Rules of forming receptive fields of formal neurons during unsupervised learning processes

In the paper two kinds of unsupervised learning processes occurring in formal neurons are analysed. The relationship between these processes and the supervised ones is discussed too. In the first case of unsupervised learning processes the neuron is considered as a filter that passes signals most frequently occurring in the learning sequence {x[n]}. In the second case it is considered as a detector of rareness which, after a finite number of steps operates as a filter passing only signals which rarely occur in the learning sequence {x[n]}. These two approaches result in different types of receptive fields of formal neurons. On the basis of the results obtained, it is possible to advance a hypothesis on the role of neurons with various types of receptive fields in information processing by a complex neuronal network.     

Comparison of two unsupervised learning algorithms

The binary decision element described by the decision rule depending upon weight vector w is a model of neuron examined in this paper. The environment of the element is described by some unknown, stationary distribution p(x). The input signals x[n] of the element appear in each step n independently in accordance with the distribution p(x). During an unsupervised learning process the weight vector w[n] is changed on the base of the input vector x[n]. In the paper there are regarded two self-learning algorithms which are stochastic approximation type. For both algorithms the same rule of past experiences neglecting or the rule of weight decrease has been introduced. The first algorithm differs from the other one by a rule of weight increase. It has been proved that only one of these algorithms always leads to the same decision rule in a given environment p(x).This work was done during stay of Dr. L. Bobrowski at the University of Salerno in the frame of Polish-Italian Agreement on Scientific Cooperation     

Multivariate analysis of Brillouin imaging data by supervised and unsupervised learning

Brillouin imaging relies on the reliable extraction of subtle spectral information from hyperspectral datasets. To date, the mainstream practice has been to use line fitting of spectral features to retrieve the average peak shift and linewidth parameters. Good results, however, depend heavily on sufficient signal-to-noise ratio and may not be applicable in complex samples that consist of spectral mixtures. In this work, we thus propose the use of various multivariate algorithms that can be used to perform supervised or unsupervised analysis of the hyperspectral data, with which we explore advanced image analysis applications, namely unmixing, classification and segmentation in a phantom and live cells. The resulting images are shown to provide more contrast and detail, and obtained on a timescale ∼10² faster than fitting. The estimated spectral parameters are consistent with those calculated from pure fitting.     

Probability density function of successive intervals of a nonhomogeneous Poisson process under low-frequency conditions

The probability density function (pdf) of successive intervals of a truncated, nonhomogeneous Poisson process was examined under both low- and high-frequency conditions. In general, if the instantaneous rate contains a single frequency component, then the phase of this component is not represented in the interval pdf. However, if the instantaneous rate contains harmonically related components, with fundamental frequency of the same order as, or less than, the mean rate, then the phases of these components do appear in the interval pdf. Correction formulae for estimating synchronization indices under low-frequency conditions are derived.     

Detecting genomic deletions from high-throughput sequence data with unsupervised learning

Background

Structural variation (SV), which ranges from 50 bp to \(\sim\) 3 Mb in size, is an important type of genetic variations. Deletion is a type of SV in which a part of a chromosome or a sequence of DNA is lost during DNA replication. Three types of signals, including discordant read-pairs, reads depth and split reads, are commonly used for SV detection from high-throughput sequence data. Many tools have been developed for detecting SVs by using one or multiple of these signals.

Results

In this paper, we develop a new method called EigenDel for detecting the germline submicroscopic genomic deletions. EigenDel first takes advantage of discordant read-pairs and clipped reads to get initial deletion candidates, and then it clusters similar candidates by using unsupervised learning methods. After that, EigenDel uses a carefully designed approach for calling true deletions from each cluster. We conduct various experiments to evaluate the performance of EigenDel on low coverage sequence data.

Conclusions

Our results show that EigenDel outperforms other major methods in terms of improving capability of balancing accuracy and sensitivity as well as reducing bias. EigenDel can be downloaded from https://github.com/lxwgcool/EigenDel.

     

High density lipoprotein modulates platelet function.

BACKGROUND: Platelet activation by atherogenic lipoproteins can be antagonized by high density lipoprotein (HDL), probably via interaction with the ATP-binding cassette transporter A1 (ABCA1). METHODS: ABCA1 expression and its association with cholesterol rich membrane domains was analyzed by mRNA and Western blot analysis. HDL effects on platelet receptor clustering were analyzed by flow cytometric analysis of fluorescence resonance energy transfer between fluorochrome-labeled antibodies. RESULTS: ABCA1 expression increased upon megakaryocytic differentiation of human stem cells and ABCA1 protein partially associated to LubroIWX-resistant membrane domains. Plasma HDL-cholesterol in healthy donors negatively correlated to the platelet membrane cholesterol content. Receptor cluster analysis revealed a decrease in the association of Gplb and FcgammaRII upon incubation of platelets with HDL3. CONCLUSION: Our results suggest that HDL modulates platelet reactivity by altering lipid raft associated receptor clustering.     

An unsupervised machine learning method for assessing quality of tandem mass spectra

Background

In a single proteomic project, tandem mass spectrometers can produce hundreds of millions of tandem mass spectra. However, majority of tandem mass spectra are of poor quality, it wastes time to search them for peptides. Therefore, the quality assessment (before database search) is very useful in the pipeline of protein identification via tandem mass spectra, especially on the reduction of searching time and the decrease of false identifications. Most existing methods for quality assessment are supervised machine learning methods based on a number of features which describe the quality of tandem mass spectra. These methods need the training datasets with knowing the quality of all spectra, which are usually unavailable for the new datasets.

Results

This study proposes an unsupervised machine learning method for quality assessment of tandem mass spectra without any training dataset. This proposed method estimates the conditional probabilities of spectra being high quality from the quality assessments based on individual features. The probabilities are estimated through a constraint optimization problem. An efficient algorithm is developed to solve the constraint optimization problem and is proved to be convergent. Experimental results on two datasets illustrate that if we search only tandem spectra with the high quality determined by the proposed method, we can save about 56 % and 62% of database searching time while losing only a small amount of high-quality spectra.

Conclusions

Results indicate that the proposed method has a good performance for the quality assessment of tandem mass spectra and the way we estimate the conditional probabilities is effective.

     

Predicting protein function by multi-label correlated semi-supervised learning

Assigning biological functions to uncharacterized proteins is a fundamental problem in the postgenomic era. The increasing availability of large amounts of data on protein-protein interactions (PPIs) has led to the emergence of a considerable number of computational methods for determining protein function in the context of a network. These algorithms, however, treat each functional class in isolation and thereby often suffer from the difficulty of the scarcity of labeled data. In reality, different functional classes are naturally dependent on one another. We propose a new algorithm, Multi-label Correlated Semi-supervised Learning (MCSL), to incorporate the intrinsic correlations among functional classes into protein function prediction by leveraging the relationships provided by the PPI network and the functional class network. The guiding intuition is that the classification function should be sufficiently smooth on subgraphs where the respective topologies of these two networks are a good match. We encode this intuition as regularized learning with intraclass and interclass consistency, which can be understood as an extension of the graph-based learning with local and global consistency (LGC) method. Cross validation on the yeast proteome illustrates that MCSL consistently outperforms several state-of-the-art methods. Most notably, it effectively overcomes the problem associated with scarcity of label data. The supplementary files are freely available at http://sites.google.com/site/csaijiang/MCSL.     

南海北部近海渔业资源密度概率分布特征

根据2014-2017年南海北部近海8个调查航次渔获量数据,结合统计方法分析该海域渔业资源密度分布特征并探索其适宜概率分布类型,进而估算区域平均资源密度.结果表明:各时期资源密度变异系数(CV)在0.67~1.03,说明该海域渔业资源密度呈较高程度的不均匀空间分布,且渔获资源密度频率分布呈现明显的右偏特征,总体以0~1000 kg·km-2资源密度为主导;单样本Kolmogorov-Smirnov检验结果表明,对数正态、伽玛和韦伯分布是该区域资源密度的适宜分布类型;在海域平均资源密度估算方面,对数正态所得结果与另两个分布类型在统计学上无显著差异,而伽玛和韦伯分布的估计值有显著差异.与1960-1970年代相比,该海域渔业资源密度适宜概率分布型已从单一类型转变为多类型,这主要归于渔业资源结构、捕捞强度以及气候变化等引起的低渔获量比例变化.     

Cryo-EM image alignment: From pair-wise to joint with deep unsupervised difference learning

Cryo-electron microscopy (cryo-EM) single-particle analysis is a revolutionary imaging technique to resolve and visualize biomacromolecules. Image alignment in cryo-EM is an important and basic step to improve the precision of the image distance calculation. However, it is a very challenging task due to high noise and low signal-to-noise ratio. Therefore, we propose a new deep unsupervised difference learning (UDL) strategy with novel pseudo-label guided learning network architecture and apply it to pair-wise image alignment in cryo-EM. The training framework is fully unsupervised. Furthermore, a variant of UDL called joint UDL (JUDL), is also proposed, which is capable of utilizing the similarity information of the whole dataset and thus further increase the alignment precision. Assessments on both real-world and synthetic cryo-EM single-particle image datasets suggest the new unsupervised joint alignment method can achieve more accurate alignment results. Our method is highly efficient by taking advantages of GPU devices. The source code of our methods is publicly available at “http://www.csbio.sjtu.edu.cn/bioinf/JointUDL/” for academic use.     

Sex-dependent loss of projection neurons involved in avian song learning.

In zebra finches, only males sing, and the neural regions controlling song exhibit prominent, hormone-induced sex differences in neuron number. In order to understand how sexual differentiation regulates neuron number within one song nucleus, the lateral magnocellular nucleus of the anterior neostriatum (IMAN), we studied the development of sex differences among IMAN neurons that project to the robust nucleus of the archistriatum (RA). The IMAN is implicated in song learning, and previous ontogenetic studies have indicated that males lose over 50% of their IMAN neurons during the juvenile song learning period. Based on developmental changes in both the extent of androgen accumulation within the IMAN and its appearance in Nissl-stained tissue, it had been hypothesized that IMAN neuron loss was even greater in young females, resulting in sex differences in neuron number. However, this hypothesis has not been tested directly because the Nissl-stained boundaries of the IMAN sometimes are ambiguous in young animals, and are not evident at all in adult females. To circumvent these problems, we employed the retrograde tracer fast blue to study the development of IMAN neurons defined on the basis of their projections to the RA. We find that the number of these IMAN-RA projection neurons is much greater in adult males than in females, and that this sex difference develops during the juvenile period of sexual differentiation and song learning because a significant number of these neurons are lost in females but not in males. With respect to sexual differentiation, we conclude that masculinization (which is stimulated by the hormone estradiol) promotes the retention of IMAN-RA projection neurons. In addition, our results indicate that any loss of IMAN neurons that may occur in young males does not include cells projecting to the RA.     

A novel machine learning unsupervised algorithm for sleep/wake identification using actigraphy

ABSTRACT

Actigraphy is widely used in sleep studies but lacks a universal unsupervised algorithm for sleep/wake identification. An unsupervised algorithm is useful in large-scale population studies and in cases where polysomnography (PSG) is unavailable, as it does not require sleep outcome labels to train the model but utilizes information solely contained in actigraphy to learn sleep and wake characteristics and separate the two states. In this study, we proposed a machine learning unsupervised algorithm based on the Hidden Markov Model (HMM) for sleep/wake identification. The proposed algorithm is also an individualized approach that takes into account individual variabilities and analyzes each individual actigraphy profile separately to infer sleep and wake states. We used Actiwatch and PSG data from 43 individuals in the Multi-Ethnic Study of Atherosclerosis study to evaluate the method performance. Epoch-by-epoch comparisons and sleep variable comparisons were made between our algorithm, the unsupervised algorithm embedded in the Actiwatch software (AS), and the pre-trained supervised UCSD algorithm. Using PSG as the reference, the accuracy was 85.7% for HMM, 84.7% for AS, and 85.0% for UCSD. The sensitivity was 99.3%, 99.7%, and 98.9% for HMM, AS, and UCSD, respectively, and the specificity was 36.4%, 30.0%, and 31.7%, respectively. The Kappa statistic was 0.446 for HMM, 0.399 for AS, and 0.311 for UCSD, suggesting fair to moderate agreement between PSG and actigraphy. The Bland–Altman plots further show that the total sleep time, sleep latency, and sleep efficiency estimates by HMM were closer to PSG with narrower 95% limits of agreement than AS and UCSD. All three methods tend to overestimate sleep and underestimate wake compared to PSG. Our HMM approach is also able to differentiate relatively active and sedentary individuals by quantifying variabilities in activity counts: individuals with higher estimated activity variabilities tend to show more frequent sedentary behaviors. Our unsupervised data-driven HMM algorithm achieved better performance than the commonly used Actiwatch software algorithm and the pre-trained UCSD algorithm. HMM can help expand the application of actigraphy in cases where PSG is hard to acquire and supervised methods cannot be trained. In addition, the estimated HMM parameters can characterize individual activity patterns and sedentary tendencies that can be further utilized in downstream analysis.     

The binding capacity is a probability density function.

The binding capacity of a system, or equivalently, the fluctuations of the number of ligands bound around the average value defined by the binding isotherm, can be regarded as a probability density function for the chemical potential of the ligand. The first moment of this density function is the mean ligand activity as defined by Wyman and gives the average free energy (in kT units) of binding per site. The second moment is directly related to the cooperativity of the system. These and higher moments can be obtained from numerical integration of experimental data in a direct way. An analytical expression for the moment generating function shows that the N independent coefficients of the partition function of a system containing N sites are uniquely defined by the first N moments of the binding capacity.