KSNP: a fast de Bruijn graph-based haplotyping tool approaching data-in time cost.

Long reads that cover more variants per read raise opportunities for accurate haplotype construction, whereas the genotype errors of single nucleotide polymorphisms pose great computational challenges for haplotyping tools. Here we introduce KSNP, an efficient haplotype construction tool based on the de Bruijn graph (DBG). KSNP leverages the ability of DBG in handling high-throughput erroneous reads to tackle the challenges. Compared to other notable tools in this field, KSNP achieves at least 5-fold speedup while producing comparable haplotype results. The time required for assembling human haplotypes is reduced to nearly the data-in time.

A compressive seeding algorithm in conjunction with reordering-based compression.

MOTIVATION: Seeding is a rate-limiting stage in sequence alignment for next-generation sequencing reads. The existing optimization algorithms typically utilize hardware and machine-learning techniques to accelerate seeding. However, an efficient solution provided by professional next-generation sequencing compressors has been largely overlooked by far. In addition to achieving remarkable compression ratios by reordering reads, these compressors provide valuable insights for downstream alignment that reveal the repetitive computations accounting for more than 50% of seeding procedure in commonly used short read aligner BWA-MEM at typical sequencing coverage. Nevertheless, the exploited redundancy information is not fully realized or utilized. RESULTS: In this study, we present a compressive seeding algorithm, named CompSeed, to fill the gap. CompSeed, in collaboration with the existing reordering-based compression tools, finishes the BWA-MEM seeding process in about half the time by caching all intermediate seeding results in compact trie structures to directly answer repetitive inquiries that frequently cause random memory accesses. Furthermore, CompSeed demonstrates better performance as sequencing coverage increases, as it focuses solely on the small informative portion of sequencing reads after compression. The innovative strategy highlights the promising potential of integrating sequence compression and alignment to tackle the ever-growing volume of sequencing data. AVAILABILITY AND IMPLEMENTATION: CompSeed is available at https://github.com/i-xiaohu/CompSeed.

Improved lung cancer classification by employing diverse molecular features of microRNAs.

MiRNAs are edited or modified in multiple ways during their biogenesis pathways. It was reported that miRNA editing was deregulated in tumors, suggesting the potential value of miRNA editing in cancer classification. Here we extracted three types of miRNA features from 395 LUAD and control samples, including the abundances of original miRNAs, the abundances of edited miRNAs, and the editing levels of miRNA editing sites. Our results show that eight classification algorithms selected generally had better performances on combined features than on the abundances of miRNAs or editing features of miRNAs alone. One feature selection algorithm, i.e., the DFL algorithm, selected only three features, i.e., the frequencies of hsa-miR-135b-5p, hsa-miR-210-3p and hsa-mir-182_48u (an edited miRNA), from 316 training samples. Seven classification algorithms achieved 100% accuracies on these three features for 79 independent testing samples. These results indicate that the additional information of miRNA editing is useful in improving the classification of LUAD samples.

Orderly Nanodendritic Nickel Substitute for Raney Nickel Catalyst Improving Alkali Water Electrolyzer.

The development of nonprecious metal catalysts to meet the activity-stability balance at industrial-grade large current densities remains a challenge toward practical alkali-water electrolysis. Here, this work develops an orderly nanodendritic nickel (ND-Ni) catalyst that consists of ultrafine nanograins in chain-like conformation, which shows both excellent activity and robust stability for large current density hydrogen evolution reaction (HER) in alkaline media, superior to currently applied Raney nickel (R-Ni) catalyst in commercial alkali-water electrolyzer (AWE). The ND-Ni catalyst featured by a three-dimensional (3D) interconnecting microporous structure endows with high specific surface area and excellent conductivity and hydrophilicity, which together afford superior charge/mass transport favorable to HER kinetics at high current densities. An actual AWE with ND-Ni catalyst demonstrates durable water splitting with 1.0 A cm-2 at 1.71 V under industrial conditions and renders a record-low power consumption of 3.95 kW h Nm-3 with an energy efficiency close to 90%. The hydrogen price per gallon of gasoline equivalent (GGE) is calculated to be ≈$0.95, which is less than the target of $2.0 per GGE by 2026 from the U.S. Department of Energy. The results suggest the feasibility of ND-Ni substitute for R-Ni catalyst in commercial AWE.

MDTL-ACP: Anticancer Peptides Prediction Based on Multi-Domain Transfer Learning.

Anticancer peptides (ACPs) have emerged as one of the most promising therapeutic agents for cancer treatment. They are bioactive peptides featuring broad-spectrum activity and low drug-resistance. The discovery of ACPs via traditional biochemical methods is laborious and costly. Accordingly, various computational methods have been developed to facilitate the discovery of ACPs. However, the data resources and knowledge of ACPs are still very scarce, and only a few of them are clinically verified, which limits the competence of computational methods. To address this issue, in this paper, we propose an ACP prediction model based on multi-domain transfer learning, namely MDTL-ACP, to discriminate novel ACPs from plentiful inactive peptides. In particular, we collect abundant antimicrobial peptides (AMPs) from four well-studied peptide domains and extract their inherent features as the input of MDTL-ACP. The features learned from multiple source domains of AMPs are then transferred into the target prediction task of ACPs via artificial neural network-based shared-extractor and task-specific classifiers in MDTL-ACP. The knowledge captured in the transferred features enhances the prediction of ACPs in the target domain. Experimental results demonstrate that MDTL-ACP can outperform the traditional and state-of-the-art ACP prediction methods. The source code of MDTL-ACP and the data used in this study are available at https://github.com/JunhangCao/MTL-ACP.

MIX-TPI: a flexible prediction framework for TCR-pMHC interactions based on multimodal representations.

MOTIVATION: The interactions between T-cell receptors (TCR) and peptide-major histocompatibility complex (pMHC) are essential for the adaptive immune system. However, identifying these interactions can be challenging due to the limited availability of experimental data, sequence data heterogeneity, and high experimental validation costs. RESULTS: To address this issue, we develop a novel computational framework, named MIX-TPI, to predict TCR-pMHC interactions using amino acid sequences and physicochemical properties. Based on convolutional neural networks, MIX-TPI incorporates sequence-based and physicochemical-based extractors to refine the representations of TCR-pMHC interactions. Each modality is projected into modality-invariant and modality-specific representations to capture the uniformity and diversities between different features. A self-attention fusion layer is then adopted to form the classification module. Experimental results demonstrate the effectiveness of MIX-TPI in comparison with other state-of-the-art methods. MIX-TPI also shows good generalization capability on mutual exclusive evaluation datasets and a paired TCR dataset. AVAILABILITY AND IMPLEMENTATION: The source code of MIX-TPI and the test data are available at: https://github.com/Wolverinerine/MIX-TPI.

Network Biomarker Detection From Gene Co-Expression Network Using Gaussian Mixture Model Clustering.

Finding network biomarkers from gene co-expression networks (GCNs) has attracted a lot of research interest. A network biomarker is a topological module, i.e., a group of densely connected nodes in a GCN, in which the gene expression values correlate with sample labels. Compared with biomarkers based on single genes, network biomarkers are not only more robust in separating samples from different categories, but are also able to better interpret the molecular mechanism of the disease. The previous network biomarker detection methods either employ distance based clustering methods or search for cliques in a GCN to detect topological modules. The first strategy assumes that the topological modules should be spherical in shape, and the second strategy requires all nodes to be fully connected. However, the relations between genes are complex, as a result, genes in the same biological process may not be directly, strongly connected. Therefore, the shapes of those modules could be oval or long strips. Hence, the shapes of gene functional modules and gene disease modules may not meet the aforementioned constraints in the previous methods. Thus, previous methods may break up the genes belonging to the same biological process into different topological modules due to those constraints. To address this issue, we propose a novel network biomarker detection method by using Gaussian mixture model clustering which allows more flexibility in the shapes of the topological modules. We have evaluated the performance of our method on a set of eight TCGA cancer datasets. The results show that our method can detect network modules that possess better discriminate power, and provide biological insights.

Multifactorial Evolutionary Algorithm Based on Diffusion Gradient Descent.

The multifactorial evolutionary algorithm (MFEA) is one of the most widely used evolutionary multitasking (EMT) algorithms. The MFEA implements knowledge transfer among optimization tasks via crossover and mutation operators and it obtains high-quality solutions more efficiently than single-task evolutionary algorithms. Despite the effectiveness of MFEA in solving difficult optimization problems, there is no evidence of population convergence or theoretical explanations of how knowledge transfer increases algorithm performance. To fill this gap, we propose a new MFEA based on diffusion gradient descent (DGD), namely, MFEA-DGD in this article. We prove the convergence of DGD for multiple similar tasks and demonstrate that the local convexity of some tasks can help other tasks escape from local optima via knowledge transfer. Based on this theoretical foundation, we design complementary crossover and mutation operators for the proposed MFEA-DGD. As a result, the evolution population is endowed with a dynamic equation that is similar to DGD, that is, convergence is guaranteed, and the benefit from knowledge transfer is explainable. In addition, a hyper-rectangular search strategy is introduced to allow MFEA-DGD to explore more underdeveloped areas in the unified express space of all tasks and the subspace of each task. The proposed MFEA-DGD is verified experimentally on various multitask optimization problems, and the results demonstrate that MFEA-DGD can converge faster to competitive results compared to state-of-the-art EMT algorithms. We also show the possibility of interpreting the experimental results based on the convexity of different tasks.

Spatial-Temporal Co-Attention Learning for Diagnosis of Mental Disorders From Resting-State fMRI Data.

Neuroimaging techniques have been widely adopted to detect the neurological brain structures and functions of the nervous system. As an effective noninvasive neuroimaging technique, functional magnetic resonance imaging (fMRI) has been extensively used in computer-aided diagnosis (CAD) of mental disorders, e.g., autism spectrum disorder (ASD) and attention deficit/hyperactivity disorder (ADHD). In this study, we propose a spatial-temporal co-attention learning (STCAL) model for diagnosing ASD and ADHD from fMRI data. In particular, a guided co-attention (GCA) module is developed to model the intermodal interactions of spatial and temporal signal patterns. A novel sliding cluster attention module is designed to address global feature dependency of self-attention mechanism in fMRI time series. Comprehensive experimental results demonstrate that our STCAL model can achieve competitive accuracies of 73.0 ± 4.5%, 72.0 ± 3.8%, and 72.5 ± 4.2% on the ABIDE I, ABIDE II, and ADHD-200 datasets, respectively. Moreover, the potential for feature pruning based on the co-attention scores is validated by the simulation experiment. The clinical interpretation analysis of STCAL can allow medical professionals to concentrate on the discriminative regions of interest and key time frames from fMRI data.

Multiobjectivization of Single-Objective Optimization in Evolutionary Computation: A Survey.

Multiobjectivization has emerged as a new promising paradigm to solve single-objective optimization problems (SOPs) in evolutionary computation, where an SOP is transformed into a multiobjective optimization problem (MOP) and solved by an evolutionary algorithm to find the optimal solutions of the original SOP. The transformation of an SOP into an MOP can be done by adding helper-objective(s) into the original objective, decomposing the original objective into multiple subobjectives, or aggregating subobjectives of the original objective into multiple scalar objectives. Multiobjectivization bridges the gap between SOPs and MOPs by transforming an SOP into the counterpart MOP, through which multiobjective optimization methods manage to attain superior solutions of the original SOP. Particularly, using multiobjectivization to solve SOPs can reduce the number of local optima, create new search paths from local optima to global optima, attain more incomparability solutions, and/or improve solution diversity. Since the term "multiobjectivization" was coined by Knowles et al. in 2001, this subject has accumulated plenty of works in the last two decades, yet there is a lack of systematic and comprehensive survey of these efforts. This article presents a comprehensive multifacet survey of the state-of-the-art multiobjectivization methods. Particularly, a new taxonomy of the methods is provided in this article and the advantages, limitations, challenges, theoretical analyses, benchmarks, applications, as well as future directions of the multiobjectivization methods are discussed.

Federated Multi-Task Learning for Joint Diagnosis of Multiple Mental Disorders on MRI Scans.

OBJECTIVE: Deep learning (DL) techniques have been introduced to assist doctors in the interpretation of medical images by detecting image-derived phenotype abnormality. Yet the privacy-preserving policy of medical images disables the effective training of DL model using sufficiently large datasets. As a decentralized computing paradigm to address this issue, federated learning (FL) allows the training process to occur in individual institutions with local datasets, and then aggregates the resultant weights without risk of privacy leakage. METHODS: We propose an effective federated multi-task learning (MTL) framework to jointly identify multiple related mental disorders based on functional magnetic resonance imaging data. A federated contrastive learning-based feature extractor is developed to extract high-level features across client models. To ease the optimization conflicts of updating shared parameters in MTL, we present a federated multi-gate mixture of expert classifier for the joint classification. The proposed framework also provides practical modules, including personalized model learning, privacy protection, and federated biomarker interpretation. RESULTS: On real-world datasets, the proposed framework achieves robust diagnosis accuracies of 69.48 ± 1.6%, 71.44 ± 3.2%, and 83.29 ± 3.2% in autism spectrum disorder, attention deficit/hyperactivity disorder, and schizophrenia, respectively. CONCLUSION: The proposed framework can effectively ease the domain shift between clients via federated MTL. SIGNIFICANCE: The current work provides insights into exploiting the advantageous knowledge shared in related mental disorders for improving the generalization capability of computer-aided detection approaches.

Multitask Learning for Joint Diagnosis of Multiple Mental Disorders in Resting-State fMRI.

Facing the increasing worldwide prevalence of mental disorders, the symptom-based diagnostic criteria struggle to address the urgent public health concern due to the global shortfall in well-qualified professionals. Thanks to the recent advances in neuroimaging techniques, functional magnetic resonance imaging (fMRI) has surfaced as a new solution to characterize neuropathological biomarkers for detecting functional connectivity (FC) anomalies in mental disorders. However, the existing computer-aided diagnosis models for fMRI analysis suffer from unstable performance on large datasets. To address this issue, we propose an efficient multitask learning (MTL) framework for joint diagnosis of multiple mental disorders using resting-state fMRI data. A novel multiobjective evolutionary clustering algorithm is presented to group regions of interests (ROIs) into different clusters for FC pattern analysis. On the optimal clustering solution, the multicluster multigate mixture-of-expert model is used for the final classification by capturing the highly consistent feature patterns among related diagnostic tasks. Extensive simulation experiments demonstrate that the performance of the proposed framework is superior to that of the other state-of-the-art methods. Moreover, the potential for practical application of the framework is also validated in terms of limited computational resources, real-time analysis, and insufficient training data. The proposed model can identify the remarkable interpretative biomarkers associated with specific mental disorders for clinical interpretation analysis.

An exploratory study of CT radiomics using differential network feature selection for WHO/ISUP grading and progression-free survival prediction of clear cell renal cell carcinoma.

Objectives: To explore the feasibility of predicting the World Health Organization/International Society of Urological Pathology (WHO/ISUP) grade and progression-free survival (PFS) of clear cell renal cell cancer (ccRCC) using the radiomics features (RFs) based on the differential network feature selection (FS) method using the maximum-entropy probability model (MEPM). Methods: 175 ccRCC patients were divided into a training set (125) and a test set (50). The non-contrast phase (NCP), cortico-medullary phase, nephrographic phase, excretory phase phases, and all-phase WHO/ISUP grade prediction models were constructed based on a new differential network FS method using the MEPM. The diagnostic performance of the best phase model was compared with the other state-of-the-art machine learning models and the clinical models. The RFs of the best phase model were used for survival analysis and visualized using risk scores and nomograms. The performance of the above models was tested in both cross-validated and independent validation and checked by the Hosmer-Lemeshow test. Results: The NCP RFs model was the best phase model, with an AUC of 0.89 in the test set, and performed superior to other machine learning models and the clinical models (all p <0.05). Kaplan-Meier survival analysis, univariate and multivariate cox regression results, and risk score analyses showed the NCP RFs could predict PFS well (almost all p < 0.05). The nomogram model incorporated the best two RFs and showed good discrimination, a C-index of 0.71 and 0.69 in the training and test set, and good calibration. Conclusion: The NCP CT-based RFs selected by differential network FS could predict the WHO/ISUP grade and PFS of RCC.

Attention-Like Multimodality Fusion With Data Augmentation for Diagnosis of Mental Disorders Using MRI.

The globally rising prevalence of mental disorders leads to shortfalls in timely diagnosis and therapy to reduce patients' suffering. Facing such an urgent public health problem, professional efforts based on symptom criteria are seriously overstretched. Recently, the successful applications of computer-aided diagnosis approaches have provided timely opportunities to relieve the tension in healthcare services. Particularly, multimodal representation learning gains increasing attention thanks to the high temporal and spatial resolution information extracted from neuroimaging fusion. In this work, we propose an efficient multimodality fusion framework to identify multiple mental disorders based on the combination of functional and structural magnetic resonance imaging. A multioutput conditional generative adversarial network (GAN) is developed to address the scarcity of multimodal data for augmentation. Based on the augmented training data, the multiheaded gating fusion model is proposed for classification by extracting the complementary features across different modalities. The experiments demonstrate that the proposed model can achieve robust accuracies of 75.1 ± 1.5%, 72.9 ± 1.1%, and 87.2 ± 1.5% for autism spectrum disorder (ASD), attention deficit/hyperactivity disorder, and schizophrenia, respectively. In addition, the interpretability of our model is expected to enable the identification of remarkable neuropathology diagnostic biomarkers, leading to well-informed therapeutic decisions.

Chameleon-Like Reconstruction on Redox Catalysts Adaptive to Alkali Water Electrolysis.

Pre-catalyst reconstruction in electrochemical processes has recently attracted intensive attention with mechanistic potentials to uncover really active species and catalytic mechanisms and advance targeted catalyst designs. Here, nickel-molybdenum oxysulfide is deliberately fabricated as pre-catalyst to present a comprehensive study on reconstruction dynamics for the oxygen evolution reaction (OER) and hydrogen evolution reaction (HER) in alkali water electrolysis. Operando Raman spectroscopy together with X-ray photoelectron spectroscopy and electron microscopy capture dynamic reconstruction including geometric, component and phase evolutions, revealing a chameleon-like reconstruction self-adaptive to OER and HER demands under oxidative and reductive conditions, respectively. The in situ generated active NiOOH and Ni species with ultrafine and porous textures exhibit superior OER and HER performance, respectively, and an electrolyzer with such two reconstructed electrodes demonstrates steady overall water splitting with an extraordinary 80% electricity-to-hydrogen (ETH) energy conversion efficiency. This work highlights dynamic reconstruction adaptability to electrochemical conditions and develops an automatic avenue toward the targeted design of advanced catalysts.

Prediction of biomarker-disease associations based on graph attention network and text representation.

MOTIVATION: The associations between biomarkers and human diseases play a key role in understanding complex pathology and developing targeted therapies. Wet lab experiments for biomarker discovery are costly, laborious and time-consuming. Computational prediction methods can be used to greatly expedite the identification of candidate biomarkers. RESULTS: Here, we present a novel computational model named GTGenie for predicting the biomarker-disease associations based on graph and text features. In GTGenie, a graph attention network is utilized to characterize diverse similarities of biomarkers and diseases from heterogeneous information resources. Meanwhile, a pretrained BERT-based model is applied to learn the text-based representation of biomarker-disease relation from biomedical literature. The captured graph and text features are then integrated in a bimodal fusion network to model the hybrid entity representation. Finally, inductive matrix completion is adopted to infer the missing entries for reconstructing relation matrix, with which the unknown biomarker-disease associations are predicted. Experimental results on HMDD, HMDAD and LncRNADisease data sets showed that GTGenie can obtain competitive prediction performance with other state-of-the-art methods. AVAILABILITY: The source code of GTGenie and the test data are available at: https://github.com/Wolverinerine/GTGenie.

CURC: a CUDA-based reference-free read compressor.

MOTIVATION: The data deluge of high-throughput sequencing (HTS) has posed great challenges to data storage and transfer. Many specific compression tools have been developed to solve this problem. However, most of the existing compressors are based on central processing unit (CPU) platform, which might be inefficient and expensive to handle large-scale HTS data. With the popularization of graphics processing units (GPUs), GPU-compatible sequencing data compressors become desirable to exploit the computing power of GPUs. RESULTS: We present a GPU-accelerated reference-free read compressor, namely CURC, for FASTQ files. Under a GPU-CPU heterogeneous parallel scheme, CURC implements highly efficient lossless compression of DNA stream based on the pseudogenome approach and CUDA library. CURC achieves 2-6-fold speedup of the compression with competitive compression rate, compared with other state-of-the-art reference-free read compressors. AVAILABILITY AND IMPLEMENTATION: CURC can be downloaded from https://github.com/BioinfoSZU/CURC. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MarkerGenie: an NLP-enabled text-mining system for biomedical entity relation extraction.

Motivation: Natural language processing (NLP) tasks aim to convert unstructured text data (e.g. articles or dialogues) to structured information. In recent years, we have witnessed fundamental advances of NLP technique, which has been widely used in many applications such as financial text mining, news recommendation and machine translation. However, its application in the biomedical space remains challenging due to a lack of labeled data, ambiguities and inconsistencies of biological terminology. In biomedical marker discovery studies, tools that rely on NLP models to automatically and accurately extract relations of biomedical entities are valuable as they can provide a more thorough survey of all available literature, hence providing a less biased result compared to manual curation. In addition, the fast speed of machine reader helps quickly orient research and development. Results: To address the aforementioned needs, we developed automatic training data labeling, rule-based biological terminology cleaning and a more accurate NLP model for binary associative and multi-relation prediction into the MarkerGenie program. We demonstrated the effectiveness of the proposed methods in identifying relations between biomedical entities on various benchmark datasets and case studies. Availability and implementation: MarkerGenie is available at https://www.genegeniedx.com/markergenie/. Data for model training and evaluation, term lists of biomedical entities, details of the case studies and all trained models are provided at https://drive.google.com/drive/folders/14RypiIfIr3W_K-mNIAx9BNtObHSZoAyn?usp=sharing. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Evolutionary Multitasking for Multiobjective Optimization With Subspace Alignment and Adaptive Differential Evolution.

In contrast to the traditional single-tasking evolutionary algorithms, evolutionary multitasking (EMT) travels in the search space of multiple optimization tasks simultaneously. Through sharing knowledge across the tasks, EMT is able to enhance solving the optimization tasks. However, if knowledge transfer is not properly carried out, the performance of EMT might become unsatisfactory. To address this issue and improve the quality of knowledge transfer among the tasks, a novel multiobjective EMT algorithm based on subspace alignment and self-adaptive differential evolution (DE), namely, MOMFEA-SADE, is proposed in this article. Particularly, a mapping matrix obtained by subspace learning is used to transform the search space of the population and reduce the probability of negative knowledge transfer between tasks. In addition, DE characterized by a self-adaptive trial vector generation strategy is introduced to generate promising solutions based on previous experiences. The experimental results on multiobjective multi/many-tasking optimization test suites show that MOMFEA-SADE is superior or comparable to other state-of-the-art EMT algorithms. MOMFEA-SADE also won the Competition on Evolutionary Multitask Optimization (the multitask multiobjective optimization track) within IEEE 2019 Congress on Evolutionary Computation.