Artificial Intelligence in Molecular Medicine

Gomes, Bruna; Ashley, Euan A.

doi:10.1056/nejmra2204787

Cited by 38 publications

(21 citation statements)

References 55 publications

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“…Artificial intelligence (AI) and machine learning (ML) tools are expected to fill this gap given their unprecedented ability to integrate multiple types of data, extracting relevant features that can inform on hidden causalities, enable accurate predictions, build useful classifications, and generate novel information [17]. Different ML tools have been developed to leverage genetic information of patients for different purposes within a precision medicine context (e.g., patients’ stratification, disease risk assessment or identification of susceptibility features) [18, 19].…”

Section: Introductionmentioning

confidence: 99%

GFPrint™: A MACHINE LEARNING TOOL FOR TRANSFORMING GENETIC DATA INTO CLINICAL INSIGHTS

Sanz-Martín,

Migliore,

del Campo

et al. 2024

Preprint

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The increasing availability of massive genetic sequencing data in the clinical setting has triggered the need for appropriate tools to help fully exploit the wealth of information these data possess. GFPrint™ is a proprietary streaming algorithm designed to meet that need. By extracting the most relevant functional features, GFPrint™ transforms high-dimensional, noisy genetic sequencing data into an embedded representation, allowing unsupervised models to create data clusters that can be re-mapped to the original clinical information. Ultimately, this allows the identification of genes and pathways relevant to disease onset and progression. GFPrint™ has been tested and validated using two cancer genomic datasets publicly available. Analysis of the TCGA dataset has identified panels of genes whose mutations appear to negatively influence survival in non-metastatic colorectal cancer (15 genes), epidermoid non-small cell lung cancer (167 genes) and pheochromocytoma (313 genes) patients. Likewise, analysis of the Broad Institute dataset has identified 75 genes involved in pathways related to extracellular matrix reorganization whose mutations appear to dictate a worse prognosis for breast cancer patients. GFPrint™ is accessible through a secure web portal and can be used in any therapeutic area where the genetic profile of patients influences disease evolution.

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Section: Introductionmentioning

confidence: 99%

GFPrint™: A MACHINE LEARNING TOOL FOR TRANSFORMING GENETIC DATA INTO CLINICAL INSIGHTS

Sanz-Martín,

Migliore,

del Campo

et al. 2024

Preprint

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“…Also accurate long-reads shows higher diagnosis rate among individuals who were previously undiagnosed [26, 27]. Long-read sequencing paired with methods based on machine learning for variant detection shows promise to have a far reaching impact in healthcare [28].…”

Section: Introductionmentioning

confidence: 99%

Local read haplotagging enables accurate long-read small variant calling

Kolesnikov,

Cook,

Nattestad

et al. 2023

Preprint

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Long-read sequencing technology has enabled variant detection in difficult-to-map regions of the genome and enabled rapid genetic diagnosis in clinical settings. Rapidly evolving third-generation sequencing platforms like Pacific Biosciences (PacBio) and Oxford nanopore technologies (ONT) are introducing newer platforms and data types. It has been demonstrated that variant calling methods based on deep neural networks can use local haplotyping information with long-reads to improve the genotyping accuracy. However, using local haplotype information creates an overhead as variant calling needs to be performed multiple times which ultimately makes it difficult to extend to new data types and platforms as they get introduced. In this work, we have developed a local haplotype approximate method that enables state-of-the-art variant calling performance with multiple sequencing platforms including PacBio Revio system, ONT R10.4 simplex and duplex data. This addition of local haplotype approximation makes DeepVariant a universal variant calling solution for long-read sequencing platforms.

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“…The next step in the evolution of AI was deep neural networks, which had multiple intermediate layers that were not visible in addition to the simple artificial neural network. [ 3 , 4 ]…”

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confidence: 99%

“…Bidirectional recurrent neural networks had a long short term memory (LSTM) function which further enabled learning. [ 3 , 4 ] A specific subset of deep learning is machine perception, which implies the ability of computers to perceive and sense information akin to human senses. Computer vision is the specific ability of computers to perceive and analyze information from images and videos using deep learning by breaking them down into individual components.…”

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confidence: 99%

“…The advent of transformers enabled the wider applicability of AI large language models (LLMs) such as the Chat Generative Pretrained Transformer (ChatGPT) which could read through voluminous literature to generate answers much like an experienced human reader. [ 3 - 5 ] ChatGPT4 from OpenAI,[ 6 ] Claude from Anthropic,[ 7 ] Bard from Google,[ 8 ] and Grok from X/Twitter[ 9 ] are the prominent LLMs available today. Claude is accessible from only a few countries, whereas, the other LLMs are accessible worldwide.…”

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confidence: 99%

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What does artificial intelligence mean in rheumatology?

Chandwar,

Prasanna Misra

2024

Arch Rheumatol

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Intelligence is the ability of humans to learn from experiences to ascribe conscious weights and unconscious biases to modulate their outputs from given inputs. Transferring this ability to computers is artificial intelligence (AI). The ability of computers to understand data in an intelligent manner is machine learning. When such learning is with images and videos, which involves deeper layers of artificial neural networks, it is described as deep learning. Large language models are the latest development in AI which incorporate self-learning into deep learning through transformers. AI in Rheumatology has immense potential to revolutionize healthcare and research. Machine learning could aid clinical diagnosis and decision-making, and deep learning could extend this to analyze images of radiology or positron emission tomography scans or histopathology images to aid a clinician’s diagnosis. Analysis of routinely obtained patient data or continuously collected information from wearables could predict disease flares. Analysis of high-volume genomics, transcriptomics, proteomics, or metabolomics data from patients could help identify novel markers of disease prognosis. AI might identify newer therapeutic targets based on in-silico modelling of omics data. AI could help automate medical administrative work such as inputting information into electronic health records or transcribing clinic notes. AI could help automate patient education and counselling. Beyond the clinic, AI has the potential to aid medical education. The ever-expanding capabilities of AI models bring along with them considerable ethical challenges, particularly related to risks of misuse. Nevertheless, the widespread use of AI in Rheumatology is inevitable and a progress with great potential.

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Artificial Intelligence in Molecular Medicine

Cited by 38 publications

References 55 publications

GFPrint™: A MACHINE LEARNING TOOL FOR TRANSFORMING GENETIC DATA INTO CLINICAL INSIGHTS

GFPrint™: A MACHINE LEARNING TOOL FOR TRANSFORMING GENETIC DATA INTO CLINICAL INSIGHTS

Local read haplotagging enables accurate long-read small variant calling

What does artificial intelligence mean in rheumatology?

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