Ten quick tips for harnessing the power of ChatGPT in computational biology

PLoS Computational Biology, Год журнала: 2023, Номер 19(8), С. e1011319 - e1011319

Опубликована: Авг. 10, 2023

The rise of advanced chatbots, such as ChatGPT, has stirred excitement and curiosity in the scientific community.Powered by large language models (LLMs) based on generative pretrained transformers (GPTs)-specifically GPT-3.5 GPT-4-ChatGPT is considered a general-purpose technology with potential to impact job market research endeavors numerous fields [1].Although similar have been fine-tuned for biology-specific projects, including text-based analysis biological sequence decoding [2,3], ChatGPT provides natural interface bioinformaticians begin using LLMs their activities.This tool already accelerating various activities undertaken computational biologists, ranging from data cleaning interpreting results publishing.However, great power comes responsibility.As scientists, we must harness full while adhering ethical guidelines avoiding pitfalls associated technology.Here, provide 10 insightful tips designed help biologists optimize workflows basic prompts more techniques.Although our primary focus current ChatGPT/GPT-4 model, believe that these will remain relevant future iterations technology, well other chatbots (such Meta's LLaMa Google's Bard) [4,5].We invite you explore (summarized Fig 1) aimed at effectively utilizing advance biology maintaining strong commitment integrity. Tip 1: Embrace be ready noveltyChatGPT, powerful coding academic writing tasks, rapidly gaining traction community.While exercising critical judgment not blindly accepting everything it produces important, incorporating into your workflow can undoubtedly

Язык: Английский

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Eight quick tips for biologically and medically informed machine learning

PLoS Computational Biology, Год журнала: 2025, Номер 21(1), С. e1012711 - e1012711

Опубликована: Янв. 9, 2025

Machine learning has become a powerful tool for computational analysis in the biomedical sciences, with its effectiveness significantly enhanced by integrating domain-specific knowledge. This integration give rise to informed machine learning, contrast studies that lack domain knowledge and treat all variables equally (uninformed learning). While application of bioinformatics health informatics datasets more seamless, likelihood errors also increased. To address this drawback, we present eight guidelines outlining best practices employing methods sciences. These quick tips offer recommendations on various aspects analysis, aiming assist researchers generating robust, explainable, dependable results. Even if originally crafted these simple suggestions novices, believe they are deemed relevant expert as well.

Язык: Английский

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Machine Learning Predicts Bleeding Risk in Atrial Fibrillation Patients on Direct Oral Anticoagulant

The American Journal of Cardiology, Год журнала: 2025, Номер unknown

Опубликована: Фев. 1, 2025

Predicting major bleeding in non-valvular atrial fibrillation (AF) patients on direct oral anticoagulants (DOACs) is crucial for personalized care. Alternatives like left appendage closure devices lower stroke risk with fewer non-procedural bleeds. This study compares machine learning (ML) models conventional scores (HAS-BLED, ORBIT, and ATRIA) predicting events requiring hospitalization AF DOACs at their index cardiologist visit. retrospective cohort used electronic health records from 2010-2022 the University of Pittsburgh Medical Center. It included 24,468 (age ≥18) DOACs, excluding those prior significant or warfarin use. The primary outcome was within one year, follow-up one, two, five years. ML algorithms (logistic regression, classification trees, random forest, XGBoost, k-nearest neighbor, naïve Bayes) were compared performance. Of patients, 553 (2.3%) had 829 (3.5%) two years, 1,292 (5.8%) outperformed HAS-BLED, ATRIA, ORBIT 1-year predictions. forest model achieved an AUC 0.76 (0.70-0.81), G-Mean 0.67, net reclassification 0.14 to HAS-BLED's 0.57 (p<0.001). showed superior results across all timepoints hemorrhagic stroke. SHAP analysis identified new factors, including BMI, cholesterol profile, insurance type. In conclusion, demonstrated improved performance uncovered novel offering potential more assessment DOACs.

Язык: Английский

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Ten quick tips for avoiding pitfalls in multi-omics data integration analyses

PLoS Computational Biology, Год журнала: 2023, Номер 19(7), С. e1011224 - e1011224

Опубликована: Июль 6, 2023

Data are the most important elements of bioinformatics: Computational analysis bioinformatics data, in fact, can help researchers infer new knowledge about biology, chemistry, biophysics, and sometimes even medicine, influencing treatments therapies for patients. Bioinformatics high-throughput biological data coming from different sources be more helpful, because each these chunks provide alternative, complementary information a specific phenomenon, similar to multiple photos same subject taken angles. In this context, integration gets pivotal role running successful study. last decades, originating proteomics, metabolomics, metagenomics, phenomics, transcriptomics, epigenomics have been labelled -omics as unique name refer them, omics has gained importance all areas. Even if is useful relevant, due its heterogeneity, it not uncommon make mistakes during phases. We therefore decided present ten quick tips perform an correctly, avoiding common we experienced or noticed published studies past. designed our guidelines beginners, by using simple language that (we hope) understood anyone, believe recommendations should into account bioinformaticians performing integration, including experts.

Язык: Английский

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Machine Learning: A Suitable Method for Biocatalysis

Catalysts, Год журнала: 2023, Номер 13(6), С. 961 - 961

Опубликована: Июнь 1, 2023

Biocatalysis is currently a workhorse used to produce wide array of compounds, from bulk fine chemicals, in green and sustainable manner. The success biocatalysis largely thanks an enlargement the feasible chemical reaction toolbox. This materialized due major advances enzyme screening tools methods, together with high-throughput laboratory techniques for biocatalyst optimization through engineering. Therefore, enzyme-related knowledge has significantly increased. To handle large number data now available, computational approaches have been gaining relevance biocatalysis, among them machine learning methods (MLMs). MLMs use algorithms learn improve experience automatically. review intends briefly highlight contribution within biochemical engineering bioprocesses present key aspects scope related fields, mostly readers non-skilled mind. Accordingly, brief overview basic concepts underlying are presented. complemented steps build model followed by insights into types intelligently analyse data, identify patterns develop realistic applications bioprocesses. Notwithstanding, given this review, some recent illustrative examples protein engineering, production, formulation provided, future developments suggested. Overall, it envisaged that will provide how these assets more efficient biocatalysis.

Язык: Английский

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Efficient management of pulmonary embolism diagnosis using a two-step interconnected machine learning model based on electronic health records data

Health Information Science and Systems, Год журнала: 2024, Номер 12(1)

Опубликована: Март 6, 2024

Язык: Английский

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Role of Artificial Intelligence in Multinomial Decisions and Preventative Nutrition in Alzheimer's Disease

Molecular Nutrition & Food Research, Год журнала: 2024, Номер 68(13)

Опубликована: Янв. 4, 2024

Abstract Alzheimer's disease (AD) affects 50 million people worldwide, an increase of 35 since 2015, and it is known for memory loss cognitive decline. Considering the morbidity associated with AD, important to explore lifestyle elements influencing chances developing special emphasis on nutritional aspects. This review will first discuss how dietary factors have impact in AD development possible role Artificial Intelligence (AI) Machine Learning (ML) preventative care patients through nutrition. The Mediterranean‐DASH diets provide individuals many nutrient benefits which assists prevention neurodegeneration by having neuroprotective roles. Lack micronutrients, protein‐energy, polyunsaturated fatty acids chance decline, memory, synaptic dysfunction among others. ML software has ability design models algorithms from data introduced present practical solutions that are accessible easy use. It can give predictions a precise medicine approach evaluate as whole. There no doubt future science lies customizing reduce dementia risk factors, maintain overall health brain function.

Язык: Английский

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Development and Application of an Early Prediction Model for Risk of Bloodstream Infection based on Real-world Study

Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Фев. 3, 2025

Background Bloodstream Infection (BSI) is a severe systemic infectious disease that can lead to sepsis and Multiple Organ Dysfunction Syndrome (MODS), resulting in high mortality rates posing major public health burden globally. Early identification of BSI crucial for effective intervention, reducing mortality, improving patient outcomes. However, existing diagnostic methods are flawed by low specificity, long detection times demands on testing platforms. The development artificial intelligence provides new approach early identification. This study aims explore the optimal combination routine laboratory data clinical monitoring indicators, utilize machine learning algorithms construct an early, rapid, universally applicable risk prediction model, assist diagnosis practice. Methods Clinical 2582 suspected patients admitted Chongqing University Central Hospital, from January 1, 2021 December 31, 2023 were collected this study. divided into modeling dataset external validation based chronological order, while was further training set internal set. occurrence rate BSI, distribution pathogens, microbial primary reporting time analyzed within During feature selection stage, univariate regression ML applied. First, Univariate logistic used screen predictive factors BSI. Then, Boruta algorithm, Lasso regression, Recursive Feature Elimination with Cross-validation (RFE-CV) employed determine predictors predicting Based combination, six model. best model selected models’ performance, Shapley Additive Explanations (SHAP) method explain evaluate performance generalizability research findings ultimately applied Results incidence among inpatients at Hospital 12.91%. Following selection, 5 variables determined, including white blood cell count, standard bicarbonate, base excess extracellular fluid, interleukin-6, body temperature. models constructed using algorithms, XGBoost demonstrating achieving AUC value 0.782 0.776 made publicly available as online webpage tool use. Conclusions successfully identified features analyzing indicators hospitalized patients. set, learning-based constructed. capable rapid differentiation between non-BSI inclusion minimal enhances its applicability settings, particularly care level. To improve model’s real-world more convenient use, application could greatly efficiency patients’ mortality.

Язык: Английский

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Forecasting the River Ice Break-Up Date in the Upper Reaches of the Heilongjiang River Based on Machine Learning

Water, Год журнала: 2025, Номер 17(3), С. 434 - 434

Опубликована: Фев. 4, 2025

Ice-jam floods (IJFs) are a significant hydrological phenomenon in the upper reaches of Heilongjiang River, posing substantial threats to public safety and property. This study employed various feature selection techniques, including Pearson correlation coefficient (PCC), Grey Relational Analysis (GRA), mutual information (MI), stepwise regression (SR), identify key predictors river ice break-up dates. Based on this, we constructed machine learning models, Extreme Gradient Boosting (XGBoost), Backpropagation Neural Network (BPNN), Random Forest (RF), Support Vector Regression (SVR). The results indicate that reserves Oupu Heihe section have most impact date section. Additionally, accumulated temperature during period average before identified as features closely related river’s opening all four methods. choice method notably impacts performance models predicting Among tested, XGBoost with PCC-based achieved highest accuracy (RMSE = 2.074, MAE 1.571, R2 0.784, NSE 0.756, TSS 0.950). provides more accurate effective for dates, offering scientific basis preventing managing IJF disasters.

Язык: Английский

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Basics of machine learning

Elsevier eBooks, Год журнала: 2025, Номер unknown, С. 29 - 63

Опубликована: Янв. 1, 2025

Язык: Английский

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