed 2.6 times as often, and plasma-protein evidence raised that to 4.7-fold in a preprint. Each molecular layer adds value only when integrated with the others, a step that automated analysis pipelines leave to human interpretation. Multi-agent artificial intelligence, in which specialist agents reason over individual omics layers under a shared orchestrator, promises to make that connection continuous. We assess how far the evidence supports it. Cross-omics agent systems that generate hypotheses from raw multiomics data exist, but to our knowledge none has been compared head-to-head with conventional pipelines and single-agent baselines. Performance metrics highlight the rapid evolution of multi-omics benchmarks: the best BixBench open-answer score was 17% at release, and a curated multiple-choice subset was answered at 84–98% a year later, yet research-study-scale tasks remain near 0.5. We compare six orchestration patterns, survey 2025–2026 systems and quantify fifteen obstacles, from inflated Type-I error and cascading failures to bias, security and oversight. We then propose eleven testable research directions, each with a validation metric and a rated primary risk, including an agent interoperability layer for biology, a provenance-and-confidence ledger, a living benchmark and trace-level failure diagnosis offered as a research agenda rather than as demonstrated results.