Deep Learning and Neural Networks: Decision-Making Implications

Symmetry, Journal Year: 2023, Volume and Issue: 15(9), P. 1723 - 1723

Published: Sept. 8, 2023

Deep learning techniques have found applications across diverse fields, enhancing the efficiency and effectiveness of decision-making processes. The integration these underscores significance interdisciplinary research. In particular, decisions often rely on output’s projected value or probability from neural networks, considering different values relevant output factor. This review examines impact deep systems, analyzing 25 papers published between 2017 2022. highlights improved accuracy but emphasizes need for addressing issues like interpretability, generalizability, to build reliable decision support systems. Future research directions include transparency, explainability, real-world validation, underscoring importance collaboration successful implementation.

Language: Английский

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Expectation management in AI: A framework for understanding stakeholder trust and acceptance of artificial intelligence systems

Heliyon, Journal Year: 2024, Volume and Issue: 10(7), P. e28562 - e28562

Published: March 25, 2024

Language: Английский

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Leveraging a meta-learning approach to advance the accuracy of Nav blocking peptides prediction

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Feb. 23, 2024

Abstract The voltage-gated sodium (Na v ) channel is a crucial molecular component responsible for initiating and propagating action potentials. While the α subunit, forming pore, plays central role in this function, complete physiological function of Na channels relies on interactions between subunit auxiliary proteins, known as protein–protein (PPI). blocking peptides (NaBPs) have been recognized promising alternative therapeutic agent pain itch. Although traditional experimental methods can precisely determine effect activity NaBPs, they remain time-consuming costly. Hence, machine learning (ML)-based that are capable accurately contributing silico prediction NaBPs highly desirable. In study, we develop an innovative meta-learning-based NaBP method (MetaNaBP). MetaNaBP generates new feature representations by employing wide range sequence-based descriptors cover multiple perspectives, combination with powerful ML algorithms. Then, these were optimized to identify informative features using two-step selection method. Finally, selected applied final meta-predictor. To best our knowledge, first meta-predictor prediction. Experimental results demonstrated achieved accuracy 0.948 Matthews correlation coefficient 0.898 over independent test dataset, which 5.79% 11.76% higher than existing addition, discriminative power surpassed conventional both training datasets. We anticipate will be exploited large-scale analysis narrow down potential NaBPs.

Language: Английский

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Advancing the accuracy of tyrosinase inhibitory peptides prediction via a multiview feature fusion strategy

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 8, 2025

Language: Английский

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Using protein language models for protein interaction hot spot prediction with limited data

BMC Bioinformatics, Journal Year: 2024, Volume and Issue: 25(1)

Published: March 16, 2024

Protein language models, inspired by the success of large models in deciphering human language, have emerged as powerful tools for unraveling intricate code life inscribed within protein sequences. They gained significant attention their promising applications across various areas, including sequence-based prediction secondary and tertiary structure, discovery new functional sequences/folds, assessment mutational impact on fitness. However, utility learning to predict residue properties based scant datasets, such protein-protein interaction (PPI)-hotspots whose mutations significantly impair PPIs, remained unclear. Here, we explore feasibility using language-learned representations features machine PPI-hotspots a dataset containing 414 experimentally confirmed 504 PPI-nonhot spots.

Language: Английский

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Open‐source large language models in action: A bioinformatics chatbot for PRIDE database

PROTEOMICS, Journal Year: 2024, Volume and Issue: unknown

Published: March 31, 2024

ABSTRACT We here present a chatbot assistant infrastructure ( https://www.ebi.ac.uk/pride/chatbot/ ) that simplifies user interactions with the PRIDE database's documentation and dataset search functionality. The framework utilizes multiple Large Language Models (LLM): llama2, chatglm, mixtral (mistral), openhermes. It also includes web service API (Application Programming Interface), interface, components for indexing managing vector databases. An Elo‐ranking system‐based benchmark component is included in as well, which allows evaluating performance of each LLM improving documentation. not only users to interact but can be used find datasets using an LLM‐based recommendation system, enabling discoverability. Importantly, while our exemplified through its application database context, modular adaptable nature approach positions it valuable tool experiences across spectrum bioinformatics proteomics tools resources, among other domains. integration advanced LLMs, innovative vector‐based construction, benchmarking framework, optimized collectively form robust transferable infrastructure. open‐source https://github.com/PRIDE‐Archive/pride‐chatbot ).

Language: Английский

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Deep learning methods for protein function prediction

PROTEOMICS, Journal Year: 2024, Volume and Issue: unknown

Published: July 12, 2024

Abstract Predicting protein function from sequence, structure, interaction, and other relevant information is important for generating hypotheses biological experiments studying systems, therefore has been a major challenge in bioinformatics. Numerous computational methods had developed to advance prediction gradually the last two decades. Particularly, recent years, leveraging revolutionary advances artificial intelligence (AI), more deep learning have improve at faster pace. Here, we provide an in‐depth review of developments prediction. We summarize significant field, identify several remaining challenges be tackled, suggest some potential directions explore. The data sources evaluation metrics widely used are also discussed assist machine learning, AI, bioinformatics communities develop cutting‐edge

Language: Английский

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The Role of Generative Artificial Intelligence in Digital Agri-Food

Journal of Agriculture and Food Research, Journal Year: 2025, Volume and Issue: unknown, P. 101787 - 101787

Published: March 1, 2025

Language: Английский

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Life and Completeness in Complex Systems

Understanding complex systems, Journal Year: 2025, Volume and Issue: unknown, P. 121 - 155

Published: Jan. 1, 2025

Language: Английский

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GramSeq-DTA: A Grammar-Based Drug–Target Affinity Prediction Approach Fusing Gene Expression Information

Biomolecules, Journal Year: 2025, Volume and Issue: 15(3), P. 405 - 405

Published: March 12, 2025

Drug–target affinity (DTA) prediction is a critical aspect of drug discovery. The meaningful representation drugs and targets crucial for accurate prediction. Using 1D string-based representations common approach that has demonstrated good results in drug–target However, these lacks information on the relative position atoms bonds. To address this limitation, graph-based have been used to some extent. solely considering structural may be insufficient DTA Integrating functional at genetic level can enhance capability models. fill gap, we propose GramSeq-DTA, which integrates chemical perturbation with targets. We applied Grammar Variational Autoencoder (GVAE) feature extraction utilized two different approaches protein as follows: Convolutional Neural Network (CNN) Recurrent (RNN). data are obtained from L1000 project, provides up-regulation down-regulation genes caused by selected drugs. This processed, compact dataset prepared, serving set By integrating drug, gene, target features model, our outperforms current state-of-the-art models when validated widely datasets (BindingDB, Davis, KIBA). work novel practical merging aspects biological entities, it encourages further research multi-modal

Language: Английский

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