Biology subjects

Bernstein, J.

Publications and source records attributed to Bernstein, J..

4 recordsLinked to original sources

bioRxiv2025

Biomni: A General-Purpose Biomedical AI Agent

Biomedical research underpins progress in our understanding of human health and disease, drug discovery, and clinical care. However, with the growth of complex lab experiments, large datasets, many analytical tools, and expansive literature, biomedical research is increasingly constrained by repetitive and fragmented workflows that slow discovery and limit innovation, underscoring the need for a fundamentally new way to scale scientific expertise. Here, we introduce Biomni, a general-purpose biomedical AI agent designed to autonomously execute a wide spectrum of research tasks across diverse biomedical subfields. To systematically map the biomedical action space, Biomni first employs an action discovery agent to create the first unified agentic environment - mining essential tools, databases, and protocols from tens of thousands of publications across 25 biomedical domains. Built on this foundation, Biomni features a generalist agentic architecture that integrates large language model (LLM) reasoning with retrieval-augmented planning and code-based execution, enabling it to dynamically compose and carry out complex biomedical workflows - entirely without relying on predefined templates or rigid task flows. Systematic benchmarking demonstrates that Biomni achieves strong generalization across heterogeneous biomedical tasks - including causal gene prioritization, drug repurposing, rare disease diagnosis, micro-biome analysis, and molecular cloning - without any task-specific prompt tuning. Real-world case studies further showcase Biomnis ability to interpret complex, multi-modal biomedical datasets and autonomously generate experimentally testable protocols. Biomni envisions a future where virtual AI biologists operate alongside and augment human scientists to dramatically enhance research productivity, clinical insight, and healthcare. Biomni is ready to use at https://biomni.stanford.edu, and we invite scientists to explore its capabilities, stress-test its limits, and co-create the next era of biomedical discoveries.

bioinformatics↗

bioRxiv2021

Direct induction of human neurons from fibroblasts carrying the neuropsychiatric 22q11.2 microdeletion reveals transcriptome- and epigenome-wide alterations

Standard methods for the creation of neuronal cells via direct induction from primary tissue use perinatal fibroblasts, which hinders the important study of patient specific genetic lesions such as those underlying neuropsychiatric disorders. To address this we developed a novel method for the direct induction of neuronal cells (induced neuronal cells, iN cells) from adult human fibroblast cells. Reprogramming fibroblasts into iN cells via recombinant virus resulted in cells that stain for markers such as MAP2 and PSA-NCAM and exhibit electrophysiological properties such as action potentials and voltage dependent sodium- and potassium currents that reveal a neuronal phenotype. Transcriptome and chromatin analysis using RNA-Seq, microRNA-Seq and ATAC-Seq, respectively, further confirm neuronal character. 22q11.2 Deletion-Syndrome (22q11DS) is caused by a large 3 million base-pair heterozygous deletion on human chromosome 22 and is strongly associated with neurodevelopmental, neuropsychiatric phenotypes such as schizophrenia and autism. We leverage the direct-iN cell model for the study of genetic neurodevelopmental conditions by presenting gene-by-gene as well as networkwide effects of the 22q11DS deletion on gene expression in human neuronal cells, on several levels of functional genomics analysis. Some of the genes within the 22q11DS deletion boundary exhibit unexpected cell-type-specific changes in transcript levels, and genome-wide we can detect dysregulation of calcium channel subunit genes and other genes known to be involved in autism or schizophrenia, such as NRXN1, as well synaptic pathways. This genome-wide effect on gene expression can also be observed at the microRNA and chromatin levels, showing that the iN cells have indeed converted to a neuronal phenotype at several regulatory levels: chromatin, protein-coding RNAs and microRNAs, revealing relevant disease pathways and genes. We present this model of inducing neurons from fibroblasts as a useful general resource to study the genetic and molecular basis of normal and abnormal brain development and brain function.

genomics↗

bioRxiv2019

Network effects of the neuropsychiatric 15q13.3 microdeletion on the transcriptome and epigenome in human induced neurons

Heterozygous deletions in the 15q13.3 region are associated with several neuropsychiatric disorders including autism, schizophrenia, and attention deficit hyperactivity disorder. Several genes within the 15q13.3 deletion region may play a role in neuronal dysfunction, based on association studies in humans and functional studies in mice, but the intermediate molecular mechanisms remain unknown. We analyzed the genome-wide effects of the 15q13.3 microdeletion on the transcriptome and epigenome. Induced pluripotent stem cell (iPSC) lines from three patients with the typical heterozygous 15q13.3 microdeletion and three sex-matched controls were generated and converted into induced neurons (iNs) using the neurogenin-2 induction method. We analyzed genome-wide gene expression using RNA-Seq, genome-wide DNA methylation using SeqCap-Epi, and genome-wide chromatin accessibility using ATAC-Seq, in both iPSCs and iNs. In both cell types, gene copy number change within the 15q13.3 microdeletion was accompanied by significantly decreased gene expression and no compensatory changes in DNA methylation or chromatin accessibility, supporting the model that haploinsufficiency of genes within the deleted region drives the disorder. Further, we observed global effects of the deletion on the transcriptome and epigenome, with the effects being cell type specific and occurring at discrete loci. Several genes and pathways associated with neuropsychiatric disorders and neuronal development were significantly altered, including Wnt signaling, ribosome biogenesis, DNA binding, and clustered protocadherins. This molecular systems analysis of a large neuropsychiatric microdeletion can also be applied to other brain relevant chromosomal aberrations to further our etiological understanding of neuropsychiatric disorders.

genetics↗

bioRxiv2019

Machine Learning Methods to Identify Genetic Correlates of Radiation-Associated Contralateral Breast Cancer in the WECARE Study

The purpose of this study is to identify germline single nucleotide polymorphisms (SNPs) that optimally predict radiation-associated contralateral breast cancer (RCBC) and to provide new biological insights into the carcinogenic process. Fifty-two women with contralateral breast cancer and 153 women with unilateral breast cancer were identified within the Womens Environmental Cancer and Radiation Epidemiology (WECARE) Study who were at increased risk of RCBC because they were [≤] 40 years of age at first diagnosis of breast cancer and received a scatter radiation dose > 1 Gy to the contralateral breast. A previously reported algorithm, preconditioned random forest regression, was applied to predict the risk of developing RCBC. The resulting model produced an area under the curve of 0.62 (p=0.04) on hold-out validation data. The biological analysis identified the cyclic AMP-mediated signaling and Ephrin-A as significant biological correlates, which were previously shown to influence cell survival after radiation in an ATM-dependent manner. The key connected genes and proteins that are identified in this analysis were previously identified as relevant to breast cancer, radiation response, or both. In summary, machine learning/bioinformatics methods applied to genome-wide genotyping data have great potential to reveal plausible biological correlates associated with the risk of RCBC.

bioinformatics↗