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Indian Scientists Develop AI Framework for Personalised Cancer Therapy

deltin55 Yesterday 23:28 views 81

Indian researchers have unveiled a new artificial intelligence framework capable of transforming cancer diagnosis and treatment by analysing the disease at its molecular level. Developed at the S N Bose National Centre for Basic Sciences in collaboration with Ashoka University, the tool offers a pathway to highly personalised cancer therapies.

A Molecular Approach to Understanding Cancer

The framework, named OncoMark, shifts focus from traditional cancer staging to the underlying biological programmes that drive tumour growth. These programmes, known as the hallmarks of cancer, determine how cells become malignant, spread through the body, evade immune responses and resist treatment. By decoding these mechanisms, the system provides deeper insight into why patients with similar stages may experience vastly different outcomes.

Large-Scale Analysis and Pseudo-Biopsy Modelling

Researchers examined 3.1 million single cells spanning 14 cancer types. They generated synthetic “pseudo-biopsies” to map hallmark-driven tumour states, allowing the AI model to understand how processes such as metastasis, immune escape and genomic instability interact. This dataset formed the foundation for training OncoMark to recognise distinct molecular patterns across cancers.

Accuracy, Validation and Clinical Potential

OncoMark achieved more than 99 per cent accuracy in internal testing and maintained above 96 per cent accuracy across five independent cohorts. The tool was also validated using 20,000 patient samples from eight global datasets. For the first time, scientists were able to visualise how hallmark activity intensifies as cancer progresses, making it possible to identify aggressive tumours that may appear less threatening under standard clinical staging.


Exam Oriented Facts

OncoMark is an AI framework developed by S N Bose National Centre and Ashoka University.
It analysed 3.1 million single cells from 14 cancer types.
The tool achieved over 99% internal accuracy and 96% across independent cohorts.
Findings were published in Communications Biology, a Nature journal.


Pathway to Personalised and Targeted Cancer Treatment


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