Given a set of t n-length DNA sequences, q satisfying 0 < q ≤ 1, and l and d satisfying 0 ≤ d < l < n, the quorum planted motif search (qPMS) finds l-length strings that occur in at least qt input sequences with up to d mismatches and is mainly used to locate transcription factor binding sites in DNA sequences. Existing qPMS algorithms have been able to efficiently process small standard datasets (e.g., t = 20 and n = 600), but they are too time consuming to process large DNA datasets, such as ChIP-seq datasets that contain thousands of sequences or more. We analyze the effects of t and q on the time performance of qPMS algorithms and find that a large t or a small q causes a longer computation time. Based on this information, we improve the time performance of existing qPMS algorithms by selecting a sample sequence set D’ with a small t and a large q from the large input dataset D and then executing qPMS algorithms on D’. A sample sequence selection algorithm named SamSelect is proposed. The experimental results on both simulated and real data show (1) that SamSelect can select D’ efficiently and (2) that the qPMS algorithms executed on D’ can find implanted or real motifs in a significantly shorter time than when executed on D. We improve the ability of existing qPMS algorithms to process large DNA datasets from the perspective of selecting high-quality sample sequence sets so that the qPMS algorithms can find motifs in a short time in the selected sample sequence set D’, rather than take an unfeasibly long time to search the original sequence set D. Our motif discovery method is an approximate algorithm.

Similarity measures play an important role in many data analysis fields. However, the current cell similarity measures are poor used in data characteristics. Herein, a novel similarity measure named segment weighting similarity (SWS) is developed for the analysis of single‐cell Raman spectra. SWS segments the spectra by the cell biological characteristics and quantifies the significant factors per region, which can increase the contribution of intrinsic biological features and reduce noise. The similarity heat maps of SWS and three other kinds of traditional similarities, including cosine similarity, Pearson correlation coefficient, and Euclidean distance, show that SWS has high accuracy and low bias in distinguishing cell spectra. K‐nearest‐neighbor classifiers have the identification accuracy, sensitivity, and specificity of 0.852, 0.853, and 0.965, respectively. The purity of the clustering model could increase by 0.31 in some tasks of the K‐means and spectral clustering. The classification and clustering results demonstrate that SWS is more effective than common ones. SWS, based on the basic data and intrinsic biological characteristics, provides a new thought and formula in the similarity measure for most of the Raman and infrared technologies, and has great potential for enhancing the performance of machine learning algorithms.

A DNA motif is a sequence pattern shared by the DNA sequence segments that bind to a specific protein. Discovering motifs in a given DNA sequence dataset plays a vital role in studying gene expression regulation. As an important attribute of the DNA motif, the motif length directly affects the quality of the discovered motifs. How to determine the motif length more accurately remains a difficult challenge to be solved. We propose a new motif length prediction scheme named MotifLen by using supervised machine learning. First, a method of constructing sample data for predicting the motif length is proposed. Secondly, a deep learning model for motif length prediction is constructed based on the convolutional neural network. Then, the methods of applying the proposed prediction model based on a motif found by an existing motif discovery algorithm are given. The experimental results show that i) the prediction accuracy of MotifLen is more than 90% on the validation set and is significantly higher than that of the compared methods on real datasets, ii) MotifLen can successfully optimize the motifs found by the existing motif discovery algorithms, and iii) it can effectively improve the time performance of some existing motif discovery algorithms.

Early detection of breast cancer can significantly improve patient outcomes and five-year survival in clinical screening. Dynamic optical breast imaging (DOBI) technology reflects the blood oxygen metabolism level of tumors based on the theory of tumor neovascularization, which offers a technical possibility for early detection of breast cancer. In this paper, we propose an intelligent scoring system integrating DOBI features assessment and a malignancy score grading reporting system for early detection of breast cancer. Specifically, we build six intelligent feature definition models to depict characteristics of regions of interest (ROIs) from location, space, time and context separately. Similar to the breast imaging-reporting and data system (BI -RADS), we conclude the malignancy score grading reporting system to score and evaluate ROIs as follows: Malignant (>= 80 score), Likely Malignant (60-80 score), Intermediate (35-60 score), Likely Benign (10-35 score), and Benign (<10 score). This system eliminates the influence of subjective physician judgments on the assessment of the malignant probability of ROIs. Extensive experiments on 352 Chinese patients demonstrate the effectiveness of the proposed system compared to state-of-the-art methods.

Identifying conserved patterns in DNA sequences, namely, motif discovery, is an important and challenging computational task. With hundreds or more sequences contained, the high-throughput sequencing data set is helpful to improve the identification accuracy of motif discovery but requires an even higher computing performance. To efficiently identify motifs in large DNA data sets, a new algorithm called PairMotifChIP is proposed by extracting and combining pairs of l-mers in the input with relatively small Hamming distance. In particular, a method for rapidly extracting pairs of l-mers is designed, which can be used not only for PairMotifChIP, but also for other DNA data mining tasks with the same demand. Experimental results on the simulated data show that the proposed algorithm can find motifs successfully and runs faster than the state-of-the-art motif discovery algorithms. Furthermore, the validity of the proposed algorithm has been verified on real data.

Quorum planted (l, d) motif search (qPMS) is a challenging computational problem in bioinformatics, mainly for the identification of regulatory elements such as transcription factor binding sites in DNA sequences. Large DNA datasets play an important role in identifying high-quality (l, d) motifs, while most existing qPMS algorithms are too time-consuming to complete the calculation of qPMS in a reasonable time. We propose an approximate qPMS algorithm called APMS to deal with large DNA datasets mainly by accelerating neighboring substring search and filtering redundant substrings. Experimental results on them show that APMS can not only identify the implanted (l, d) motifs, but also run orders of magnitude faster than the state-of-the-art qPMS algorithms. The source code of APMS and the python wrapper for the code are freely available at https://github.com/qyu071/apms.