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ModelsLópez-Cortés2020 - Prediction of Breast Cancer (BC) proteins involved in cancer immunotherapy using molecular descriptors and Multi Layer Perceptron (MLP) neural network
This study introduces a predictive classifier for breast cancer-related proteins, utilising a combination of protein sequence descriptors and machine learning techniques. The best-performing classifier is a Multi Layer Perceptron (artificial neural network) with 300 features, achieving an average Ar...
2023-10-09 | BIOMD0000001076 | BioModels
FAIR-AIML 
ModelsKong2022 - Conditional Antibody Design as 3D Equivariant Graph Translation
Multi-channel Equivariant Attention Network (MEAN) to co-design 1D sequences and 3D structures of CDRs. To be specific, MEAN formulates antibody design as a conditional graph translation problem by importing extra components including the target antigen and the light chain of the antibody. Then, MEA...
2023-05-10 | BIOMD0000001070 | BioModels
Machine Learning Model FAIR-AIML 
ModelsAlam2019 - Machine learning approach of automatic identification and counting of blood cells
This model is used for automatic identification and counting of three types of blood cells: Red Blood Cells (RBC), White Blood Cells (WBC) and Platelet (Platelets) using the ‘you only look once’ (YOLO) object detection and classification algorithm with some additions to remove overannotation. The YO...
2023-05-25 | BIOMD0000001069 | BioModels
Machine Learning Model FAIR-AIML 
ModelsDeshpande2019 - Random Forest model to predict long non-coding RNAs from coding RNAs in Zea Mays plant transcriptomic data
This is a Random Forest algorithm-based machine learning model to predict lncRNAs from coding mRNAs in plant transcriptomic data. The model assigns 1 for coding sequences and 2 for long non-coding sequences. The prediction is performed using a combination of Open Reading Frame (ORF) based, Sequence-...
2024-12-09 | BIOMD0000001067 | BioModels
Machine Learning Model FAIR-AIML 
ModelsWang2022 - Scaffolding protein functional sites using deep learning
Deep learning approaches for scaffolding such functional sites without needing to prespecify the fold or secondary structure of the scaffold. The first approach, “constrained hallucination,” optimizes sequences such that their predicted structures contain the desired functional site. The second appr...
2023-05-10 | BIOMD0000001071 | BioModels
Machine Learning Model FAIR-AIML 
ModelsPatterson2022 - Tumour mutation data driven Random Forest model to predict immune checkpoint inhibitor therapy benefit in metastatic melanoma
A Random Forest model is developed to incorporate tumor mutation data within the context of the biological process known as leukocyte proliferation regulation. This model aims to predict a patient's response to anti-PD1 treatment. The authors conducted experiments using four different types of clas...
2023-07-03 | BIOMD0000001073 | BioModels
Machine Learning Model FAIR-AIML 
ModelsLiu2023 - Predicting the efficacy of immune checkpoint inhibitors monotherapy in advanced non-small cell lung cancer: a machine learning method based on multidimensional data
Immunotherapy has improved the prognosis of patients with advanced non-small cell lung cancer (NSCLC), but only a small subset of patients achieved clinical benefit. The purpose of our study was to integrate multidimensional data using a machine learning method to predict the therapeutic efficacy of...
2023-07-11 | BIOMD0000001074 | BioModels
Machine Learning Model FAIR-AIML 
ModelsLiu2019 - Logistic Regression models to predict intrinsic resistance to anti-PD1 ICB in Ipilimumab treated and Ipilimumab naive patients with metastatic melanoma.
In this comprehensive study, the authors have developed concise models integrating clinical, genomic and transcriptomic features to predict intrinsic resistance to anti-PD1 Immune Checkpoint Blockade (ICB) treatment in individual tumors. It's important to note that their validation was performed in ...
2023-11-17 | MODEL2310150001 | BioModels
Machine Learning Model FAIR-AIML 
ModelsSammut2022 - Multi-omic machine learning model to predict pathological complete response for breast cancer neoadjuvant therapy
In this publication, researchers investigated the intricate relationship between breast cancers and their microenvironment, specifically focusing on predicting treatment responses using multi-omic machine learning model. They collected diverse data types including clinical, genomic, transcriptomic, ...
2023-08-28 | BIOMD0000001075 | BioModels
Machine Learning Model FAIR-AIML 
ModelsNassar2022 - Metagenomics Classification Task for Scientific Literature Text
This is a use case to show that, given any automatic metagenomic classification model for the documents, we can convert those to ONNX (Open Neural Network Exchange) format; it also consists of the Dockerfile that can be used to prepare a docker image. This conversion ensures interoperability and op...
2023-05-25 | BIOMD0000001068 | BioModels
Machine Learning Model FAIR-AIML 
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