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

Improving binding affinity prediction by leveraging molecular mechanics energies with a 3D Convolutional Neural Network.

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.

A Machine Learning Model for Predicting the Pathogenicity of Missense Variants.

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

Flame is an open source framework for model development, hosting, and usage in production environments. Flame is a web application, with a Python backend and a web-based graphic interface, which can be used as a desktop application or installed in a server receiving requests from multiple users. Models can be built starting from any collection of biologically annotated chemical structures, since the software supports structural normalization, molecular descriptor generation and machine learning building, using predefined workflows. The model building workflow can be customized from the graphic interface, selecting the type of normalization, molecular descriptors, and machine learning algorithm to be used from a panel of state-of-the-art methods implemented natively.

Accurate prediction of protein contact maps by coupling residual two-dimensional bidirectional long short-term memory with convolutional neural networks.

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.

Predicting effects of mutations on protein stability using graph neural networks.

An Error-correction Tool for Nanopore Sequencing Based on a Deep Learning Algorithm. Nanopore sequencing is regarded as one of the most promising third-generation sequencing (TGS) technologies. However, the nanopore sequencing reads are susceptible to a fairly high error rate owing to the difficulty in identifying the DNA bases from the complex electrical signals. Here we proposed a DNA basecalling reviser, NanoReviser, based on a deep learning algorithm to correct the basecalling errors introduced by current basecallers provided by default. In our module, we re-segmented the raw electrical signals based on the basecalled sequences provided by the default basecallers. By employing convolution neural networks (CNNs) and bidirectional long short-term memory (Bi-LSTM) networks, we took advantage of the information from the raw electrical signals and the basecalled sequences from the basecallers.

Machine Learning-Assisted Directed Evolution Navigates a Combinatorial Epistatic Fitness Landscape with Minimal Screening Burden. A machine-learning package for navigating combinatorial protein fitness landscapes. This repository accompanies our work "Machine Learning-Assisted Directed Evolution Navigates a Combinatorial Epistatic Fitness Landscape with Minimal Screening Burden".

A method for annotation of metal binding functionality encoded in protein sequences.

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.

External validation of machine learning models with adaptive sample splitting

Integrating millions of single cells using batch-aware triplet neural networks. INtegration of millions of Single Cells using batch-aware Triplet networks. insct is a deep learning algorithm which calculates an integrated embedding for scRNA-seq data. With insct, you can:. a, INSCT learns a data representation, which integrates cells across batches. The goal of the network is to minimize the distance between Anchor and Positive while maximizing the distance between Anchor and Negative. Anchor and Positive pairs consist of transcriptionally similar cells from different batches. The Negative is a transcriptomically dissimilar cell sampled from the same batch as the Anchor. b, Principal components of three data points corresponding to Anchor, Positive and Negative are fed into three identical neural networks, which share weights. The triplet loss function is used to train the network weights and the two-dimensional embedding layer activations represent the integrated embedding.

DeepFrag is a deep convolutional neural network that guides ligand optimization by extending a ligand with a molecular fragment, such that the resulting extension is also highly complementary to the receptor.

An interpretable recurrent neural network model with attention mechanism for peptide-HLA class II binding prediction. A sequence-based pan model for peptide-MHC II binding affinity prediction.

A deep convolutional neural network toolkit for epigenomics. SDK for deep learning based processing of Atac-seq data. AtacWorks is a deep learning toolkit for track denoising and peak calling from low-coverage or low-quality ATAC-Seq data. AtacWorks trains a deep neural network to learn a mapping between noisy (low coverage/low quality) ATAC-Seq data and matching clean (high coverage/high quality) ATAC-Seq data from the same cell type. Once this mapping is learned, the trained model can be applied to improve other noisy ATAC-Seq datasets

PeakBot is a python package for peak-picking in LC-HRMS profile mode data. It uses local-maxima in the LC-HRMS dataset each of which is then exported as a standarized two-dimensional area (rt x mz), which is used as the input for a machine-learning CNN model that reports whether the local-maxima is a chromatographic peak with left/right isomeric compounds or a signal of the background.

ProteInfer is an approach for predicting the functional properties of protein sequences using deep neural networks.

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

scDART (single cell Deep learning model for ATAC-Seq and RNA-Seq Trajectory integration) is a scalable deep learning framework that embed the two data modalities of single cells, scRNA-seq and scATAC-seq data, into a shared low-dimensional latent space while preserving cell trajectory structures. Furthermore, scDART learns a nonlinear function represented by a neural network encoding the cross-modality relationship simultaneously when learning the latent space representations of the integrated dataset.

DeepTE is aimed to classify transposons with unknown classification via Convolutional Neural Network. A computational method for de novo classification of transposons with convolutional neural network.

Graph convolutional networks for drug response prediction.

Web application for analysing medical imaging research data in the browser, without local installation or coding. Supports automatic and interactive segmentation of organs, bones and muscles in CT and MRI (including TotalSegmentator, MOOSE, MRSegmentator and MuscleMap models), manual editing of segmentation masks, synthetic CT and brain MRI generation (MAISI-v2), structured research reports from chest and abdominal CT, and quantitative PET/SPECT measurements with longitudinal and group comparisons. Accepts DICOM and NIfTI input. For research use only; not a medical device.