AIMC Topic: Machine Learning

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Machine learning based screening of biomarkers associated with cell death and immunosuppression of multiple life stages sepsis populations.

Scientific reports
Sepsis is a condition resulting from the uncontrolled immune response to infection, leading to widespread inflammatory damage and potentially fatal organ dysfunction. Currently, there is a lack of specific prevention and treatment strategies for seps...

Predicting cancer risk using machine learning on lifestyle and genetic data.

Scientific reports
Cancer remains one of the leading causes of mortality worldwide, where early detection significantly improves patient outcomes and reduces treatment burden. This study investigates the application of Machine Learning (ML) techniques to predict cancer...

Serological proteomic characterization for monitoring liver fibrosis regression in chronic hepatitis B patients on treatment.

Nature communications
Longitudinal serological proteomic dynamics during antiviral therapy (AVT) in chronic hepatitis B (CHB) patients with liver fibrosis remain poorly characterized. Here, using four-dimensional data-independent acquisition mass spectrometry (4D-DIA-MS),...

Analyzing the vulnerabilities in Split Federated Learning: assessing the robustness against data poisoning attacks.

Scientific reports
Distributed Collaborative Machine Learning (DCML) offers a promising alternative to address privacy concerns in centralized machine learning. Split learning (SL) and Federated Learning (FL) are two effective learning approaches within DCML. Recently,...

Machine learning-based construction of Immunogenic cell death-related score for improving prognosis and personalized treatment in glioma.

Scientific reports
Immunogenic cell death (ICD) is capable of activating both innate and adaptive immune responses. In this study, we aimed to develop an ICD-related signature in glioma patients and facilitate the assessment of their prognosis and drug sensitivity. Con...

Discovering action insights from large-scale assessment log data using machine learning.

Scientific reports
This study introduces a novel machine learning algorithm that combines natural language processing techniques, such as Word2Vec and Doc2Vec, with neural networks to identify and validate significant actions within human action sequences. Using the 20...

Predictive modeling of asthma drug properties using machine learning and topological indices in a MATLAB based QSPR study.

Scientific reports
Machine learning is a vital tool in advancing drug development by accurately predicting the physical, chemical, and biological properties of various compounds. This study utilizes MATLAB program-based algorithms to calculate topological indices and m...

The chronODE framework for modelling multi-omic time series with ordinary differential equations and machine learning.

Nature communications
Many genome-wide studies capture isolated moments in cell differentiation or organismal development. Conversely, longitudinal studies provide a more direct way to study these kinetic processes. Here, we present an approach for modeling gene-expressio...

Application of machine learning in early childhood development research: a scoping review.

BMJ open
BACKGROUND: Early childhood development (ECD) lays the foundation for lifelong health, academic success and social well-being, yet over 250 million children in low- and middle-income countries are at risk of not reaching their developmental potential...

Computational function prediction of bacteria and phage proteins.

Microbiology and molecular biology reviews : MMBR
SUMMARYUnderstanding protein functions is crucial for interpreting microbial life; however, reliable function annotation remains a major challenge in computational biology. Despite significant advances in bioinformatics methods, ~30% of all bacterial...