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General General

The application of pangenomics and machine learning in genomic selection in plants.

In The plant genome

Genomic selection approaches have increased the speed of plant breeding, leading to growing crop yields over the last decade. However, climate change is impacting current and future yields, resulting in the need to further accelerate breeding efforts to cope with these changing conditions. Here we present approaches to accelerate plant breeding and incorporate nonadditive effects in genomic selection by applying state-of-the-art machine learning approaches. These approaches are made more powerful by the inclusion of pangenomes, which represent the entire genome content of a species. Understanding the strengths and limitations of machine learning methods, compared with more traditional genomic selection efforts, is paramount to the successful application of these methods in crop breeding. We describe examples of genomic selection and pangenome-based approaches in crop breeding, discuss machine learning-specific challenges, and highlight the potential for the application of machine learning in genomic selection. We believe that careful implementation of machine learning approaches will support crop improvement to help counter the adverse outcomes of climate change on crop production.

Bayer Philipp E, Petereit Jakob, Danilevicz Monica Furaste, Anderson Robyn, Batley Jacqueline, Edwards David


oncology Oncology

[Evaluation of quality of life: Clinical relevance for patient].

In Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique

The quality of life of patients and its evaluation remains one of the primordial objectives in oncology. Different methods and tools of evaluation of quality of life have been developed with the objective of having a global evaluation, throughout different aspects, be it physical, emotional, psychological or social. The quality of life questionnaires improve and simplify the reevaluation and follow-up of patients during clinical trials. Patient reported outcome measures (PROMs) are an evaluation of the quality of life as experienced by the patients (patient-reported-outcomes [PROs]) and allow for physicians a personalized treatment approach. In radiotherapy, PROMs are a useful tool for the follow-up of patients during or after treatment. The technological advances, notably in data collecting, but also in their integration and treatment with regard to artificial intelligence will allow integrating these evaluation tools in the management of patients in oncology.

Dossun C, Popescu B V, Antoni D


EPROS, Evaluation, PROS, Patient, Quality of life, Qualité de vie, ePROS, Évaluation

General General

Deep-learning-based motion correction in OCT angiography.

In Journal of biophotonics

Optical coherence tomography angiography (OCTA) is a widely applied tool to image microvascular networks with high spatial resolution and sensitivity. Due to limited imaging speed, the artifacts caused by tissue motion can severely compromise visualization of the microvascular networks and quantification of OCTA images. In this paper, we propose a deep-learning-based framework to effectively correct motion artifacts and retrieve microvascular architectures. This method comprised two deep neural networks in which the first subnet was applied to distinguish motion corrupted B-scan images from a volumetric dataset. Based on the classification results, the artifacts could be removed from the en face maximum-intensity-projection (MIP) OCTA image. To restore the disturbed vasculature induced by artifact removal, the second subnet, an inpainting neural network, was utilized to reconnect the broken vascular networks. We applied the method to postprocess OCTA images of the microvascular networks in mouse cortex in vivo. Both image comparison and quantitative analysis show that the proposed method can significantly improve OCTA image by efficiently recovering microvasculature from the overwhelming motion artifacts. This article is protected by copyright. All rights reserved.

Li Ang, Du Congwu, Pan Yingtian


OCTA, deep neural networks, microvascular network, motion correction

oncology Oncology

Unsupervised flow cytometry analysis in hematological malignancies: A new paradigm.

In International journal of laboratory hematology ; h5-index 29.0

Ever since hematopoietic cells became "events" enumerated and characterized in suspension by cell counters or flow cytometers, researchers and engineers have strived to refine the acquisition and display of the electronic signals generated. A large array of solutions was then developed to identify at best the numerous cell subsets that can be delineated, notably among hematopoietic cells. As instruments became more and more stable and robust, the focus moved to analytic software. Almost concomitantly, the capacity increased to use large panels (both with mass and classical cytometry) and to apply artificial intelligence/machine learning for their analysis. The combination of these concepts raised new analytical possibilities, opening an unprecedented field of subtle exploration for many conditions, including hematopoiesis and hematological disorders. In this review, the general concepts and progress achieved in the development of new analytical approaches for exploring high-dimensional data sets at the single-cell level will be described as they appeared over the past few years. A larger and more practical part will detail the various steps that need to be mastered, both in data acquisition and in the preanalytical check of data files. Finally, a step-by-step explanation of the solution in development to combine the Bioconductor clustering algorithm FlowSOM and the popular and widely used software Kaluza® (Beckman Coulter) will be presented. The aim of this review was to point out that the day when these progresses will reach routine hematology laboratories does not seem so far away.

Béné Marie C, Lacombe Francis, Porwit Anna


artificial intelligence, flow cytometry, machine learning, unsupervised analysis

Pathology Pathology

Machine learning in health care and laboratory medicine: General overview of supervised learning and Auto-ML.

In International journal of laboratory hematology ; h5-index 29.0

Artificial Intelligence (AI) and machine learning (ML) have now spawned a new field within health care and health science research. These new predictive analytics tools are starting to change various facets of our clinical care domains including the practice of laboratory medicine. Many of these ML tools and studies are also starting to populate our literature landscape as we know it but unfamiliarity of the average reader to the basic knowledge and critical concepts within AI/ML is now demanding a need to better prepare our audience to such relatively unfamiliar concepts. A fundamental knowledge of such platforms will inevitably enhance cross-disciplinary literacy and ultimately lead to enhanced integration and understanding of such tools within our discipline. In this review, we provide a general outline of AI/ML along with an overview of the fundamental concepts of ML categories, specifically supervised, unsupervised, and reinforcement learning. Additionally, since the vast majority of our current approaches within ML in laboratory medicine and health care involve supervised algorithms, we will predominantly concentrate on such platforms. Finally, the need for making such tools more accessible to the average investigator is becoming a major driving force for the need of automation within these ML platforms. This has now given rise to the automated ML (Auto-ML) world which will undoubtedly help shape the future of ML within health care. Hence, an overview of Auto-ML is also covered within this manuscript which will hopefully enrich the reader's understanding, appreciation, and the need for embracing such tools.

Rashidi Hooman H, Tran Nam, Albahra Samer, Dang Luke T


Algorithm, artificial intelligence, auto-ML, feature selection, principal component analysis

General General

Facial recognition accuracy in photographs of Thai neonates with Down syndrome among physicians and the Face2Gene application.

In American journal of medical genetics. Part A

Down syndrome (DS) is typically recognizable in those who present with multiple dysmorphism, especially in regard to facial phenotypes. However, as the presentation of DS in neonates is less obvious, a phenotype-based presumptive diagnosis is more challenging. Recently, an artificial intelligence (AI) application, Face2Gene, was developed to help physicians recognize specific genetic syndromes by using two-dimensional facial photos. As of yet, there has not been any study comparing accuracy among physicians or applications. Our objective was to compare the facial recognition accuracy of DS in Thai neonates, using facial photographs, among physicians and the Face2Gene. Sixty-four Thai neonates at Thammasat University Hospital, with genetic testing and signed parental consent, were divided into a DS group (25) and non-DS group (39). Non-DS was further divided into unaffected (19) and those affected with other syndromes (20). Our results revealed physician accuracy (89%) was higher than the Face2Gene (81%); however, the application was higher in sensitivity (100%) than physicians (86%). While this application can serve as a helpful assistant in facilitating any genetic syndrome such as DS, to aid clinicians in recognizing DS facial features in neonates, it is not a replacement for well-trained doctors.

Srisraluang Wewika, Rojnueangnit Kitiwan


Face2Gene, Thai neonates with Down syndrome, facial recognition