Deep Learning Models for Predicting Phenotypic Traits and Diseases from Omics Data

Computational analysis of high-throughput omics data, such as gene expressions, copy number alterations and DNA methylation (DNAm), has become popular in disease studies in recent decades because such analyses can be very helpful to predict whether a patient has certain disease or its subtypes. However, due to the high-dimensional nature of the data sets with hundreds of thousands of variables and very small number of samples, traditional machine learning approaches, such as support vector machines (SVMs) and random forests, have limitations to analyze these data efficiently. In this chapter, we reviewed the progress in applying deep learning algorithms to solve some biological questions. The focus is on potential software tools and public data sources for the tasks. Particularly, we show some case studies using deep neural network (DNN) models for classifying molecular subtypes of breast cancer and DNN-based regression models to account for interindividual variation in triglyceride concentrations measured at different visits of peripheral blood samples using DNAm profiles. We show that integration of multi-omics profiles into DNN-based learning methods could improve the prediction of the molecular subtypes of breast cancer. We also demonstrate the superiority of our proposed DNN models over the SVM model for predicting triglyceride concentrations

Hierarchical representation for PPI sites prediction

Background: Protein–protein interactions have pivotal roles in life processes, and aberrant interactions are associated with various disorders. Interaction site identification is key for understanding disease mechanisms and design new drugs. Effective and efficient computational methods for the PPI prediction are of great value due to the overall cost of experimental methods. Promising results have been obtained using machine learning methods and deep learning techniques, but their effectiveness depends on protein representation and feature selection. Results: We define a new abstraction of the protein structure, called hierarchical representations, considering and quantifying spatial and sequential neighboring among amino acids. We also investigate the effect of molecular abstractions using the Graph Convolutional Networks technique to classify amino acids as interface and no-interface ones. Our study takes into account three abstractions, hierarchical representations, contact map, and the residue sequence, and considers the eight functional classes of proteins extracted from the Protein–Protein Docking Benchmark 5.0. The performance of our method, evaluated using standard metrics, is compared to the ones obtained with some state-of-the-art protein interface predictors. The analysis of the performance values shows that our method outperforms the considered competitors when the considered molecules are structurally similar. Conclusions: The hierarchical representation can capture the structural properties that promote the interactions and can be used to represent proteins with unknown structures by codifying only their sequential neighboring. Analyzing the results, we conclude that classes should be arranged according to their architectures rather than functions

Machine learning solutions for predicting protein–protein interactions

Proteins are social molecules. Recent experimental evidence supports the notion that large protein aggregates, known as biomolecular condensates, affect structurally and functionally many biological processes. Condensate formation may be permanent and/or time dependent, suggesting that biological processes can occur locally, depending on the cell needs. The question then arises as to which extent we can monitor protein-aggregate formation, both experimentally and theoretically and then predict/simulate functional aggregate formation. Available data are relative to mesoscopic interacting networks at a proteome level, to protein-binding affinity data, and to interacting protein complexes, solved with atomic resolution. Powerful algorithms based on machine learning (ML) can extract information from data sets and infer properties of never-seen-before examples. ML tools address the problem of protein–protein interactions (PPIs) adopting different data sets, input features, and architectures. According to recent publications, deep learning is the most successful method. However, in ML-computational biology, convincing evidence of a success story comes out by performing general benchmarks on blind datasets. Results indicate that the state-of-the-art ML approaches, based on traditional and/or deep learning, can still be ameliorated, irrespectively of the power of the method and richness in input features. This being the case, it is quite evident that powerful methods still are not trained on the whole possible spectrum of PPIs and that more investigations are necessary to complete our knowledge of PPI-functional interaction

Reciprocal Perspective for Improved Protein-Protein Interaction Prediction

All protein-protein interaction (PPI) predictors require the determination of an operational decision threshold when differentiating positive PPIs from negatives. Historically, a single global threshold, typically optimized via cross-validation testing, is applied to all protein pairs. However, we here use data visualization techniques to show that no single decision threshold is suitable for all protein pairs, given the inherent diversity of protein interaction profiles. The recent development of high throughput PPI predictors has enabled the comprehensive scoring of all possible protein-protein pairs. This, in turn, has given rise to context, enabling us now to evaluate a PPI within the context of all possible predictions. Leveraging this context, we introduce a novel modeling framework called Reciprocal Perspective (RP), which estimates a localized threshold on a per-protein basis using several rank order metrics. By considering a putative PPI from the perspective of each of the proteins within the pair, RP rescores the predicted PPI and applies a cascaded Random Forest classifier leading to improvements in recall and precision. We here validate RP using two state-of-the-art PPI predictors, the Protein-protein Interaction Prediction Engine and the Scoring PRotein INTeractions methods, over five organisms: Homo sapiens, Saccharomyces cerevisiae, Arabidopsis thaliana, Caenorhabditis elegans, and Mus musculus. Results demonstrate the application of a post hoc RP rescoring layer significantly improves classification (p < 0.001) in all cases over all organisms and this new rescoring approach can apply to any PPI prediction method

A guide to machine learning for biologists

The expanding scale and inherent complexity of biological data have encouraged a growing use of machine learning in biology to build informative and predictive models of the underlying biological processes. All machine learning techniques fit models to data; however, the specific methods are quite varied and can at first glance seem bewildering. In this Review, we aim to provide readers with a gentle introduction to a few key machine learning techniques, including the most recently developed and widely used techniques involving deep neural networks. We describe how different techniques may be suited to specific types of biological data, and also discuss some best practices and points to consider when one is embarking on experiments involving machine learning. Some emerging directions in machine learning methodology are also discussed

An Extended Feature Representation Technique for Predicting Sequenced-based Host-pathogen Protein-protein Interaction

Background: The use of machine learning models in sequence-based Protein-Protein Interaction prediction typically requires the conversion of amino acid sequences into feature vectors. From the literature, two approaches have been used to achieve this transformation. These are referred to as the Independent Protein Feature (IPF) and Merged Protein Feature (MPF) extraction methods. As observed, studies have predominantly adopted the IPF approach, while others preferred the MPF method, in which host and pathogen sequences are concatenated before feature encoding. Objective: This presents the challenge of determining which approach should be adopted for improved HPPPI prediction. Therefore, this work introduces the Extended Protein Feature (EPF) method. Methods: The proposed method combines the predictive capabilities of IPF and MPF, extracting essential features, handling multicollinearity, and removing features with zero importance. EPF, IPF, and MPF were tested using bacteria, parasite, virus, and plant HPPPI datasets and were deployed to machine learning models, including Random Forest (RF), Support Vector Machine (SVM), Multilayer Perceptron (MLP), Naïve Bayes (NB), Logistic Regression (LR), and Deep Forest (DF). Results: The results indicated that MPF exhibited the lowest performance overall, whereas IPF performed better with decision tree-based models, such as RF and DF. In contrast, EPF demonstrated improved performance with SVM, LR, NB, and MLP and also yielded competitive results with DF and RF. Conclusion: In conclusion, the EPF approach developed in this study exhibits substantial improvements in four out of the six models evaluated. This suggests that EPF offers competitiveness with IPF and is particularly well-suited for traditional machine learning models

４種類の免疫ペプチド分類問題を解決する機械学習アプローチ

Peptides play an important role in all aspects of the immunological reactions to invading cancer and pathogen cells. It has been known for over 40-years that peptides are critical influences in assembling the immune system against foreign invaders. Since then, new knowledge about the generation and function of peptides in immunology has supported efforts to harness the immune system to treat disease. Yet, with little immunological insight, most of the highly productive treatments, including vaccines, have been developed empirically. Nonetheless, increased knowledge of the biology of antigen processing as well as chemistry and pharmacological properties of antigenic and antimicrobial peptides has now permitted to development of drugs and vaccines. Due to advanced technologies, it is vitally important to develop automatic computational methods for rapidly and accurately predicting immune-peptides. In this thesis, the author focuses on the machine learning approaches for addressing classification problems of four types of immune-peptides (anti-inflammatory, proinflammatory, anti-tuberculosis, and linear B-cell peptides).Numerous inflammatory diseases and autoimmune disorders by therapeutic peptides have received substantial consideration; however, the exploration of anti-inflammatory peptides via biological experiments is often a time consuming and expensive task. The development of novel in silico predictors is desired to classify potential anti-inflammatory peptides prior to in vitro investigation. Herein, an accurate predictor, called PreAIP (Predictor of Anti-Inflammatory Peptides) was developed by integrating multiple complementary features. We systematically investigated different types of features including primary sequence, evolutionary and structural information through a random forest classifier. The final PreAIP model achieved an AUC value of 0.833 in the training dataset via 10-fold cross-validation test, which was better than that of existing models. Moreover, we assessed the performance of the PreAIP with an AUC value of 0.840 on a test dataset to demonstrate that the proposed method outperformed the two existing methods. These results indicated that the PreAIP is an accurate predictor for identifying anti-inflammatory peptides and contributes to the development of anti-inflammatory peptides therapeutics and biomedical research. The curated datasets and the PreAIP are freely available at http://kurata14.bio.kyutech.ac.jp/PreAIP/. A proinflammatory peptide (PIP) is a type of signaling molecules that are secreted from immune cells, which contributes to the first line of defense against invading pathogens. Numerous experiments have shown that PIPs play an important role in human physiology such as vaccines and immunotherapeutic drugs. Considering high-throughput laboratory methods that are time consuming and costly, effective computational methods are great demand to timely and accurately identify PIPs. Thus, in this study, we proposed a computational model in conjunction with a multiple feature representation, called ProIn-Fuse, to improve the performance of PIPs identification. Specifically, a feature representation learning model was utilized to generate a set of informative probabilistic features by making the use of random forest models with eight sequence encoding schemes. Finally, the ProIn-Fuse was constructed by the linearly combined models of the informative probabilistic features. The generalization capability of our proposed method evaluated through independent test showed that ProIn-Fuse yielded an accuracy of 0.746, which was over 10% higher than those obtained by the state-of-the-art PIP predictors. Cross-validation and independent results consistently demonstrated that ProIn-Fuse is more precise and promising in the identification of PIPs than existing PIP predictors. The web server, datasets and online instruction are freely accessible at http://kurata14.bio.kyutech.ac.jp/ProIn-Fuse/. We believe that the proposed ProIn-Fuse can facilitate faster and broader applications of PIPs in drug design and development. Tuberculosis (TB) is a leading killer caused by Mycobacterium tuberculosis. Recently anti-TB peptides have provided an alternative approach to combat antibiotic tolerance. Herein, we have developed an effective computational predictor iAntiTB (identification of anti-tubercular peptides) that integrates multiple feature vectors deriving from the amino acid sequences via Random Forest (RF) and Support Vector Machine (SVM) classifiers. The iAntiTB combined the RF and SVM scores via linear regression to enhance the prediction accuracy. To make a robust and accurate predictor we prepared the two datasets with different types of negative samples. The iAntiTB achieved AUC values of 0.896 and 0.946 on the training datasets of the first and second datasets, respectively. The iAntiTB outperformed the other existing predictors. Thus, the iAntiTB is a robust and accurate predictor that is helpful for researchers working on peptide therapeutics and immunotherapy. All the employed datasets and software application are accessible at http://kurata14.bio.kyutech.ac.jp/iAntiTB/. Linear B-cell peptides are critically important for immunological applications such as vaccine design, immunodiagnostic tests, antibody production, and disease diagnosis and therapy. The accurate identification of linear B-cell peptides remains challenging despite several decades of research. In this work, we have developed a novel predictor, iLBE (Identification of B-Cell Epitope), by integrating evolutionary and sequence-based features. The successive feature vectors were optimized by a Wilcoxon rank-sum test. Then the random forest (RF) algorithm used the optimal consecutive feature vectors to predict linear B-cell epitopes. We combined the RF scores by the logistic regression to enhance the prediction accuracy. The performance of the final iLBE yielded an AUC score of 0.809 on the training dataset. It outperformed other existing prediction models on a comprehensive independent dataset. The iLBE is suggested to be a powerful computational tool to identify the linear B-cell peptides and development of penetrating diagnostic tests. A web application with curated datasets is freely accessible of iLBE at http://kurata14.bio.kyutech.ac.jp/iLBE/. Taken together, the above results suggest that our proposed predictors (PreAIP, ProIn-Fuse, iAntiTB, and iLBE) would be helpful computational resources for the prediction of anti-inflammatory, pro-inflammatory, tuberculosis, and linear B-cell peptides. / ペプチドは、癌や病原体細胞に対する免疫反応のあらゆる側面で重要な役割を果たす。ペプチドが外来の侵入物に対する免疫系を起動する上で決定的な影響を与えることは40年以上前から知られている。それ以来、免疫学におけるペプチドの生成と機能に関する新しい知見は、病気を治療するために免疫系を利用する研究を支えてきた。依然として、免疫学的洞察がほとんどないため、ワクチンを含む効率的治療法のほとんどは、経験的に開発されている。それでもなお、抗原プロセシングの生物学、ならびに抗原性および抗菌性ペプチドの化学・薬理学に関する知見の増加により、現在、薬物およびワクチンの開発が可能になっている。高度な技術により、免疫ペプチドを迅速かつ正確に予測するためのコンピュータ技術を開発することが非常に重要である。この論文では、著者は4種類の免疫ペプチド（抗炎症、炎症誘発性、抗結核、および線形B細胞エピトープ）の分類問題に対処するための機械学習アプローチに焦点を当てる。炎症性疾患および自己免疫疾患に対する治療用ペプチドは、多くの検討がなされてきた。しかし、生物学的実験による抗炎症ペプチドの探索は、多くの場合、時間と費用のかかる作業である。新しいin siloco予測器の開発は、in vitro実験に先立って、潜在的な抗炎症ペプチドを同定するために望まれている。ここでは、PreAIP（抗炎症ペプチドの予測器）と呼ばれる予測器が、複数の補完的機能を統合することによって開発された。一次配列、進化的および構造的情報を含むさまざまなタイプの特徴量を、ランダムフォレスト分類器を介して抽出した。最終的なPreAIPモデルは、10分割交差検定によるトレーニングデータセットで0.833のAUC値を達成した。これは、既存のモデルよりも優れた値である。さらに、独立の検証用データセットでAUC値0.840を達成し、提案された方法が2つの既存の予測器よりも優れていることを示した。これらの結果は、PreAIPが抗炎症ペプチドを同定するための正確な予測器であり、抗炎症ペプチド治療および生物医学研究の開発に貢献した。用いたデータセットとPreAIPは、http：//kurata14.bio.kyutech.ac.jp/PreAIP/から自由に利用できる。炎症誘発性ペプチド（PIP）は、免疫細胞から分泌されるシグナル伝達分子の一種であり、侵入する病原体に対する防御の第一線を担当する。多くの実験により、PIPはワクチンや免疫療法薬などにおいて重要な役割を果たすことが示されている。ハイスループットな生物実験に時間と費用が掛かることを考えると、効率的なコンピュータ予測は、PIPを短時間にかつ正確に特定するために大きな需要がある。したがって、この研究では、PIP識別性能を向上させるために、ProIn-Fuseと呼ばれる複数の特徴表現を組み合わせた計算モデルを提案した。具体的には、特徴表現学習モデルを利用して、8つのシーケンスエンコーディングスキームを備えたランダムフォレストモデルを利用することにより、確率的予測スコアを計算した。ProIn-Fuseは、確率的予測スコアの線形結合モデルによって構築された。提案手法の汎化性能を独立したテストデータで評価した結果、ProIn-Fuseの精度は0.746であり、これは最新のPIP予測器によって得られた精度よりも10％以上高かった。テストデータによる検証結果は、ProIn-Fuseが既存のPIP予測器よりも正確にPIP識別できることを示した。Webサーバー、データセット、および説明書は、http：//kurata14.bio.kyutech.ac.jp/ProIn-Fuse/から自由にアクセスできる。ProIn-Fuseは、ドラッグデザイン含む幅広いアプリケーションに応用できる。結核（TB）は、結核菌によって引き起こされる疾患である。最近、抗結核ペプチドは抗生物質耐性に対抗するための代替アプローチを提供している。ここでは、ランダムフォレスト（RF）およびサポートベクターマシン（SVM）分類器を用いてアミノ酸配列に由来する複数の特徴ベクトルを統合する効果的な予測器iAntiTB（抗結核ペプチドの識別）を開発した。iAntiTBは、線形回帰を介してRFスコアとSVMスコアを組み合わせて、予測精度を向上させた。ロバストで正確な予測器を作成するために、異なるタイプのネガティブサンプルを使用して2つのデータセットを準備した。iAntiTBは、1番目と2番目のデータセットのトレーニングデータセットでそれぞれ0.896と0.946のAUC値を達成した。iAntiTBは、他の既存の予測器の性能を上回った。このように、iAntiTBは、ペプチド治療および免疫療法に取り組んでいる研究者に役立つロバストで正確な予測器である。利用されたすべてのデータセットとソフトウェアアプリケーションは、http：//kurata14.bio.kyutech.ac.jp/iAntiTB/から自由にアクセスできる。線形B細胞エピトープは、ワクチンの設計、免疫診断テスト、抗体産生、疾患の診断や治療などの免疫学的応用に非常に重要である。線形B細胞エピトープの正確な同定は、数十年の研究にもかかわらず、依然として挑戦的課題のままである。本研究では、配列の進化的特徴や物理化学的特徴等を統合することにより、新規な線形B細胞エピトープ予測モデル（iLBE）を開発した。Wilcoxon順位和検定によって最適化した特徴ベクトル群をランダムフォレスト（RF）アルゴリズムを用いて学習して、線形B細胞エピトープの予測スコアを計算した。ロジスティック回帰を用いてRFスコアを組合せて、予測精度を高めた。iLBEは、トレーニングデータセットで0.809のAUCを達成し、独立のテストデータセットを用いた検定では、既存の予測モデルの性能を超えた。線形B細胞エピトープを同定する強力な計算ツールであるiLBEは、診断テストの開発に有用である。注釈付きデータセットを備えたiLBEモデルのウエブアプリケーションは自由にアクセスできるhttp://kurata14.bio.kyutech.ac.jp/iLBE/。九州工業大学博士学位論文 学位記番号：情工博甲第358号 学位授与年月日：令和3年3月25日1 Introduction|2 Prediction of Anti-Inflammatory Peptides by Integrating Mulptle Complementary Features|3 Prediction of Proinflammatory Peptides by Fusing of Multiple Feature Representations|4 Prediction of Anti-Tubercular Peptides by Exploiting Amino Acid Pattern and Properties|5 Prediction of Linear B-Cell Epitopes by Integrating Sequence and Evolutionary Features|6 Conclusions and Perspectives九州工業大学令和2年