arXiv subjects

Shikai Jin

Publications and source records attributed to Shikai Jin.

3 recordsLinked to original sources

arXiv2025

Learning Cross-Domain Representations for Transferable Drug Perturbations on Single-Cell Transcriptional Responses

Phenotypic drug discovery has attracted widespread attention because of its potential to identify bioactive molecules. Transcriptomic profiling provides a comprehensive reflection of phenotypic changes in cellular responses to external perturbations. In this paper, we propose XTransferCDR, a novel generative framework designed for feature decoupling and transferable representation learning across domains. Given a pair of perturbed expression profiles, our approach decouples the perturbation representations from basal states through domain separation encoders and then cross-transfers them in the latent space. The transferred representations are then used to reconstruct the corresponding perturbed expression profiles via a shared decoder. This cross-transfer constraint effectively promotes the learning of transferable drug perturbation representations. We conducted extensive evaluations of our model on multiple datasets, including single-cell transcriptional responses to drugs and single- and combinatorial genetic perturbations. The experimental results show that XTransferCDR achieved better performance than current state-of-the-art methods, showcasing its potential to advance phenotypic drug discovery.

cs.LG↗

arXiv2023

CrysFormer: Protein Structure Prediction via 3d Patterson Maps and Partial Structure Attention

Determining the structure of a protein has been a decades-long open question. A protein's three-dimensional structure often poses nontrivial computation costs, when classical simulation algorithms are utilized. Advances in the transformer neural network architecture -- such as AlphaFold2 -- achieve significant improvements for this problem, by learning from a large dataset of sequence information and corresponding protein structures. Yet, such methods only focus on sequence information; other available prior knowledge, such as protein crystallography and partial structure of amino acids, could be potentially utilized. To the best of our knowledge, we propose the first transformer-based model that directly utilizes protein crystallography and partial structure information to predict the electron density maps of proteins. Via two new datasets of peptide fragments (2-residue and 15-residue) , we demonstrate our method, dubbed \texttt{CrysFormer}, can achieve accurate predictions, based on a much smaller dataset size and with reduced computation costs.

cs.LG↗

arXiv2016

A New Parallelization Method for K-means

K-means is a popular clustering method used in data mining area. To work with large datasets, researchers propose PKMeans, which is a parallel k-means on MapReduce. However, the existing k-means parallelization methods including PKMeans have many limitations. PKMeans can't finish all its iterations in one MapReduce job, so it has to repeat cascading MapReduce jobs in a loop until convergence. On the most popular MapReduce platform, Hadoop, every MapReduce job introduces significant I/O overheads and extra execution time at stages of job start-up and shuffling. Even worse, it has been proved that in the worst case, k-means needs $2^{Ω(n)}$ MapReduce jobs to converge, where n is the number of data instances, which means huge overheads for large datasets. Additionally, in PKMeans, at most one reducer can be assigned to and update each centroid, so PKMeans can only make use of limited number of parallel reducers. In this paper, we propose an improved parallel method for k-means, IPKMeans, which has a parallel preprocessing stage using k-d tree and can finish k-means in one single MapReduce job with much more reducers working in parallel and lower I/O overheads than PKMeans and has a fast post-processing stage generating the final result. In our method, both k-d tree and the new improved parallel k-means are implemented using MapReduce and tested on Hadoop. Our experiments show that with same dataset and initial centroids, our method has up to 2/3 lower I/O overheads and consumes less amount of time than PKMeans to get a very close clustering result.

cs.DC↗