Find open-source science resources

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.

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

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.

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.

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.

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.