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RiSPICE (Rice SNP Prioritization Integrating Chromatin Effects) is a computational framework for prioritizing non-coding rice variants by integrating predicted chromatin effects from a fine-tuned DNA language model.

Accessible, Reproducible, and Scalable Machine Learning for Biomedicine. Galaxy-ML is a web machine learning end-to-end pipeline building framework, with special support to biomedical data.

SMIAL is an open-source GUI tool enabling end-to-end machine learning workflows for multichannel 2D imaging data. It allows users to perform complex analysis on label-free imaging data for tasks such as disease detection and food quality assessment without needing programming expertise.

M2UMol is a multimodal pre-training framework designed for molecular representation learning, enabling accurate predictions even when incomplete molecular data modalities are provided.

Biomed-DPT is a knowledge-enhanced framework for adapting pre-trained Vision-Language Models (VLMs) to biomedical image classification tasks. It uses a dual prompt tuning mechanism incorporating ensemble clinical prompts and LLM-driven expert prompts.

Predicting the effect of mutations on protein-RNA binding with Deep Learning | This repository contains all DeepCLIP Python code | A context-aware neural network for modeling and predicting protein binding to nucleic acids using only sequence input | DeepCLIP is a neural network with shallow convolutional layers connected to a bidirectional LSTM layer

SuperSurv is an R package that provides a unified framework for building, evaluating, and interpreting ensemble machine learning models for right-censored survival data. It implements techniques like stacking and integrates interpretability tools such as SHAP values and Restricted Mean Survival Time (RMST) contrasts.

graphpancake is an open-source Python package that generates molecular graphs for small organic molecules by translating quantum mechanical data (DFT/WFT) into graph representations. It is designed for cheminformatics pipelines, enabling advanced predictive modeling like QSAR.

PyMVPD is a Python-based toolbox to model the multivariate interactions between brain regions using fMRI data.

CoNSEPT (Convolutional Neural Network-based Sequence-to-Expression Prediction Tool) is a tool to predict gene expression in various cis and trans contexts. Inputs to CoNSEPT are enhancer sequence, transcription factor levels in one or many trans conditions, TF motifs (PWMs), and any prior knowledge of TF-TF interactions.

Coevolution and Convolutional Neural Network (CoCoNet) is a meyhod used for RNA contact prediction.

Cell Adhesion Supervised Training and Learning Environment (CASTLE) is a series of ImageJ plugins. CASTLE combines effective techniques for phase-contrast image processing with statistical analysis and machine learning algorithms to interpret the results.

An in-silico method for predicting the thermal activity for xylanases from GH10 and GH11 families. a multi-step method for the classification of xylanases from glycoside hydrolases families 10 and 11 based on their optimum temperature of activity. TAXyl: An in-silico method for predicting the optimum temperature of activity for xylanases from GH10 and GH11 families. Xylanases are involved in lignocellulosic biomass degradation and the enzyme optimum temperature is an essential factor to be considered for choosing an appropriate biocatalyst for specific usage

using deep learning to classify riboswitches with ~99% accuracy. Classifying Putative Riboswitch Sequences. RIBOFLOW - classifying riboswitches with >99% accuracy. riboflow is a python package for classifying putative riboswitch sequences into one of 32 classes with > 99% accuracy. It is based on a tensorflow deep learning model. riboflow has been tested using Python 3.5.2

Joint Integration and Discrimination for Automated Single-Cell Annotation. JIND is a framework for automated cell-type identification based on neural networks. It directly learns a low-dimensional representation (latent code) inwhich cell-types can be reliably determined. To account for batch effects, JIND performs a novel asymmetric alignment in which the transcriptomic profileof unseen cells is mapped onto the previously learned latent space, hence avoiding the need of retraining the model whenever a new dataset becomes available. JIND also learns cell-type-specific confidence thresholds to identify and reject cells that cannot be reliably classified. We show on datasets with and without batch effects that JIND classifies cells more accurately than previously proposed methods while rejecting only a small proportion of cells. Moreover, JIND batch alignment is parallelizable, being more than five or six times faster than Seurat integration.

FastISM is a Python package for in-silico saturation mutagenesis for convolutional neural networks.

DeepHE is a deep learning framework for essential gene prediction based on sequence data and PPI network.

DeePaC-Live is a deep learning framework for real-time detection of novel pathogens during sequencing.

comboLTR is a new polynomial regression-based framework for modeling anti-cancer effects of drug combinations in various doses and across different cancer cell lines.

Multi-task self-supervised learning for cell type classification. Rapidly generated scRNA-seq datasets enable us to understand cellular differences and the function of each individual cell at single-cell resolution.

SAPLING: Suffix Array Piecewise Linear INdex for Genomics. A method for achieving faster suffix array queries compared to binary search.

PanClassif is a machine learning classifier pipeline for TCGA pancancer classification. It is a complete machine learning pipeline package to work with TCGA cancer RNA-seq gene count data.

HEMnet is a deep learning automated cancer diagnosis software using molecular labelling to improve pathological annotation of Haematoxylin and Eosin (H&E) stained tissue.

The Case for Sensible Extraction of Interactions in the Era of Machine Learning. Extract meaningful interactions from machine learning models to obtain machine-learning performance with statistical model interpretability. Welcome to InteractionTransformer. Welcome to the InteractionTransformer wiki!. What our package does not do: extract transformations in the data to add them to the design matrix. For those capabilities, please see the SAFE Transformer package (https://github.com/ModelOriented/SAFE). Our package does have an experimental feature that will iteratively call InteractionTransformer and SAFETransformer to develop multi-layer interactions/transformation models, but this is still under development.

Rapid Machine Learned Synthesizability Classification from AI Driven Retrosynthetic Planning. Retrosynthetic Accessibility (RA) score. RAscore is a score learned from the predictions of a computer aided synthesis planning tool (AiZynthfinder: https://github.com/MolecularAI/aizynthfinder). RAscore is intended to be a binary score, indicating whether the underlying computer aided synthesis planning tool can find a route (1) or not (0) to a given compound.

PhANNs is a tool to classify any phage ORF as one of 10 structural protein class, or as "others". It uses an ensemble of Artificial Neural Networks. PhANNs predicts the structural class of a phage ORF by running an artificial neural network ensemble that we created against a fasta file of protein sequences. If you upload a multi-fasta file, we’ll provide you estimates of the structural classes of all the proteins, and we’ll let you download the sequences for each class as a fasta file.

The Automatic Mycorrhiza Finder (AMFinder) allows for automatic computer vision-based identification and quantification of AM fungal colonisation and intraradical hyphal structures on ink-stained root images using convolutional neural networks.

EpiDope is a deep neural network for linear B-cell epitope prediction.

Gromov-Wasserstein optimal transport to align single-cell multi-omics data. Single-Cell alignment using Optimal Transport (SCOT). SCOT is a python tool for performing unsupervised alignment of single-cell multi-omics datasets. Its methodology is detailed in the pre-print "Gromov-Wasserstein based optimal transport for aligning single-cell multi-omics data".

BWA-MEME is a practical and efficient seeding algorithm based on a suffix array search algorithm that solves the challenges in utilizing learned indices for SMEM search which is extensively used in the seeding phase

a Graph Convolutional Networks Algorithm for Knowledge Transfer in Single Cell Omics. single cell graph convolutional network (scGCN). This is a TensorFlow implementation of scGCN for leveraging differnt single cell datasets, which is described in our paper:.