From one-size-fits-all to Personalized Treatment (and Medicine) designed for each Patient based on their genetic history, genomic sequencing, medical records, location, environmental factors, and more. AI can predict the impact of such specific treatment per patient
Understand the Use-case under 5 minutes
Video (4 minutes)
The role of AI in untangling the complexity in RNA biology, identifying novel targets dealing with genomic dysfunctions, and evaluating thousands of possibilities to identify the best therapeutic candidates
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Article (5 minutes)
How AI is important for enabling Precision Medicine. Not only it’s vital for predicting outcomes but also for Predicting Future Patients’ Probability of Having certain diseases
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Video (2 minutes)
As a subset of precision medicine, genomic medicine accelerates the speed at which scientists understand diseases. It also enables clinicians to provide more accurate diagnoses and prescribe individualized treatments to help patients based on their genetic code.
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Get to know more Business and Technical details about the use-case (15-30 minutes)
More detailed introduction covering business and technical aspects
Article (16 minutes)
Sharing many examples of using Machine Learning to predict best Treatment, based on analyzing genomic data along with historical EHR data sets. Expands on possibilities and limitations of AI-based Precision Medicine
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Article (14 minutes)
Expanding on the role of AI in drug discovery and precision oncology, referencing examples for efforts pursued by major industry players (Roche & Pfizer), and highlighting some of the major challenges foreseen
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Article (6 minutes)
This article you will understand the role of innovation in genomics through Artificial Intelligence (AI) and its impact on precision medicine.
ReadCase studies, Organizational Aspects, Return on Investment examples
Video (25 minutes)
Using Machine learning to improve assay quality and cost, and increasing the predictive power of single-cell data. Enabling precise detection of heterogeneity in disease progression and treatment response
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White Paper (7 minutes)
More details on the technical aspects of the use-case
Article (15 minutes)
Active research in both AI and precision medicine is demonstrating a future where health‐related tasks of both medical professionals and consumers are augmented with highly personalized medical diagnostic and therapeutic information.
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Video (14.5 minutes)
In this video you will learn about cofactor genomics, a biotech company dedicated to bridging the precision medicine gap. They couple machine learning with cloud computing to support its groundbreaking system of predictive immune modelling.
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Technical resources that will help you implement the use-case (notebooks, tutorials..)
Article (19 minutes)
In this work, they employed CNN, CNN-LSTM, and CNN-Bidirectional LSTM architectures using Label and K-mer encoding for DNA sequence classification. The models are evaluated on different classification metrics. From the experimental results, the CNN and CNN-Bidirectional LSTM with K-mer encoding offers high accuracy with 93.16% and 93.13%, respectively, on testing data.
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Video (53 minutes)
Interpretability of machine learning models is important for data-driven precision medicine efforts. This video digs into some techniques to interpret some precision medicine models
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Github Repo
The ‘personalized’ package is designed for the analysis of data where the effect of a treatment or intervention may vary for different patients.
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Colab Notebook
This colab notebook, develops a CNN to classify DNA sequences from two-data sets. Which was replicated from this paper.
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Article with Code (55 minutes)
How ML algorithms can be used for DNA/RNA/protein sequences classification and prediction
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An Artificial Neural Network-based discriminator for validating clinically significant genomic variants.
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Article (19 minutes)
In this work, they employed CNN, CNN-LSTM, and CNN-Bidirectional LSTM architectures using Label and K-mer encoding for DNA sequence classification. The models are evaluated on different classification metrics. From the experimental results, the CNN and CNN-Bidirectional LSTM with K-mer encoding offers high accuracy with 93.16% and 93.13%, respectively, on testing data.
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Data Sets you can use to build Demos, POCs, or test Algorithms
Training and Testing Datasets for 1. Genetic Mutations and 2. Clinical Evidence experts used to classify those mutations
PM Trials data. The primary results of the first round of pediatric precision oncology clinical trials will provide us with a greater understanding of the clinical impact of linking tumor profiling to selection of targeted therapies.
Uncovering significant spatial and phenotypic immune–ITH from multiple tumor sectors and deciphering its relationship with tumor evolution and disease progression in hepatocellular carcinomas (HCC).
Off-the-Shelf Products using AI for Fraud Detection
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