Feature extraction and classification of bio-signals are crucial in human-machine interface (HMI), yet suffer from high delay and limited energy efficiency using conventional hardware. To mitigate this challenge, we propose an SDISC architecture, a neuromorphic HMI with the innovation from signal encoding, computing-in-memory (CIM) hardware, to algorithm-hardware co-optimization. The following strategies are implemented: (1) A spike-driven feature extractor, achieving > $10 \times$ sparser dataflow than frame-based method; (2) In-situ computing based on resistive random-access memory (RRAM), enabling energy-efficient (4.09 TOPS/W) spiking neural network (SNN) classifier; (3) A Spike-Activity-Distillation algorithm and an Aid-Loser-Only recovery scheme to alleviate the non-ideality of RRAM devices, ensuring SDISC maintains high accuracy ($\sim \mathbf{9 8. 0 \%}$) in long time inference ($\boldsymbol{\gt} \mathbf{1 5}$ days). We further develop an end-to-end SDISC system for real-time EMG-based robot control, achieving a low latency ($34 \mu \mathrm{~s}$) and low power ($39.72 \mu \mathrm{~W} /$ sample) interaction on edge.

Spatial transcriptomics quantifies spatial molecular profiles alongside histology, enabling computational prediction of spatial gene expression distribution directly from whole slide images. Inspired by image-to-text alignment and generation, we introduce Magic, a self-training contrastive learning model designed for histology-to-gene expression prediction. Magic (i) employs contrastive learning to derive shared embeddings for histology and gene expression while utilizing a momentum-based module to generate pseudo-targets to reduce the impact of noise; and (ii) leverages a transformer-based decoder to predict the expression of 300 genes based on histological features. Trained on 75 760 spots from 56 breast cancer slices and validated on 11 026 spots from five independent slices, Magic outperforms existing methods in aligning and generating histology-gene expression data, achieving a 10% improvement over the second-best approach. Furthermore, Magic demonstrates robust generalization, effectively predicting gene expression in colorectal cancer samples and The Cancer Genome Atlas (TCGA) datasets through zero-shot learning. Notably, Magic's predicted gene expression captures interpatient differences, highlighting its strong potential for clinical applications.

Counting the occurrence frequency of each $k$k-mer in a biological sequence is a preliminary yet important step in many bioinformatics applications. However, most $k$k-mer counting algorithms rely on a given $k$k to produce single-length $k$k-mers, which is inefficient for sequence analysis for different $k$k. Moreover, existing $k$k-mer counters focus more on DNA and RNA sequences and less on protein ones. In practice, the analysis of $k$k-mers in protein sequences can provide substantial biological insights in structure, function, and evolution. To this end, an efficient algorithm, called MulMer (Multiple-Mer mining), is proposed to mine $k$k-mers of various lengths termed multi-mers via inverted-index technique, which is orders of magnitude faster than the conventional forward-index methods. Moreover, to the best of our knowledge, MulMer is the first able to mine multi-mers in a variety of sequences, including DNA, RNA, and protein sequences.

The discovery of conserved sequential patterns in biological sequences is essential to unveiling common shared functions. Mining sequential generators as well as mining closed sequential patterns can contribute to a more concise result set than mining all sequential patterns, especially in the analysis of big data in bioinformatics. Previous studies have also presented convincing arguments that the generator is preferable to the closed pattern in inductive inference and classification. However, classic sequential generator mining algorithms, due to the lack of consideration on the contiguous constraint along with the lower-closed one, still pose a great challenge at spawning a large number of inefficient and redundant patterns, which is too huge for effective usage. Driven by some extensive applications of patterns with contiguous feature, we propose ConSgen, an efficient algorithm for discovering contiguous sequential generators. It adopts the n-gram model, called shingles, to generate potential frequent subsequences and leverages several pruning techniques to prune the unpromising parts of search space. And then, the contiguous sequential generators are identified by using the equivalence class-based lower-closure checking scheme. Our experiments on both DNA and protein data sets demonstrate the compactness, efficiency, and scalability of ConSgen.

Ontology plays a very important role in supporting knowledge-based applications. In cloud computing, ontology learning technology is facing new challenges in dealing with heterogeneous data sources from different domains and researchers, which may contain various particular concepts and relations. Traditional ontology learning frameworks usually focus only on the extraction of concepts and taxonomic relations from the multi-structured corpus. However, former researches rarely studied the interactions during ontology learning process among different researchers. Lack of interactions among people who build ontology in different domains may cause inconsistent ontology. Besides, lack of incentive during the ontology building process will also result in low efficiency. To address these challenges, this paper specifies a novel solution to perform ontology learning. The solution includes a service-oriented ontology interaction framework, a service-oriented ontology learning strategy. It shows that it advances ontology learning to a higher level of performance and portability with a number of experiments in demo system.