Over the past few years, artificial intelligence (AI) has emerged as a transformative force in drug discovery and development (DDD), revolutionizing many aspects of the process. This survey provides a comprehensive re...
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Over the past few years, artificial intelligence (AI) has emerged as a transformative force in drug discovery and development (DDD), revolutionizing many aspects of the process. This survey provides a comprehensive review of recent advancements in AI applications within early drug discovery and post-market drug assessment. It addresses the identification and prioritization of new therapeutic targets, prediction of drug-target interaction (DTI), design of novel drug-like molecules, and assessment of the clinical efficacy of new medications. By integrating AI technologies, pharmaceutical companies can accelerate the discovery of new treatments, enhance the precision of drug development, and bring more effective therapies to market. This shift represents a significant move towards more efficient and cost-effective methodologies in the DDD landscape.
In recent years, the management and analysis of biological data have experienced exponential growth propelled by the relentless advancement of machine learning (ML) and artificial intelligence (AI) technologies. This ...
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In recent years, the management and analysis of biological data have experienced exponential growth propelled by the relentless advancement of machine learning (ML) and artificial intelligence (AI) technologies. This is driven mainly by the remarkable ability and potentials of AI-based systems to craft sophisticated, yet effective, algorithms and analytical models tailored for the interpretation of biological information;thus, assist in making accurate predictions and/or decisions [1]. The surge in AI adoption is not unfounded;it's a response to the overwhelming increase in both the volume and acquisition rates of biological data.
Legal regulations do not eliminate existing ethical problems related to the use of modern technology in medical devices for disease diagnosis and treatment. Among these problems, some are already well analyzed in the ...
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ISBN:
(纸本)9798350310177
Legal regulations do not eliminate existing ethical problems related to the use of modern technology in medical devices for disease diagnosis and treatment. Among these problems, some are already well analyzed in the literature - they concern the protection of personal data and the occurrence of bias and discrimination. In addition to these, however, there are several other, no less important issues involving the application of artificial intelligence technologies to medical devices. They concern how the technologies used in these devices for the purpose of diagnosis and therapy may replace human intervention and what evaluation criteria they should therefore be subject to, as well as what risks they entail.
Cancer driver genes (CDGs) are crucial in cancer development and are key targets for treatment. This study proposes a novel approach utilizing Graph Neural Networks (GNNs), particularly Graph Convolutional Networks (G...
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The papers in this special section were presented at the 16th International symposium on bioinformatics Research and Applications (ISBRA 2020), which was held virtually, on December 1-4, 2020. The ISBRA symposium prov...
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The papers in this special section were presented at the 16th International symposium on bioinformatics Research and Applications (ISBRA 2020), which was held virtually, on December 1-4, 2020. The ISBRA symposium provides a forum for the exchange of ideas and results among researchers, developers, and practitioners working on all aspects of bioinformatics and computationalbiology and their applications.
Many studies have been conducted with the goal of correctly predicting diagnostic status of a disorder using the combination of genomic data and machine learning. It is often hard to judge which components of a study ...
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Many studies have been conducted with the goal of correctly predicting diagnostic status of a disorder using the combination of genomic data and machine learning. It is often hard to judge which components of a study led to better results and whether better reported results represent a true improvement or an uncorrected bias inflating performance. We extracted information about the methods used and other differentiating features in genomic machine learning models. We used these features in linear regressions predicting model performance. We tested for univariate and multivariate associations as well as interactions between features. Of the models reviewed, 46% used feature selection methods that can lead to data leakage. Across our models, the number of hyperparameter optimizations reported, data leakage due to feature selection, model type, and modeling an autoimmune disorder were significantly associated with an increase in reported model performance. We found a significant, negative interaction between data leakage and training size. Our results suggest that methods susceptible to data leakage are prevalent among genomic machine learning research, resulting in inflated reported performance. Best practice guidelines that promote the avoidance and recognition of data leakage may help the field avoid biased results.
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