Delving into the Latest Updates on Aigen Sciences, Inc. with Synapse

概览

疾病领域	数量

肿瘤	7
内分泌与代谢疾病	1
免疫系统疾病	1

排名前五的药物类型	数量

小分子化药	6
化学药	2

排名前五的靶点	数量

PARP x USP1	1
SOS1(SOS1蛋白质)	1
KRAS(KRAS蛋白)	1

关联

8

项与 Aigen Sciences, Inc. 相关的药物

AIG-01012

靶点

SOS1

作用机制

SOS1抑制剂

在研机构

Aigen Sciences, Inc.初创企业

原研机构

Aigen Sciences, Inc.初创企业

在研适应症

肿瘤

非在研适应症-

最高研发阶段临床前

首次获批国家/地区-

首次获批日期-

AIG-Onco1

靶点

KRAS

作用机制

KRAS抑制剂

在研机构

Aigen Sciences, Inc.初创企业

原研机构

Aigen Sciences, Inc.初创企业

在研适应症

结肠癌 [+1]

非在研适应症-

最高研发阶段临床前

首次获批国家/地区-

首次获批日期-

USP1 inhibitor(Aigen Sciences)

靶点

PARP x USP1

作用机制

PARP抑制剂 [+1]

在研机构

Aigen Sciences, Inc.初创企业

原研机构

Aigen Sciences, Inc.初创企业

在研适应症

肿瘤

非在研适应症-

最高研发阶段临床前

首次获批国家/地区-

首次获批日期-

100 项与 Aigen Sciences, Inc. 相关的临床结果

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0 项与 Aigen Sciences, Inc. 相关的专利（医药）

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2

项与 Aigen Sciences, Inc. 相关的文献（医药）

2024-08-01·JCO Clinical Cancer Informatics

Deep-Transfer-Learning–Based Natural Language Processing of Serial Free-Text Computed Tomography Reports for Predicting Survival of Patients With Pancreatic Cancer

Article

作者: Kim, Sunkyu ; Shin, Sang Joon ; Kim, Eejung ; Choi, Ji A ; Kang, Chang Moo ; Choi, Hye Jin ; Kim, Sung Hyun ; Park, Mi-Suk ; Cecchini, Michael ; Kang, Jaewoo ; Kim, Seung-seob ; Hwang, Ho Kyoung ; Lee, Choong-kun

PURPOSE To explore the predictive potential of serial computed tomography (CT) radiology reports for pancreatic cancer survival using natural language processing (NLP). METHODS Deep-transfer-learning–based NLP models were retrospectively trained and tested with serial, free-text CT reports, and survival information of consecutive patients diagnosed with pancreatic cancer in a Korean tertiary hospital was extracted. Randomly selected patients with pancreatic cancer and their serial CT reports from an independent tertiary hospital in the United States were included in the external testing data set. The concordance index (c-index) of predicted survival and actual survival, and area under the receiver operating characteristic curve (AUROC) for predicting 1-year survival were calculated. RESULTS Between January 2004 and June 2021, 2,677 patients with 12,255 CT reports and 670 patients with 3,058 CT reports were allocated to training and internal testing data sets, respectively. ClinicalBERT (Bidirectional Encoder Representations from Transformers) model trained on the single, first CT reports showed a c-index of 0.653 and AUROC of 0.722 in predicting the overall survival of patients with pancreatic cancer. ClinicalBERT trained on up to 15 consecutive reports from the initial report showed an improved c-index of 0.811 and AUROC of 0.911. On the external testing set with 273 patients with 1,947 CT reports, the AUROC was 0.888, indicating the generalizability of our model. Further analyses showed our model's contextual interpretation beyond specific phrases. CONCLUSION Deep-transfer-learning–based NLP model of serial CT reports can predict the survival of patients with pancreatic cancer. Clinical decisions can be supported by the developed model, with survival information extracted solely from serial radiology reports.

2023-07-26·Database : the journal of biological databases and curation

Biomedical relation extraction with knowledge base-refined weak supervision.

Article

作者: Yoon, Wonjin ; Kim, Hyunjae ; Kim, Sunkyu ; Yi, Sean ; Jackson, Richard ; Kang, Jaewoo

Biomedical relation extraction (BioRE) is the task of automatically extracting and classifying relations between two biomedical entities in biomedical literature. Recent advances in BioRE research have largely been powered by supervised learning and large language models (LLMs). However, training of LLMs for BioRE with supervised learning requires human-annotated data, and the annotation process often accompanies challenging and expensive work. As a result, the quantity and coverage of annotated data are limiting factors for BioRE systems. In this paper, we present our system for the BioCreative VII challenge-DrugProt track, a BioRE system that leverages a language model structure and weak supervision. Our system is trained on weakly labelled data and then fine-tuned using human-labelled data. To create the weakly labelled dataset, we combined two approaches. First, we trained a model on the original dataset to predict labels on external literature, which will become a model-labelled dataset. Then, we refined the model-labelled dataset using an external knowledge base. Based on our experiment, our approach using refined weak supervision showed significant performance gain over the model trained using standard human-labelled datasets. Our final model showed outstanding performance at the BioCreative VII challenge, achieving 3rd place (this paper focuses on our participating system in the BioCreative VII challenge). Database URL: http://wonjin.info/biore-yoon-et-al-2022.

100 项与 Aigen Sciences, Inc. 相关的药物交易

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100 项与 Aigen Sciences, Inc. 相关的转化医学

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2024年12月22日管线快照

管线布局中药物为当前组织机构及其子机构作为药物机构进行统计，早期临床1期并入临床1期，临床1/2期并入临床2期，临床2/3期并入临床3期

药物发现

5

3

临床前

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当前项目

药物(靶点)	适应症	全球最高研发状态

AIG-01012 ( SOS1 )	肿瘤更多	临床前
AIG-Onco1 ( KRAS )	结肠癌更多	临床前
USP1 inhibitor(Aigen Sciences) ( PARP x USP1 )	肿瘤更多	临床前
AIG-Onco3	实体瘤更多	药物发现
AIG-Immune1	自身免疫性疾病更多	药物发现