Результаты поиска

Обнаружение корневых причинных генов с вспышками с высокой пропускной способностью.

Уровни корневой причинно-следственной экспрессии - или коренные причинные гены для короткого периода - соответствуют первоначальным изменениям генной экспрессии, которые порождают симптомы пациента как эффект ниже по течению. Выявление коренных причинно-следственных генов имеет решающее значение для разработки методов лечения, которые изменяют болезнь в момент ее возникновения, однако никакие существующие алгоритмы не пытаются определить коренные причинно-следственные гены на основе данных. Данные РНК-последовательность (RNA-seq) создают такие проблемы, как погрешность измерения, высокомерност

Decoding Immunodeficiencies with Artificial Intelligence: A New Era of Precision Medicine.

Primary and secondary immunodeficiencies comprise a wide array of illnesses marked by immune system abnormalities, resulting in heightened vulnerability to infections, autoimmunity, and cancers. Notwithstanding progress in diagnostic instruments and an enhanced comprehension of the underlying pathophysiology, delayed diagnosis and underreporting persist as considerable obstacles. The implementation of artificial intelligence into clinical practice has surfaced as a viable method to enhance early detection, risk assessment, and management of immunodeficiencies. Recent advancements illustrate ho

Explainable artificial intelligence for precision medicine in acute myeloid leukemia.

Artificial intelligence (AI) can unveil novel personalized treatments based on drug screening and whole-exome sequencing experiments (WES). However, the concept of "black box" in AI limits the potential of this approach to be translated into the clinical practice. In contrast, explainable AI (XAI) focuses on making AI results understandable to humans. Here, we present a novel XAI method -called multi-dimensional module optimization (MOM)- that associates drug screening with genetic events, while guaranteeing that predictions are interpretable and robust. We applied MOM to an acute myeloid leuk

The Applications of Machine Learning in the Management of Patients Undergoing Stem Cell Transplantation: Are We Ready?

Hematopoietic stem cell transplantation (HSCT) is a life-saving therapy for hematologic malignancies, such as leukemia and lymphoma and other severe conditions but is associated with significant risks, including graft versus host disease (GVHD), relapse, and treatment-related mortality. The increasing complexity of clinical, genomic, and biomarker data has spurred interest in machine learning (ML), which has emerged as a transformative tool to enhance decision-making and optimize outcomes in HSCT. This review examines the applications of ML in HSCT, focusing on donor selection, conditioning re

Машинное обучение алгоритмов вводит эвоидные потенциальные возможности в качестве альтернативных биомаркеров для расширения масштабов инвалидности, прогнозируя пациентов с множественным склерозом.

Заметно возросло значение машинного обучения (ML) в области медицины, главным образом благодаря расширению доступа к медицинским данным и прогрессивному совершенствованию алгоритмов ML. Таким образом, ML может использоваться для разработки прогнозных моделей, способствующих диагностике заболеваний, прогнозированию развития заболеваний, адаптации лечения к индивидуальным потребностям пациентов и повышению оперативной эффективности систем здравоохранения. Своевременное обнаружение того или иного заболевания способствует эффективному лечению симптомов и гарантирует надлежащее лечение. В случае мн

Революционизация медицины сна: влияние машинного обучения на диагностику, лечение и индивидуальное лечение.

Преобразовательным инструментом в медицине сна стало обучение основам махинации (ML), предлагающее новаторские решения для диагностики, лечения и понимания расстройств сна. В настоящем обзоре рассматривается нынешнее состояние и будущий потенциал применения ML в этой области. Methods Мы провели всеобъемлющий обзор литературы с уделением особого внимания ключевым достижениям в области технологий ML, применяемых для лечения сна. Были изучены, в частности, такие конкретные области, как классификация стадий сна, оптимизация лечения апноэ и бессонницы сна, а также внедрение износостойкой технологии

Machine Learning: A New Prospect in Multi-Omics Data Analysis of Cancer.

Cancer is defined as a large group of diseases that is associated with abnormal cell growth, uncontrollable cell division, and may tend to impinge on other tissues of the body by different mechanisms through metastasis. What makes cancer so important is that the cancer incidence rate is growing worldwide which can have major health, economic, and even social impacts on both patients and the governments. Thereby, the early cancer prognosis, diagnosis, and treatment can play a crucial role at the front line of combating cancer. The onset and progression of cancer can occur under the influence of

Machine Learning for Multi-Omics Characterization of Blood Cancers: A Systematic Review.

Artificial Intelligence and machine learning are increasingly used to interrogate complex biological data. This systematic review evaluates their application to multi-omics for the molecular characterization of hematological malignancies, an area with unmet clinical need. We searched PubMed, Embase, Institute of Electrical and Electronics Engineers Xplore, and Web of Science from January 2015 to December 2024. Two reviewers screened records, extracted data, and used a modified appraisal emphasizing explainability, performance, reproducibility, and ethics. From 2847 records, 89 studies met incl

Алгоритмы машинного обучения для виртуального раннего обнаружения и выявления нейродегенеративных и нейрокогнитивных расстройств: систематический обзор.

Происхождение и аналоговые расстройства (например, болезнь Альцгеймера, болезнь Паркинсона) приводят к нейронной потере; нейрокогнитивные расстройства (например, бред, слабоумие) свидетельствуют о когнитивном упадке. Раннее обнаружение имеет решающее значение для эффективного управления. Машинное обучение помогает в более точном выявлении заболеваний, потенциально преобразуя медицинское обслуживание. В рамках этого всеобъемлющего систематического обзора обсуждается вопрос о том, каким образом машинное обучение (ML) может способствовать раннему выявлению этих расстройств, превосходя при этом тр

Совпадение в полном тексте источника
… . | The study introduces a methodology using genetic algorithms to select critical features from Alzheimer’s Disease (AD) data, including gene indexes and clinical assessments. These features are utilized to create supervised classification algorithms with SVM architecture. … Algorithm (GA) | 6 genes were identified in association with AD. | This method adaptively achieves more reliable and efficient candidate biomarkers in a data-driven manner, overcoming the limits related to the impact of subjective factors and dependence on prior knowledge. …

Translating Data Science Results into Precision Oncology Decisions: A Mini Review.

While reviewing and discussing the potential of data science in oncology, we emphasize medical imaging and radiomics as the leading contextual frameworks to measure the impacts of Artificial Intelligence (AI) and Machine Learning (ML) developments. We envision some domains and research directions in which radiomics should become more significant in view of current barriers and limitations.

Research on RNA modification in disease diagnosis and prognostic biomarkers: current status and challenges.

RNA modification, as a crucial post-transcriptional regulatory mechanism, plays a pivotal role in normal physiological processes and is closely associated with the onset and progression of various human diseases. Recent studies have highlighted significant alterations in the level of RNA modifications, including m6A, m6Am, m1A, m5C, m7G, ac4C, Ψ, and A-to-I editing, across multiple diseases. These findings suggest the potential of RNA modifications and their regulatory factors as biomarkers for early disease diagnosis and prognosis. This review provides an overview of statistical methods, mach

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… Wrapper methods iteratively evaluate feature combinations, such as linear SVM recursive feature elimination and genetic algorithms, based on the predictive performance of a given model. …

Applications of Neural Network-Based Plan-Cancer Method for Primary Diagnosis of Mesothelioma Cancer.

"Malignant mesothelioma (MM)" is an uncommon although fatal form of cancer. The proper MM diagnosis is crucial for efficient therapy and has significant medicolegal implications. Asbestos is a carcinogenic material that poses a health risk to humans. One of the most severe types of cancer induced by asbestos is "malignant mesothelioma." Prolonged shortness of breath and continuous pain are the most typical symptoms of the condition. The importance of early treatment and diagnosis cannot be overstated. The combination "epithelial/mesenchymal appearance of MM," however, makes a definite diagnosi

A Systematic Review of Machine Learning Techniques in Hematopoietic Stem Cell Transplantation (HSCT).

Machine learning techniques are widely used nowadays in the healthcare domain for the diagnosis, prognosis, and treatment of diseases. These techniques have applications in the field of hematopoietic cell transplantation (HCT), which is a potentially curative therapy for hematological malignancies. Herein, a systematic review of the application of machine learning (ML) techniques in the HCT setting was conducted. We examined the type of data streams included, specific ML techniques used, and type of clinical outcomes measured. A systematic review of English articles using PubMed, Scopus, Web o

Translational Bioinformatics Applied to the Study of Complex Diseases.

Translational Bioinformatics (TBI) is defined as the union of translational medicine and bioinformatics. It emerges as a major advance in science and technology by covering everything, from the most basic database discoveries, to the development of algorithms for molecular and cellular analysis, as well as their clinical applications. This technology makes it possible to access the knowledge of scientific evidence and apply it to clinical practice. This manuscript aims to highlight the role of TBI in the study of complex diseases, as well as its application to the understanding and treatment o

Integrated Network Pharmacology Approach for Drug Combination Discovery: A Multi-Cancer Case Study.

Despite remarkable efforts of computational and predictive pharmacology to improve therapeutic strategies for complex diseases, only in a few cases have the predictions been eventually employed in the clinics. One of the reasons behind this drawback is that current predictive approaches are based only on the integration of molecular perturbation of a certain disease with drug sensitivity signatures, neglecting intrinsic properties of the drugs. Here we integrate mechanistic and chemocentric approaches to drug repositioning by developing an innovative network pharmacology strategy. We developed

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… Genetic algorithms are well established tools to solve this kind of problem. … Robustness and Stability Evaluation We assessed the robustness of the genetic algorithm with respect to random initializations by running each use case ten times. …

Novel Insights in Venous Thromboembolism Risk Assessment Methods in Ambulatory Cancer Patients: From the Guidelines to Clinical Practice.

Many cancer patients will experience venous thromboembolism (VTE) at some stage, with the highest rate in the initial period following diagnosis. Novel cancer therapies may further enhance the risk. VTE in a cancer setting is associated with poor prognostic, a decreased quality of life, and high healthcare costs. If thromboprophylaxis in hospitalized cancer patients and perioperative settings is widely accepted in clinical practice and supported by the guidelines, it is not the same situation in ambulatory cancer patient settings. The guidelines do not recommend primary thromboprophylaxis, exc

Artificial intelligence across the cancer care continuum.

Artificial intelligence (AI) holds significant potential to enhance various aspects of oncology, spanning the cancer care continuum. This review provides an overview of current and emerging AI applications, from risk assessment and early detection to treatment and supportive care. AI-driven tools are being developed to integrate diverse data sources, including multi-omics and electronic health records, to improve cancer risk stratification and personalize prevention strategies. In screening and diagnosis, AI algorithms show promise in augmenting the accuracy and efficiency of medical image ana

Advances in Leukemia detection and classification: A Systematic review of AI and image processing techniques.

BACKGROUND: Leukemia, a heterogeneous group of blood cancers, poses significant challenges to global health due to its complexity, diverse risk factors, and variable outcomes. Accurate and early diagnosis is critical but remains a significant hurdle, particularly in low-resource settings. Recent advancements in artificial intelligence (AI) and image processing offer transformative solutions to improve leukemia detection and classification, addressing limitations in traditional diagnostic methods. METHODS: This study systematically reviewed over 25,000 scientific articles sourced from Scopus,

Cancer and Obesity: An Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) 2022.

BackgroundThis Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) provides an overview of cancer and increased body fat.MethodsThe scientific information for this CPS is based upon published scientific citations, clinical perspectives of OMA authors, and peer review by the Obesity Medicine Association leadership.ResultsTopics include the increased risk of cancers among patients with obesity, cancer risk factor population-attributable fractions, genetic and epigenetic links between obesity and cancer, adiposopathic and mechanistic processes accounting for increased cancer risk

Унификация одиссеи: искусственный интеллект для диагностирования и лечения редких заболеваний.

Подытожить текущие проблемы в диагностике и лечении редких заболеваний (РД), осветить последние достижения в области искусственного интеллекта (АИ) для РД и предложить модель будущего состояния лечения пациентов РД. Многодисциплинарный экспертный обзор с кратким изложением современных практических задач и мер по ограничению ставок в области РД-клиентов с учетом ключевых клинических и исследовательских соображений в отношении нормативных и экономических ограничений. 10,000 известные РД оказывают коллективное воздействие 1 В 10 Американцы, в целом 30 million Люди. Ежегодно на РД приходится свыше