AIMC Topic: Machine Learning

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Neural Networks for Survival Prediction in Medicine Using Prognostic Factors: A Review and Critical Appraisal.

Computational and mathematical methods in medicine
Survival analysis deals with the expected duration of time until one or more events of interest occur. Time to the event of interest may be unobserved, a phenomenon commonly known as right censoring, which renders the analysis of these data challengi...

Imputation of missing monthly rainfall data using machine learning and spatial interpolation approaches in Thale Sap Songkhla River Basin, Thailand.

Environmental science and pollution research international
Missing rainfall data has been a prevalent issue and primarily interested in hydrology and meteorology. This research aimed to examine the capability of machine learning (ML) and spatial interpolation (SI) methods to estimate missing monthly rainfall...

One Size Does Not Fit All: Methodological Considerations for Brain-Based Predictive Modeling in Psychiatry.

Biological psychiatry
Psychiatric illnesses are heterogeneous in nature. No illness manifests in the same way across individuals, and no two patients with a shared diagnosis exhibit identical symptom profiles. Over the last several decades, group-level analyses of in vivo...

BAT: Block and token self-attention for speech emotion recognition.

Neural networks : the official journal of the International Neural Network Society
Transformers have achieved great success in many artificial intelligence fields, such as computer vision (CV), audio processing and natural language processing (NLP). In speech emotion recognition (SER), transformer-based architectures usually comput...

Ligand Unbinding Pathway and Mechanism Analysis Assisted by Machine Learning and Graph Methods.

Journal of chemical information and modeling
We present two methods to reveal protein-ligand unbinding mechanisms in biased unbinding simulations by clustering trajectories into ensembles representing unbinding paths. The first approach is based on a contact principal component analysis for red...

Machine Learning Enhanced Optical Spectroscopy for Disease Detection.

The journal of physical chemistry letters
Optical spectroscopy plays an important role in disease detection. Improving the sensitivity and specificity of spectral detection has great importance in the development of accurate diagnosis. The development of artificial intelligence technology pr...

Analysis of the Human Protein Atlas Weakly Supervised Single-Cell Classification competition.

Nature methods
While spatial proteomics by fluorescence imaging has quickly become an essential discovery tool for researchers, fast and scalable methods to classify and embed single-cell protein distributions in such images are lacking. Here, we present the design...

Artificial intelligence-informed planning for the rapid response of hazard-impacted road networks.

Scientific reports
Post-hazard rapid response has emerged as a promising pathway towards resilient critical infrastructure systems (CISs). Nevertheless, it is challenging to scheme the optimal plan for those rapid responses, given the enormous search space and the hard...

Generative and interpretable machine learning for aptamer design and analysis of in vitro sequence selection.

PLoS computational biology
Selection protocols such as SELEX, where molecules are selected over multiple rounds for their ability to bind to a target of interest, are popular methods for obtaining binders for diagnostic and therapeutic purposes. We show that Restricted Boltzma...