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28 papers · ranked by Valyu relevance

01
AarXiv7 Jan 2026
When Predictions Shape Reality: A Socio-Technical Synthesis of Performative Predictions in Machine Learning
Gal Fybish, Teo Susnjak
Machine learning models are increasingly used in high-stakes domains where their predictions can actively shape the environments in which they operate, a phenomenon known as performative prediction. This dynamic, in which the deployment of the model influences the very outcome it seeks to predict, can lead to…
87relevance
02
AarXiv25 Oct 2025
Machine Learning Enabled Early Warning System For Financial Distress Using Real-Time Digital Signals
pant, Laxmi, Reza, Syed Ali + 16 more
The growing instability of both global and domestic economic environments has increased the risk of financial distress at the household level. However, traditional econometric models often rely on delayed and aggregated data, limiting their effectiveness. This study introduces a machine learning-based early warning…
83relevance
03
AarXiv30 Nov 2025
A Benchmark of Causal vs. Correlation AI for Predictive Maintenance
Shaunak Dhande, Ma, Chutian, Saggese + 3 more
Predictive maintenance in manufacturing environments presents a challenging optimization problem characterized by extreme cost asymmetry, where missed failures incur costs roughly fifty times higher than false alarms. Conventional machine learning approaches typically optimize statistical accuracy metrics that do not…
81relevance
04
PPubMed19 Jan 2026
Predictive models in obstetrics: Lessons from the celestial <i> N </i> ‐body problem
Jeremy Boujenah, Patrick Rozenberg
1## INTRODUCTION The rise of personalized medicine, the growing autonomy of patients in therapeutic decision-making, and increasing medico-legal pressures have intensified the desire to “predict the future” and fueled the search for reliable predictive models. In obstetrics, more than 100 models have been developed to…
79relevance
05
CchemRxiv9 Jan 2026
Toward On-the-Fly Prediction of Reaction Energetics for High-Throughput Screening
Authors not listed
Machine learning is increasingly used to predict reaction properties such as barrier heights, reaction energies, rates, or yields, as well as the underlying molecular geometries, including transition state structures. While such predictions have the potential to provide mechanistic insight for high-impact applications…
78relevance
06
BbioRxiv14 Oct 2025
System-level health profiling from blood DNA methylation with explainable deep learning
David Martínez-Enguita, Thomas Hillerton, Julia Åkesson, Maria Lerm + 1 more
Genome-scale DNA methylation (DNAm) profiles capture organismal physiology, but most predictive models lack transparency and multi-level applicability. Here we develop an explainable framework that quantifies respiratory, cardiovascular, and metabolic status as bounded health scores (0–1) derived from sex-specific…
77relevance
07
BbioRxiv24 Jun 2026
Enhanced Prediction of Gut Microbiome–Related Diseases Using Hybrid Machine Learning Models
Sathya Abhijna Marisetti, Prathit Chatterjee, U. Deva Priyakumar
The human gut, containing 100 trillion microbes, is also considered the “second brain,” having control over the different functions of the physiological system. With advancements in bioinformatics and the development of sequencing technologies, researchers are able to explore the diversity and functional implications…
77relevance
08
AarXiv21 May 2026
Explainable AI for Data-Driven Design of High-Dimensional Predictive Studies
Junyu Yan, Damian Machlanski, Kurt Butler, Panagiotis Dimitrakopoulos + 3 more
Predictive modelling is important for health data analysis and data-driven clinical decision-making. However, predictive studies are challenging to design optimally by hand when tens or even hundreds of features require selection, transformation, or interaction modelling. While complex machine learning models offer…
76relevance
09
CchemRxiv7 Oct 2025
Robust Learning from Literature Data: Model Generalizability and Uncertainty for Predicting Conjugated Polymer Solution Conformation
Authors not listed
Predicting solution conformation and aggregation of conjugated polymers remains a bottleneck for translating solution processing into controlled film microstructure and for closing the loop in self-driving laboratories. We construct a cleaned, machine-readable dataset of 256 entries that links polymer size…
73relevance
10
PPubMed9 Jun 2026
Methodological guidance on clinical prediction models in mental health research
Raquel Costa, Bruno de Sousa, Thomas Kneib, Rui Martins + 1 more
Clinical prediction models play a crucial role in advancing personalized care for mental health disorders, providing essential insights for diagnosis, prognosis and intervention planning. This work examines the current methodological approaches used to develop such models, emphasizing their application to mental health…
73relevance
11
AarXiv28 Nov 2025
Machine learning for violence prediction: a systematic review and critical appraisal
Kozhevnikova, Stefaniya, Yukhnenko, Denis + 3 more
We systematically searched nine bibliographic databases and Google Scholar up to September 2025 for development and/or validation studies on machine learning methods for predicting all forms of violent behaviour. We synthesised the results by summarising discrimination and calibration performance statistics and…
73relevance
12
BbioRxiv1 Jul 2026
Tabular Foundation Models Are Competitive Cellular Perturbation Predictors Across Biological Scales
Giovanni Palla, Alexander Hillsley, Yang-Joon Kim, Loic A. Royer
Predicting how cells respond to genetic and chemical perturbations is a central challenge in drug discovery and functional genomics. A growing ecosystem of specialized single-cell foundation models has been developed to address this problem, yet their practical advantage over domain-agnostic approaches remains unclear.…
73relevance
13
BbioRxiv19 Feb 2026
Foundation Models Improve Perturbation Response Prediction
Elijah Cole, Geert-Jan Huizing, Sohan Addagudi, Nicholas Ho + 16 more
Predicting cellular responses to genetic or chemical perturbations has been a long-standing goal in biology. Recent applications of foundation models to this task have yielded contradictory results regarding their superiority over simple baselines. We conducted an extensive analysis of over 600 different models across…
73relevance
14
BbioRxiv25 Aug 2026
Artificial intelligence model: optimizing cancer risk level predictions using machine learning and deep learning approaches
Areen Arabiat, Hamza Abu Owida, Suhaila Abuowaida, Nawaf Alshdaifat + 2 more
This study emphasizes the potential of computational techniques in cancer risk assessment, highlighting opportunities for specific and data-driven healthcare solutions. It examines the use of artificial intelligence (AI), machine learning (ML), and deep learning (DL) approaches to improve cancer risk assessment using a…
73relevance
15
BbioRxiv10 Jun 2026
The Conductome: A Bayesian Classifier Approach to Predicting and Understanding Behaviour
Santiago Herce Castañon, Christopher R. Stephens
Predicting and understanding behaviour is a primary objective of many disciplines, especially human behaviour, as it is the cause of many of the world’s most pressing problems. Although it is a fundamental concept in multiple disciplines, there is no agreed operational definition of what it is. Neither is there a…
72relevance
16
CchemRxiv17 Oct 2025
Positivity is All You Need (PAYN): A PU Learning Framework for Yield Prediction in Organic Chemistry
Authors not listed
Accurately predicting chemical reaction yields in silico is a long-standing goal in organic chemistry that, if achieved, would revolutionize synthesis design, op-timization, and discovery. The vast reaction data within scientific literature rep-resents a rich resource for training predictive machine learning models…
72relevance
17
PPubMed8 Dec 2025
Padding interpolation, median imputation, RobustScalar, and particle swarm optimization with heterogeneous classifiers: a robust combination for effective heart disease diagnosis
Sanjay Dhanka, Ankur Kumar, Surita Maini, Nitin Kumar + 4 more
Introduction Heart disease is a leading cause of death worldwide, necessitating accurate early diagnosis. Although machine learning (ML) shows potential for this task, many current models are hindered by data inconsistencies, poor feature selection, and limited robustness. Methods This study proposes a novel, robust…
71relevance
18
PPubMed10 Jun 2026
Regression based hybrid machine learning model performance evaluation on air quality index prediction in Kolkata
Muni Lakshmi G K, Mokesh Rayalu G
Air pollution, especially elevated particulate matter concentrations, presents a substantial risk to public health and environmental sustainability in urban regions. By employing machine learning and hybrid ensemble models, this study develops a robust frame work for predicting the Air Quality Index (AQI). A multi-step…
70relevance
19
CchemRxiv2 Dec 2025
Towards Generalizable Data-Driven Pharmacokinetics with Interpretable Neural ODEs
Authors not listed
Accurate modeling of drug concentration--time (C--t) profiles is central to pharmacokinetics (PK) and plays a critical role in both early-stage compound selection and late-stage individualized dosing. Traditional PK model offer mechanistic interpretability but often rely on rigid assumptions, extensive…
70relevance
20
CchemRxiv28 Oct 2025
A Vacancy-Driven Deep Learning Framework for Predicting and Interpreting OER Activity in High-Entropy Layered Double Hydroxides
Authors not listed
High-entropy layered double hydroxides (HE-LDHs) have shown great potential in oxygen evolution reaction (OER) catalysis due to their tunable compositions and electronic structures. However, the synergistic effects between multiple vacancies, such as metal and oxygen vacancies, remain poorly understood and challenging…
70relevance
21
AarXiv15 Dec 2025
Heart Disease Prediction using Case Based Reasoning (CBR)
Mohaiminul Islam Bhuiyan, Chan Hue Wah, Nur Shazwani Kamarudin, Nur Hafieza Ismail + 1 more
This study provides an overview of heart disease prediction using an intelligent system. Predicting disease accurately is crucial in the medical field, but traditional methods relying solely on a doctor's experience often lack precision. To address this limitation, intelligent systems are applied as an alternative to…
69relevance
22
AarXiv25 Jun 2026
Explainable Ensemble-Based Machine Learning Models for Detecting the Presence of Cirrhosis in Hepatitis C Patients
Abrar Alotaibi, Lujain Alnajrani, Nawal Alsheikh, Alhatoon Alanazy + 4 more
Hepatitis C is a liver infection caused by a virus, which results in mild to severe inflammation of the liver. Over many years, hepatitis C gradually damages the liver, often leading to permanent scarring, known as cirrhosis. Patients sometimes have moderate or no symptoms of liver illness for decades before developing…
69relevance
23
CchemRxiv18 Nov 2025
High throughput tight binding calculation of electronic HOMO-LUMO gaps and its prediction for natural compounds
Authors not listed
This research investigates predicting the Highest Occupied Molecular Orbital and the Lowest Unoccupied Molecular Orbital (HOMO-LUMO; short HL) gap of natural compounds, a crucial property for understanding molecular electronic behavior relevant to cheminformatics and materials science. To address the high computational…
69relevance
24
CchemRxiv30 Oct 2025
Causal Descriptors in QSAR: Deconfounding High-Dimensional Molecular Features via Double Machine Learning and Hypothesis Testing
Authors not listed
Quantitative Structure-Activity Relationship (QSAR) modeling is a pillar of computational drug discovery. However, standard machine learning (ML) models are often confounded by the high-dimensional and intensely correlated nature of molecular descriptors. A model may identify a "bulk" property (e.g., molecular weight)…
68relevance
25
PPubMed24 Apr 2026
Machine-Learning Models Outperform Clinicians in Predicting Postnatal Growth Failure Among Very Low Birth Weight Infants
Joohee Lim, Sook Hyun Park, Teahyen Cha, So Jin Yoon + 7 more
Background/Objectives: Early detection of postnatal growth failure (PGF) is essential for optimizing nutritional management in preterm infants, as PGF is associated with adverse neurodevelopmental outcomes. Early prediction remains difficult because postnatal growth is influenced by multiple clinical factors including…
68relevance
26
PPubMed15 Dec 2025
Interpretable machine learning for predicting placental abruption in early-onset preeclampsia: model development and evaluation
Lijun Su, Jingli Zhang, Haiying Wu
Objective Early-onset preeclampsia (EOPE) represents a particularly severe clinical subtype of preeclampsia (PE) and is frequently complicated by placental abruption, which can result in serious maternal and fetal morbidity or mortality. This study aimed to develop and validate an interpretable machine learning (IML)…
68relevance
27
BbioRxiv27 Oct 2025
Integrating Genomics into Multimodal EHR Foundation Models
Jonathan Amar, Edward Liu, Alessandra Breschi, Liangliang Zhang + 12 more
This paper introduces an innovative Electronic Health Record (EHR) foundation model that integrates Polygenic Risk Scores (PRS) as a foundational data modality, moving beyond traditional EHR-only approaches to build more holistic health profiles. Leveraging the extensive and diverse data from the All of Us (AoU)…
67relevance
28
CchemRxiv18 Dec 2025
Mechanistically Constrained Multi-Fidelity Scaling Enables Accurate 100 m³ Fermentation Predictions from Only Three Calibration Volumes
Authors not listed
Accurate extrapolation in data-scarce scientific systems remains a central challenge for machine intelligence. In microbial bioprocessing, kinetic parameters change non-monotonically with reactor volume due to interacting hydrodynamic, oxygen-transfer, and mixing effects, rendering classical empirical scaling laws…
65relevance

Results for predictive models

When Predictions Shape Reality: A Socio-Technical Synthesis of Performative Predictions in Machine Learning

Machine Learning Enabled Early Warning System For Financial Distress Using Real-Time Digital Signals

A Benchmark of Causal vs. Correlation AI for Predictive Maintenance

Predictive models in obstetrics: Lessons from the celestial <i> N </i> ‐body problem

Toward On-the-Fly Prediction of Reaction Energetics for High-Throughput Screening

System-level health profiling from blood DNA methylation with explainable deep learning

Enhanced Prediction of Gut Microbiome–Related Diseases Using Hybrid Machine Learning Models

Explainable AI for Data-Driven Design of High-Dimensional Predictive Studies

Robust Learning from Literature Data: Model Generalizability and Uncertainty for Predicting Conjugated Polymer Solution Conformation

Methodological guidance on clinical prediction models in mental health research

Machine learning for violence prediction: a systematic review and critical appraisal

Tabular Foundation Models Are Competitive Cellular Perturbation Predictors Across Biological Scales

Foundation Models Improve Perturbation Response Prediction

Artificial intelligence model: optimizing cancer risk level predictions using machine learning and deep learning approaches

The Conductome: A Bayesian Classifier Approach to Predicting and Understanding Behaviour

Positivity is All You Need (PAYN): A PU Learning Framework for Yield Prediction in Organic Chemistry

Padding interpolation, median imputation, RobustScalar, and particle swarm optimization with heterogeneous classifiers: a robust combination for effective heart disease diagnosis

Regression based hybrid machine learning model performance evaluation on air quality index prediction in Kolkata

Towards Generalizable Data-Driven Pharmacokinetics with Interpretable Neural ODEs

A Vacancy-Driven Deep Learning Framework for Predicting and Interpreting OER Activity in High-Entropy Layered Double Hydroxides

Heart Disease Prediction using Case Based Reasoning (CBR)

Explainable Ensemble-Based Machine Learning Models for Detecting the Presence of Cirrhosis in Hepatitis C Patients

High throughput tight binding calculation of electronic HOMO-LUMO gaps and its prediction for natural compounds

Causal Descriptors in QSAR: Deconfounding High-Dimensional Molecular Features via Double Machine Learning and Hypothesis Testing

Machine-Learning Models Outperform Clinicians in Predicting Postnatal Growth Failure Among Very Low Birth Weight Infants

Interpretable machine learning for predicting placental abruption in early-onset preeclampsia: model development and evaluation

Integrating Genomics into Multimodal EHR Foundation Models

Mechanistically Constrained Multi-Fidelity Scaling Enables Accurate 100 m³ Fermentation Predictions from Only Three Calibration Volumes

When Predictions Shape Reality: A Socio-Technical Synthesis of Performative Predictions in Machine Learning

Machine Learning Enabled Early Warning System For Financial Distress Using Real-Time Digital Signals

A Benchmark of Causal vs. Correlation AI for Predictive Maintenance

Predictive models in obstetrics: Lessons from the celestial <i> N </i> ‐body problem

Toward On-the-Fly Prediction of Reaction Energetics for High-Throughput Screening

System-level health profiling from blood DNA methylation with explainable deep learning

Enhanced Prediction of Gut Microbiome–Related Diseases Using Hybrid Machine Learning Models

Explainable AI for Data-Driven Design of High-Dimensional Predictive Studies

Robust Learning from Literature Data: Model Generalizability and Uncertainty for Predicting Conjugated Polymer Solution Conformation

Methodological guidance on clinical prediction models in mental health research

Machine learning for violence prediction: a systematic review and critical appraisal

Tabular Foundation Models Are Competitive Cellular Perturbation Predictors Across Biological Scales

Foundation Models Improve Perturbation Response Prediction

Artificial intelligence model: optimizing cancer risk level predictions using machine learning and deep learning approaches

The Conductome: A Bayesian Classifier Approach to Predicting and Understanding Behaviour

Positivity is All You Need (PAYN): A PU Learning Framework for Yield Prediction in Organic Chemistry

Padding interpolation, median imputation, RobustScalar, and particle swarm optimization with heterogeneous classifiers: a robust combination for effective heart disease diagnosis

Regression based hybrid machine learning model performance evaluation on air quality index prediction in Kolkata

Towards Generalizable Data-Driven Pharmacokinetics with Interpretable Neural ODEs

A Vacancy-Driven Deep Learning Framework for Predicting and Interpreting OER Activity in High-Entropy Layered Double Hydroxides

Heart Disease Prediction using Case Based Reasoning (CBR)

Explainable Ensemble-Based Machine Learning Models for Detecting the Presence of Cirrhosis in Hepatitis C Patients

High throughput tight binding calculation of electronic HOMO-LUMO gaps and its prediction for natural compounds

Causal Descriptors in QSAR: Deconfounding High-Dimensional Molecular Features via Double Machine Learning and Hypothesis Testing

Machine-Learning Models Outperform Clinicians in Predicting Postnatal Growth Failure Among Very Low Birth Weight Infants

Interpretable machine learning for predicting placental abruption in early-onset preeclampsia: model development and evaluation

Integrating Genomics into Multimodal EHR Foundation Models

Mechanistically Constrained Multi-Fidelity Scaling Enables Accurate 100 m³ Fermentation Predictions from Only Three Calibration Volumes