AIMC Topic: Machine Learning

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A Visual Analytics Framework for Assessing Interactive AI for Clinical Decision Support.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Human involvement remains critical in most instances of clinical decision-making. Recent advances in AI and machine learning opened the door for designing, implementing, and translating interactive AI systems to support clinicians in decision-making....

Session Introduction: AI and Machine Learning in Clinical Medicine: Generative and Interactive Systems at the Human-Machine Interface.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Artificial Intelligence (AI) technologies are increasingly capable of processing complex and multilayered datasets. Innovations in generative AI and deep learning have notably enhanced the extraction of insights from both unstructured texts, images, ...

SG-ML-PLAP: A structure-guided machine learning-based scoring function for protein-ligand binding affinity prediction.

Protein science : a publication of the Protein Society
Computational methods to predict binding affinity of protein-ligand complex have been used extensively to design inhibitors for proteins selected as drug targets. In recent years machine learning (ML) is being increasingly used for design of drugs/in...

Establishment and Validation of the Novel Necroptosis-related Genes for Predicting Stemness and Immunity of Hepatocellular Carcinoma Machine-learning Algorithm.

Combinatorial chemistry & high throughput screening
BACKGROUND: Necroptosis, a recently identified mechanism of programmed cell death, exerts significant influence on various aspects of cancer biology, including tumor cell proliferation, stemness, metastasis, and immunosuppression. However, the role o...

Evaluation of temporomandibular joint disc displacement with MRI-based radiomics analysis.

Dento maxillo facial radiology
OBJECTIVES: The purpose of this study was to propose a machine learning model and assess its ability to classify temporomandibular joint (TMJ) disc displacements on MR T1-weighted and proton density-weighted images.

Combining Real-Time Neuroimaging With Machine Learning to Study Attention to Familiar Faces During Infancy: A Proof of Principle Study.

Developmental science
Looking at caregivers' faces is important for early social development, and there is a concomitant increase in neural correlates of attention to familiar versus novel faces in the first 6 months. However, by 12 months of age brain responses may not d...

Beyond AlphaFold2: The Impact of AI for the Further Improvement of Protein Structure Prediction.

Methods in molecular biology (Clifton, N.J.)
Protein structure prediction is fundamental to molecular biology and has numerous applications in areas such as drug discovery and protein engineering. Machine learning techniques have greatly advanced protein 3D modeling in recent years, particularl...

Machine Learning Techniques to Infer Protein Structure and Function from Sequences: A Comprehensive Review.

Methods in molecular biology (Clifton, N.J.)
The elucidation of protein structure and function plays a pivotal role in understanding biological processes and facilitating drug discovery. With the exponential growth of protein sequence data, machine learning techniques have emerged as powerful t...

Machine learning-based prediction models in medical decision-making in kidney disease: patient, caregiver, and clinician perspectives on trust and appropriate use.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: This study aims to improve the ethical use of machine learning (ML)-based clinical prediction models (CPMs) in shared decision-making for patients with kidney failure on dialysis. We explore factors that inform acceptability, interpretabi...

Comparison of six natural language processing approaches to assessing firearm access in Veterans Health Administration electronic health records.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Access to firearms is associated with increased suicide risk. Our aim was to develop a natural language processing approach to characterizing firearm access in clinical records.