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Artificial Intelligence in the Life Sciences
Scopus
ISSN 2667-3185 · Elsevier · 152 artikel di indeks
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Artikel Jurnal
Performance assessment strategies for language model applications in healthcare
Language models (LMs) represent an emerging paradigm within artificial intelligence, with applications throughout the medical enterprise. A comprehensive understanding of the clinical task and awareness of the variability in performance when implemented in actual clinical environments lays the…
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Artikel Jurnal
The comfort of automation: why cognitive sovereignty matters in AI-driven life sciences
The integration of artificial intelligence (AI) into the life sciences is radically transforming research, clinical diagnosis, and therapeutic development processes, redefining the relationship between knowledge, decision-making, and responsibility. Advanced tools, from generative models to…
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Artikel Jurnal
Drug sensitivity prediction across cancer cell lines with a focus on sarcoma
Screening of drugs on cancer cell lines is a first step in the search for compounds leading to cancer cell death, providing the basis for follow-up investigations. Cell line-based drug sensitivity data can be used for predictive modeling, for example, in the context of drug repurposing. In this…
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CLOVER: A framework for benchmarking synthetic data generation methods balancing utility and privacy in healthcare
Background: Synthetic data enables open and efficient medical research by enhancing real-world data. We examined the utility-privacy trade-off of synthetic data generated with and without differential privacy (DP) using CLOVER, a novel open-source Python library that we have developed. Methods: We…
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Understanding biology with machine learning: compression, intelligibility, and dependency
Machine learning (ML) is increasingly used to interrogate biological systems whose complexity resists law-like, deductive explanation. As a result, embeddings, clusters, and attributions are often overinterpreted, dependencies are left implicit, and claims about explainability are often…
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Long short-term memory-based chemical language models for bioactive molecular generation using tailored pre-training datasets
Chemical language models (CLMs) computationally generate molecules as line notations, and their usefulness has been demonstrated in various applications. A common approach to producing target-specific molecules is to pre-train a CLM on a large molecular library and then fine-tune it on a dataset of…
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Artikel Jurnal
Integrating clinical evidence for multi-condition care
Clinical guidelines typically focus on single diseases and often overlook the complex comorbidities emerging in the aftermath of COVID-19. As growing evidence links COVID-19 to long-term neurological outcomes, integrative approaches are required to uncover cross-disease relationships. We present a…
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Artikel Jurnal
PATHOS: Predicting variant pathogenicity by combining protein language models and biological features
Predicting the pathogenic impact of missense variants is essential for understanding and diagnosing genetic diseases. These approaches have undergone significant evolution, with the latest methodologies based on deep learning approaches. Nonetheless, only a limited number use the potential of…
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Artikel Jurnal
CDTA: Consistency-based deep learning method for drug-target affinity prediction using sequential features from protein language model
Accurate drug-target affinity prediction is a critical phase for accelerating and reducing the failure rate of the drug discovery process. It is more challenging in the absence of reliable 3D structural information. Even though protein 3D structure prediction methods have improved, they still lack…
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Single-cell morphological profiling reveals insights into programmed cell death
Analysis at the single-cell level is a powerful approach to study biological processes and responses to perturbations. However, its application in morphological profiling with phenomics remains underexplored. Here, we use the Cell Painting assay to investigate morphological effects of 53 small…
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ISAAC: Prior-aligned structural sensitivity auditing for drug–target interaction models
Deep learning models for drug–target interaction (DTI) prediction often achieve strong benchmark performance while relying on input patterns that are not captured by standard accuracy-based evaluation. We introduce ISAAC (Intervention-based Structural Auditing ApproaCh for Model Evaluation), a…
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Artikel Jurnal
SynthFormer: Equivariant pharmacophore-based generation of synthesizable molecules for ligand-based drug design
Drug discovery is a complex, resource-intensive process requiring significant time and cost to bring new medicines to patients. Many generative models aim to accelerate drug discovery, but few produce synthetically accessible molecules. Conversely, synthesis-focused models do not leverage the 3D…
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From Research to Impact: A Comprehensive Platform for AI Model Deployment in Drug Discovery
The increasing application of machine learning (ML) and artificial intelligence (AI) in drug discovery necessitates scalable solutions for operationalizing these technologies. This paper presents the development and implementation of a cloud-native Machine Learning Operations (MLOps) platform…
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Fine-tuning protein language models enhances the identification and interpretation of the transcription factors
Transcription factors (TFs) are pivotal regulators of gene expression and play essential roles in diverse cellular activities. The three-dimensional organization of the genome and transcriptional regulation are predominantly orchestrated by TFs. By recruiting the transcriptional machinery to gene…
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Development of a deep neural network model for simultaneous analysis of extracellular analyte gradients for a population of cells
Detecting the spatial release of extracellular nitric oxide (NO) is essential for understanding the dynamics in cell communication for physiological and pathological processes. This study presents an innovative methodology that integrates fluorescence-based sensing platforms utilizing single walled…
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The time dimension matters: Improving mode of action classification with live-cell imaging
Morphological profiling is a common approach to investigate the modes of action (MOAs) of compounds. Most methods rely on fixed-cell assays, which provide only a single snapshot at a predefined time point and overlook the dynamic nature of cellular responses. In contrast, live-cell imaging tracks…
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A client-enhanced language model for interactive compound optimization guided by explainable artificial intelligence
Compound optimization is of central relevance in medicinal chemistry. We introduce a new machine learning framework for iterative chemical optimization that integrates compound potency predictions, the explanation of predictions, and generative modeling and that is applicable to individual…
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Artikel Jurnal
Implementing XAI in life sciences: Key challenges and pathways to solutions
The growing adoption of artificial intelligence (AI) in life sciences has been paralleled by growing concerns regarding transparency, interpretability, and trustworthiness of predictive models. While explainable artificial intelligence (XAI) has emerged as a powerful framework to bridge this gap…