Diabetes Prediction Through Linkage of Causal Discovery and Inference Model with Machine Learning Models
Predicting diabetes and identifying causal risk factors from patient health data, to understand contributors to a growing global disease.
Papers
1,138 of 1,138 verified cases.
De novo protein design—From new structures to programmable functions
Biology & Life Sciences
Protein Structure and Dynamics
partially solved
importance 8/10
scale stated
Designing new proteins with shapes and molecular functions not found in nature, requiring precise control over conformation and molecular recognition.
Predicting sepsis onset in ICU using machine learning models: a systematic review and meta-analysis
Sepsis in ICU patients is often recognized too late, and early recognition is crucial for effective treatment. The study systematically evaluated how well machine learning models predict sepsis onset.
Illuminating protein space with a programmable generative model
The space of possible protein molecules is vastly larger than the space of functional ones, making it hard for computation and experiments to access proteins with desired properties and functions.
Accurate medium-range global weather forecasting with 3D neural networks
Climate & Environment
Meteorological Phenomena and Simulations
partially solved
importance 8/10
scale stated
Numerical weather prediction is accurate but computationally expensive, while prior AI forecasting methods were much faster but significantly less accurate. The paper aims to make AI-based medium-range global weather forecasting accurate.
Machine Learning Models for Cardiovascular Disease Events Prediction
Cardiovascular diseases cause high mortality worldwide, and early prediction of CVD-related death within ten years could enable prevention.
Early Prediction of Sepsis Based on Machine Learning Algorithm
Sepsis, an infection-driven organ failure with extremely high mortality, needs to be predicted early to improve outcomes.
Genome-Scale Metabolic Models and Machine Learning Reveal Genetic Determinants of Antibiotic Resistance in Escherichia coli and Unravel the Underlying Metabolic Adaptation Mechanisms
Biology & Life Sciences
Microbial Metabolic Engineering and Bioproduction
ai assisted
importance 8/10
scale stated
Understanding which genes drive antibiotic resistance in E. coli and how the bacterium's metabolism adapts to resist drugs.
Deep Learning for SARS COV-2 Genome Sequences
During a novel virus outbreak, authorities must quickly determine whether the epidemic stems from a novel or a known virus; this work addresses classifying SARS-CoV-2 among coronaviruses from genome sequences.
Machine Learning Algorithm Identifies an Antibiotic Vocabulary for Permeating Gram-Negative Bacteria
Many potential antibiotics cannot enter Gram-negative bacteria such as multidrug-resistant Pseudomonas aeruginosa, limiting treatment options.
Using Machine Learning to Predict Synergistic Antimalarial Compound Combinations With Novel Structures
Malaria resistance is developing rapidly, and exhaustively screening large drug spaces for synergistic antimalarial combinations experimentally is infeasible.
Prediction of survival and metastasis in breast cancer patients using machine learning classifiers
Predicting survival and metastasis in breast cancer patients accurately enough to improve early diagnosis and treatment decisions.
Computer Vision Malaria Diagnostic Systems—Progress and Prospects
Malaria diagnosis by microscopy and rapid tests can be slow, inconsistent and inaccurate, contributing to fatalities and drug overuse.
An Artificial Intelligence Analysis of Electrocardiograms for the Clinical Diagnosis of Cardiovascular Diseases: A Narrative Review
Fast, accurate clinical diagnosis of cardiovascular diseases from ECG traces, the most widely used tool in cardiology.
Protein Design Using Structure-Prediction Networks: AlphaFold and RoseTTAFold as Protein Structure Foundation Models
Biology & Life Sciences
Protein Structure and Dynamics
partially solved
importance 8/10
scale stated
Designing proteins with tailored structures and functions is a long-standing unsolved goal in bioengineering.
An explainable machine learning approach for Alzheimer’s disease classification
Medicine & Health
Dementia and Cognitive Impairment Research
ai assisted
importance 8/10
scale stated
Early Alzheimer's diagnosis is difficult because subtle biomarker changes are overlooked and data is high-dimensional with limited samples.
Deep and Transfer Learning Approaches for Automated Early Detection of Monkeypox (Mpox) Alongside Other Similar Skin Lesions and Their Classification
Mpox symptoms resemble chickenpox and measles, causing possible misdiagnosis, and a rapid, automated early diagnosis method is needed.
Self-supervised attention-based deep learning for pan-cancer mutation prediction from histopathology
It was unclear whether deep learning predictions of genetic alterations from tumor histopathology slides generalize to external datasets across cancer types.
Recent Applications of Deep Learning Methods on Evolution- and Contact-Based Protein Structure Prediction
Biology & Life Sciences
Protein Structure and Dynamics
partially solved
importance 8/10
scale stated
Predicting proteins' 3D structures from sequence is difficult; deep learning methods were applied to interpret how sequences and homology govern inter-residue contacts and structural organization.
Comparison of risk models for mortality and cardiovascular events between machine learning and conventional logistic regression analysis
Predicting all-cause mortality and major cardiovascular events (heart failure, coronary syndrome, stroke, hemorrhage) in patients, and comparing machine learning with conventional logistic regression risk models.
Deep learning-based skin lesion analysis using hybrid ResUNet++ and modified AlexNet-Random Forest for enhanced segmentation and classification
Medicine & Health
Cutaneous Melanoma Detection and Management
ai assisted
importance 8/10
scale stated
Early skin cancer detection requires accurate segmentation and classification of skin lesion images, where misdiagnosis can be fatal.
Development and Validation of a Deep Learning Model for Histopathological Slide Analysis in Lung Cancer Diagnosis
Lung cancer diagnosis is often delayed and relies on pathologist review of histopathology slides, contributing to cancer-related deaths.
An Advanced Deep Learning Framework for Multi-Class Diagnosis from Chest X-ray Images
Diagnosing multiple lung diseases (fibrosis, tuberculosis, pneumonia, COVID-19) from chest X-ray images quickly and accurately is difficult and requires expert radiologists.
Deep Learning Pipeline for Accelerating Virtual Screening in Drug Discovery
Drug discovery is slow and costly, and diseases like Marburg virus lack treatments, so faster identification of candidate compounds is needed.