Biology subjects

Ahmed, A. Y.

Publications and source records attributed to Ahmed, A. Y..

3 recordsLinked to original sources

bioRxiv2025

MELO-ED: learning locality-sensitive multi-embeddings for edit distance

Edit distance is a fundamental metric for quantifying similarity between biological sequences, but its high computational cost limits large-scale applications. Previously, we proposed learned locality-sensitive bucketing (LSB) functions that achieved superior performance and efficiency compared to classical seeding methods for identifying similar and dissimilar sequences. How-ever, each component of an LSB function is represented as a one-dimensional hash value that can only be compared for identity, which constrains the methods accuracy. Here, we intro-duce MELO-ED, a multi-embedding locality-sensitive framework that upgrades each hash value to a higher-dimensional embedding capable of efficiently approximating edit distance. MELO-ED employs a Siamese convolutional neural architecture that learns complementary embeddings capturing both global sequence context and fine-grained edit operations. By integrating locality-sensitive bucketing with multi-embedding representations, MELO-ED achieves near-perfect ac-curacy without increasing the number of buckets required. Leveraging mature indexing methods in the embedding space, MELO-ED transforms time-consuming edit distance computations into scalable similarity searches across massive genomic databases. Comprehensive evaluations on simulated DNA sequences and real barcode datasets demonstrate that MELO-ED outperforms both traditional alignment-free methods and contemporary machine learning approaches, in-cluding our previously developed learned LSB functions. These results establish MELO-ED as a state-of-the-art framework for fast and accurate classification of similar and dissimilar sequences. MELO-ED is available at https://github.com/Shao-Group/MELO-ED.

bioinformatics↗

bioRxiv2024

TomoPicker: Annotation-Efficient Particle Picking in cryo-electron Tomograms

MotivationLocalizing macromolecules in crowded cellular cryo-electron tomograms (cryo-ET) is crucial for determining their in situ structures. Traditional template matching-based approaches for this task suffer from template-specific biases and have low throughput. Given these problems, learning-based solutions are necessary. However, the paucity of annotated data for training poses substantial challenges for such learning-based methods. Moreover, preparing extensively annotated cellular cryo-ET tomograms for training macromolecule localization methods is extremely time-consuming and burdensome due to the large volume and low signal-to-noise ratio of the tomograms. ResultsIn this work, we developed TomoPicker, an annotation-efficient macromolecule localization method for cryo-ET tomograms. To achieve such annotation-efficiency, TomoPicker regards macromolecule localization as a voxel classification problem and solves it with two different positive-unlabeled learning approaches. We evaluated TomoPicker on two experimental cryo-electron tomography (cryo-ET) datasets of crowded eukaryotic cells and one experimental dataset of relatively less crowded prokaryotic cell. We observed that, with only 10 annotated macromolecule locations, TomoPicker with positive unlabeled learning achieved a performance comparable to that of state-of-the-art supervised methods trained with several hundred annotations. In other words, TomoPicker achieved plausible segmentation with up to 98% less data compared to supervised learning-based methods. Furthermore, it demonstrated substantial improvements over existing learning-based macromolecule localization methods under sparse annotation scenarios. CodeThe code to train and use TomoPicker is available on https://github.com/DuranRafid/TomoPicker.

bioinformatics↗

bioRxiv2022

SAINT-Angle: self-attention augmented inception-inside-inception network and transfer learning improve protein backbone torsion angle prediction

MotivationProtein structure provides insight into how proteins interact with one another as well as their functions in living organisms. Protein backbone torsion angles ({phi} and{psi} ) prediction is a key sub-problem in predicting protein structures. However, reliable determination of backbone torsion angles using conventional experimental methods is slow and expensive. Therefore, considerable effort is being put into developing computational methods for predicting backbone angles. ResultsWe present SAINT-Angle, a highly accurate method for predicting protein backbone torsion angles using a self-attention based deep learning network called SAINT, which was previously developed for the protein secondary structure prediction. We extended and improved the existing SAINT architecture as well as used transfer learning to predict backbone angles. We compared the performance of SAINT-Angle with the state-of-the-art methods through an extensive evaluation study on a collection of benchmark datasets, namely, TEST2016, TEST2018, CAMEO, and CASP. The experimental results suggest that our proposed self-attention based network, together with transfer learning, has achieved notable improvements over the best alternate methods. Availability and implementationSAINT-Angle is freely available as an open-source project at https://github.com/bayzidlab/SAINT-Angle. Contactshams_bayzid@cse.buet.ac.bd Supplementary informationSupplementary material SM.pdf.

bioinformatics↗