What You Should Know:

你应该知道的：

–

–

Biostate AI,

生物状态人工智能，

an innovator at the intersection of artificial intelligence and RNA sequencing raises $12M in Series A funding spearheaded by

一位处于人工智能和RNA测序交叉领域的创新者获得了由某公司领投的1200万美元A轮融资

Accel

加速

, with participation from

，参与方包括

Gaingels

盖恩格尔斯

,

，

Mana Ventures

Mana Ventures

,

，

InfoEdge Ventures

信息边缘风险投资公司

, and existing investors

，以及现有投资者

Matter Venture Partners,

物质风险合伙人，

Vision Plus Capital

视加资本

, and

，以及

Catapult Ventures

弹射器风险投资公司

. This brings the company’s total funding to over $20M.

这使得该公司的总融资额超过2000万美元。

– The newly acquired funds will be pivotal in advancing Biostate AI’s mission to unlock affordable and integrated precision medicine, beginning with the widespread accessibility of RNA sequencing (RNAseq) services for US-based molecular research.

新获得的资金将对推动Biostate AI实现解锁可负担且综合的精准医疗使命至关重要，首先是为美国基于分子研究的广泛应用RNA测序（RNAseq）服务提供支持。

– The company aims to develop clinically relevant predictive models, laying the groundwork for truly personalized therapeutics.

该公司旨在开发具有临床意义的预测模型，为真正个性化的治疗奠定基础。

Unlocking the Transcriptome: A New Frontier in Precision Medicine

解锁转录组：精准医学的新前沿

Founded by former professors and repeat entrepreneurs David Zhang (CEO) and Ashwin Gopinath (CTO), Biostate AI operates on the principle that the entire RNA transcriptome – the full range of RNA transcripts in a cell – is an underutilized real-time biomarker for human health. Until now, the comprehensive and simultaneous analysis of all these transcripts has been hampered by significant cost and analytical barriers.

由前教授和多次创业者David Zhang（首席执行官）与Ashwin Gopinath（首席技术官）创立的Biostate AI基于一个原则运作：整个RNA转录组——细胞中所有RNA转录本的全范围——是人类健康的一种未被充分利用的实时生物标志物。到目前为止，对所有这些转录本进行全面且同时分析一直受到显著的成本和分析障碍的阻碍。

Biostate AI aims to eliminate these bottlenecks, envisioning a “one-stop shop” for precision medicine by making RNAseq significantly cheaper and more effective..

Biostate AI 旨在消除这些瓶颈，通过使 RNAseq 显著更便宜和更有效，构想出一个“一站式”精准医疗平台。

Overcoming Traditional RNAseq Limitations with AI and Innovation

利用人工智能和创新克服传统RNA测序的局限性

Conventional RNA sequencing faces several key challenges that Biostate AI is engineered to solve:

传统的RNA测序面临几个关键挑战，而Biostate AI正是为解决这些问题而设计的：

High Cost:

高成本：

It’s expensive, limiting the scale of research for many labs, especially as research budgets tighten. Biostate has developed patented biomolecular technologies (BIRT and PERD) that reduce the cost of turning tissue samples into RNAseq data by nearly an order of magnitude, effective on both fresh and decades-old tissues.

它很昂贵，限制了许多实验室的研究规模，尤其是在研究预算收紧的情况下。Biostate 开发了获得专利的生物分子技术（BIRT 和 PERD），这些技术可将组织样本转化为 RNAseq 数据的成本降低近一个数量级，且对新鲜组织和数十年的老组织均有效。

This allows researchers to process 2-3 times more samples within existing budgets..

这使得研究人员能够在现有预算内处理多2到3倍的样本。

Data Aggregation Issues:

数据聚合问题：

Combining datasets from various research sites often introduces “batch effects” – noise that can obscure subtle clinical signals. Biostate’s lower internal costs facilitate the collection of millions of consented, de-identified RNAseq profiles globally, creating a massive dataset to train sophisticated generative AI models..

结合来自各个研究站点的数据集常常会引入“批次效应”——这种噪声可能会掩盖细微的临床信号。Biostate 较低的内部成本有助于在全球范围内收集数百万份经过同意且去标识化的 RNAseq 数据，从而创建一个庞大的数据集，用于训练复杂的生成式 AI 模型。

Lack of Standardization & Vendor Siloing:

缺乏标准化和供应商孤岛化：

Inconsistent methodologies across studies make data comparison difficult, and reliance on multiple specialized vendors leads to communication breakdowns and slower workflows. Biostate’s unified workflow standardizes experiments, enabling its AI to consistently learn the “grammar of biology” without confounding batch effects.

不同研究方法的不一致性使数据比较变得困难，而依赖多个专业供应商则会导致沟通中断和工作流程变慢。Biostate 的统一工作流程将实验标准化，使其人工智能能够持续学习“生物学语法”，避免了批次效应的混淆影响。

This also allows for the extraction of meaningful signals from smaller, clinically labeled cohorts to fine-tune models..

这还允许从较小的、具有临床标签的队列中提取有意义的信号，以微调模型。

Towards General-Purpose AI for Understanding and Curing Disease

面向理解与治疗疾病的通用人工智能

While Large Language Models learn from text, Biostate’s AI models identify gene expression signatures correlated with specific disease states and treatment responses. This enables the detection of subtle molecular changes that may precede clinical symptoms by weeks, months, or even years, facilitating earlier intervention..

虽然大型语言模型从文本中学习，但 Biostate 的人工智能模型能够识别与特定疾病状态和治疗反应相关的基因表达特征。这使得检测可能在临床症状出现前数周、数月甚至数年发生的细微分子变化成为可能，从而促进更早的干预。

“Rather than solve the diagnostics and therapeutics as separate, siloed problems for each disease, we believe that the modern and future AI can be general purpose to understand and help cure every disease,” said David Zhang, co-founder and CEO of Biostate AI, and former Associate Professor of Bioengineering at Rice University.

“我们认为，现代和未来的人工智能可以具备通用性，能够理解并帮助治愈每一种疾病，而不是将每个疾病的诊断和治疗视为孤立的问题来解决，”Biostate AI联合创始人兼首席执行官、前莱斯大学生物工程副教授张大卫表示。

“Every diagnostic I’ve built was about moving the answer closer to the patient. Biostate takes the biggest leap yet by making the whole transcriptome affordable.”.

“我开发的每一项诊断技术都旨在让答案更接近患者。Biostate 通过使整个转录组变得可负担，实现了迄今为止最大的飞跃。”

Early Traction and Future Expansion

早期吸引力与未来扩展

The AI developed from this wealth of RNAseq data is intended to better inform clinicians of optimal treatment decisions. Biostate has already achieved internal proof-of-concept success in predicting disease recurrence in human leukemia patients and plans to expand collaborations with clinical partners in oncology, autoimmune disease, and cardiovascular disease..

从这些丰富的RNAseq数据中开发出来的人工智能旨在更好地告知临床医生最佳的治疗决策。Biostate已经在预测人类白血病患者的疾病复发方面取得了内部概念验证的成功，并计划扩大与肿瘤学、自身免疫疾病和心血管疾病领域的临床合作伙伴的合作。

Since commercializing its offering just two quarters ago, Biostate has processed RNAseq for over 10,000 samples from more than 150 collaborators and customers at leading institutions, including pilot projects for leukemia with Cornell and multiple sclerosis with the Accelerated Cure Project. The startup has also secured agreements to process several hundred thousand unlabeled samples annually, rapidly accelerating its dataset growth and AI development capabilities..

自从两个季度前将其产品商业化以来，Biostate已为来自150多家合作方和客户的超过10,000个样本进行了RNA测序，其中包括与康奈尔大学合作的白血病试点项目，以及与加速治愈项目（Accelerated Cure Project）合作的多发性硬化症项目。这家初创公司还达成了协议，每年处理数十万个未标记样本，迅速加快了其数据集增长和人工智能开发能力。