Journal articles: 'Distance learning of biology'

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Myakisheva, Yu V., O. Ya Skazkina, Yu A. Aleshina, R. A. Bogdanova, and I. V. Fedoseykina. "TRADIIONAL AND MODERN EDUCATIONAL TECNOLOGIES IN THE PROCESS OF TEACHING BIOLOGY IN FULL-TIME AND DISTANCE LEARNING." Izvestiya of the Samara Science Centre of the Russian Academy of Sciences. Social, Humanitarian, Medicobiological Sciences 22, no. 74 (2020): 63–69. http://dx.doi.org/10.37313/2413-9645-2020-22-74-63-69.

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The article describes the experience of personal teaching experience in the area of Biology, in the system in the conditions of full-time and distance learning. The authors considered the definitions of the concepts of "distance learning", developed by domestic researchers. The article showsthe essence of distance education, its state at the present level.The traditional and innovative approaches used by the staff of the department in the process of teaching Biology are described.The basic questions of teaching Biology are answered in the process of reading lectures, organizing recitations, organizing competitions and scientific works. A new aspect of the work of teachers is the use of distance learning. Analyzed by the results of the use session of distance learning Biology students. various aspects of the use of distance learning for future doctors are noted.The advantages and disadvantages of this knowledge system are discussed, the results of application are analyzing.

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Tolvanen, Martti, and Mauno Vihinen. "Virtual bioinformatics distance learning suite." Biochemistry and Molecular Biology Education 32, no. 3 (May 2004): 156–60. http://dx.doi.org/10.1002/bmb.2004.494032030336.

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Štaupienė, Rita. "DISTANCE LEARNING COURSE „FROM THE CELL TO THE ORGANISM“." GAMTAMOKSLINIS UGDYMAS / NATURAL SCIENCE EDUCATION 5, no. 1 (April 1, 2008): 43–50. http://dx.doi.org/10.48127/gu-nse/08.5.43.

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After analyzing the possibilities of Moodle surroundings, there was a biology course established for the students of the 9-12th classes, called „From the cell to the organism“. There was also prepared a theoretical material for studying, as well as illustrations, models and examples, individual tasks and tests based on the Lithuanian common programmes of secondary schools and educational standards. The distant course of biology in virtual surroundings is meant to cause an interest in learning process, understanding and enriching the knowledge, as well as self educating and achieving high results. It may be used by biology teachers, explaining the new material, revising and summarizing. Analyzing the course material students may choose the appropriate time and study individually enriching their knowledge and doing individual tasks consulting the teacher. Individual learning allows students to choose individual learning speed and time and be undependable from the other students in a group. Integrating and connecting the basics of several subjects, the possibility of enriching students’ abilities to learn and develop their skills gets higher and the learning process becomes more attractive with the possibility to use various methods of teaching and learning. The aim of distant learning course is to involve students into learning process, using different ways of understanding, improving their abilities to use their knowledge solving the given tasks, communicating and co-operating. The program is directed towards the sphere of problem solving and application of knowledge.

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Hanurani, Hikmawati. "INTEGRASI LITERASI INFORMASI PADA PENDIDIKAN DAN PELATIHAN JARAK JAUH PENDALAMAN MATERI BIOLOGI MADARASAH ALIYAH." JPPS (Jurnal Penelitian Pendidikan Sains) 10, no. 1 (November 28, 2020): 1874. http://dx.doi.org/10.26740/jpps.v10n1.p1874-1888.

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Abstract This study aims to find out how the integration of information literacy substnce in distance training on biological substance for Madrasah Aliyah (MA) in improving information literacy skills in MA biology teachers. In this study the teacher participated in Distance Training on MA biological material that was integrated with information literacy substance. The research sample consisted of 26 participants in Distance Training in Biology Substance for Biology Teacher Aliyah Madrasah in the Bandung Religious Education Training Center originating from the Ministry of Religion of West Java Province. The research design used was pre-experimental design with one group pretest-posttes design. The design procedure of this study was to take the 1st measurement on the subject (pretest) on the ability of mastery of information literacy, then the subject was treated for a certain period (exposure) through education and training in e-learning based distance training deepening of Biology Substance. The second measurement (posttest) was carried out after the treatment was given, and the prestest measurement results were compared with the results of the posttest measurements, using the t-test statistical test. Based on the t-test it can be concluded that "There is a significant difference in the results of self-assessment of information literacy between before and after doing Distance Learning Training".Abstrak Penelitian ini bertujuan untuk mengetahui bagaimana integrasi materi literasi informasi pada kurikulum pendidikan dan pelatihan Diklat jarak jauh (DJJ) pendalaman materi biologi Madrasah Aliyah (MA) dalam meningkatkan keterampilan literasi informasi pada guru biologi MA. Dalam penelitian ini guru mengikuti Diklat Jarak Jauh Pendalaman materi biologi MA yang diintegrasikan dengan materi literasi informasi. Sampel penelitian berjumlah 26 orang peserta Diklat Jarak Jauh Teknis Substantif Pendalaman Materi Biologi Bagi Guru Biologi Madrasah Aliyah di Balai Diklat Keagamaan Bandung yang berasal dari Lingkungan Kementerian Agama Provinsi Jawa Barat. Desain penelitian yang digunakan adalah pre-eksperimental dengan desain One group pretest-posttes design. Prosedur desain penelitian ini adalah melakukan pengukuran ke-1 pada subyek (pretest) terhadap kemampuan penguasaan literasi informasi, kemudian subjek diberi perlakuan untuk jangka waktu tertentu (exposure) melalui pendidikan dan pelatihan diklat jarak jauh pendalaman materi biologi MA berbasis e-learning yang terdiri dari tiga kegiatan belajar. Pengukuran ke-2 (posttest) dilakukan setelah perlakuan diberikan, dan hasil pengukuran prestest dibandingan dengan hasil pengukuran posttest, menggunakan uji statistik Uji-t. Berdasarkan uji-t dapat disimpulkan bahwa “Terdapat perbedaan yang signifikan hasil penilaian diri penguasaan literasi informasi antara sebelum dan sesudah melakukan pembelajaran Diklat Jarak Jauh”

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Nawawi, Nawawi, Isra 'Indar Handayani, and Sukardi Sukardi. "Pickles: Independent Biology Practicum Diffusion and Material Osmosis On the Concept of Substance Transport during the Pandemic Period." JURNAL PENDIDIKAN SAINS (JPS) 9, no. 1 (April 20, 2021): 61. http://dx.doi.org/10.26714/jps.9.1.2021.61-68.

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Teachers had difficulty developing Science Process Skills in biology subjects during the Pandemic at SMA Negeri 1 Sambit Ponorogo. This study aims to 1) analyze the Science Process Skills of class XI IPS 1 student at SMA Negeri 1 Sambit Ponorogo in Biology learning through pickle making practicum, 2) describe the involvement of parents in biology learning during distance learning. The research design used One-Shot Case Study. The research subjects were 27 people from class XI IPS 1 SMA Negeri 1 Sambit Ponorogo. The research instrument used a Science Process Skills observation sheet, an independent practicum assessment sheet, and an online questionnaire for parents & students. The analysis was carried out by descriptive quantitative. Based on the implementation results, it is known that Science Process Skills are categorized as good in independent practicum activities, where the ability to apply is 89.91%, concludes 78.70%, proposes 90.74% hypotheses, predicts 77.78%, and observes 92.59%. While 62.59% of parents are less involved in helping students while learning from home, this affects the success of distance learning. The conclusion of this research is; 1) Practicum making events can be used in applying Science Process skills of Class XI IPS 1 student at SMA Negeri 1 Sambit Ponorogo, 2) Parental involvement in biology learning during distance learning in Class XI IPS 1 SMA Negeri 1 Sambit Ponorogo needs to be improved, because of involvement parents are still very low during distance learning in class XI IPS 1 SMA Negeri 1 Sambit Ponorogo.

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Quinn, Frances Catherine. "Learning in First-Year Biology: Approaches of Distance and On-Campus Students." Research in Science Education 41, no. 1 (December 10, 2009): 99–121. http://dx.doi.org/10.1007/s11165-009-9148-7.

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Permana, Fendy Hardian, Ellisa Sukma, and Poncojari Wahyono. "The use of distance learning through whatsapp and google meeting to identify differences in biology learning outcomes." Biosfer 14, no. 1 (April 30, 2021): 86–98. http://dx.doi.org/10.21009/biosferjpb.20094.

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The COVID-19 pandemic has turned face-to-face learning into distance learning using online media. Educational institutions strive to provide a system to meet students' needs in conducting distance learning using online media. Educational institutions also try to familiarize teachers and students with using online media in the learning process. Educators and students can utilize distance learning media in Whatsapp groups and google meetings to carry out the learning process. This study aims to determine whether there are differences in student biology learning outcomes between the experimental class I learning using WhatsApp group and the practical class II learning using google meetings. This type of research is a quasi-experimental design with a posttest-only control design. The study population was all class X MIPA (mathematics and science) Public Senior High School 02 Batu (There are five classes). The sampling technique used purposive sampling. The experimental class I was class X MIPA 4 with 28 students and the practical class II was class X MIPA 5 with 28 students. Descriptive and inferential statistical data analysis techniques using Ms. Excell and the Independent Sample T-Test (SPSS Statistics 17.0). This study indicates that there is a difference in the average learning outcomes of students between experimental class I (74.75) who carry out learning using WhatsApp group and experimental class II (64.75) who carry out learning using google meetings. In conclusion, learning using the WhatsApp group is better.

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Jørgensen, Sven. "Announcement — Ecological Modeling, a Distance Learning Course." Scientific World JOURNAL 3 (2003): 867–69. http://dx.doi.org/10.1100/tsw.2003.86.

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Lozovskaya, Marina V., and Yuri V. , Nesterov. "JUSTIFICATION OF THE ADDITIONAL EDUCATION PROGRAM OF BIOLOGY TEACHERS USING INNOVATIVE ENVIRONMENT OF DISTANCE LEARNING." HUMANITARIAN RESEARCHES 61, no. 1 (2017): 106–9. http://dx.doi.org/10.21672/1818-4936-2017-61-1-106-109.

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Vaughan, Martin A., and Richard L. Doolittle. "LECTURE/LAB COURSE IN SPORTS BIOLOGY AND LIFE FITNESS THROUGH MULTIMEDIA/DISTANCE LEARNING FORMAT 663." Medicine &amp Science in Sports &amp Exercise 29, Supplement (May 1997): 116. http://dx.doi.org/10.1097/00005768-199705001-00662.

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Romaniuk, R. K., R. P. Vlasenko, V. A. Yakovleva, and V. S. Kostiuk. "THE FORMATION OF FUTURE BIOLOGY AND GEOGRAPHY TEACHES’ WILLINGNESS FOR IMPLEMENTING DISTANCE AND MIXED LEARNING." Innovate Pedagogy 1, no. 30 (2020): 129–37. http://dx.doi.org/10.32843/2663-6085/2020/30-1.26.

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Xu, Jinbo, and Sheng Wang. "Analysis of distance‐based protein structure prediction by deep learning in CASP13." Proteins: Structure, Function, and Bioinformatics 87, no. 12 (September 13, 2019): 1069–81. http://dx.doi.org/10.1002/prot.25810.

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Khaleyla, Firas, Wisanti Wisanti, Reni Ambarwati, Dwi Anggorowati Rahayu, and Eva Kristinawati Putri. "Software preference for online learning of science and biology teachers under COVID-19 pandemic." JPBI (Jurnal Pendidikan Biologi Indonesia) 7, no. 1 (March 31, 2021): 35–42. http://dx.doi.org/10.22219/jpbi.v7i1.14253.

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As measure against the rapid spreading of coronavirus disease 2019 (COVID-19) which now has reached global level, Indonesian government established Large-scale Social Distancing (LsSD). As consequence, learning method used in junior and senior high school is substituted from face-to-face learning in class to online distance learning, including for science and biology. This study was conducted to know software preference used by science and biology junior and senior high school teachers for online learning during LsSD measure. A total of 189 science and biology junior and senior high school teachers from various area had given their response via questionnaire. Data was analyzed using quantitative descriptive method. About 57% respondents had never manage online learning before COVID-19 pandemic while the remaining 43% had experience in managing one before, however almost all managed online learning. Non-paid software used the most (81%) among respondents to manage online science/biology learning. Software types used were social networking (64%) especially WhatsApp, learning management system (LMS) (51%) especially Google Classroom, teleconference applications (12%), and assessment software outside of LMSs (15%). Software chosen were mostly non-paid, easily accessed by all people, already familiar among Indonesian, and its interface were easily mastered.

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Cuvitoglu, Ali, Joseph X. Zhou, Sui Huang, and Zerrin Isik. "Predicting drug synergy for precision medicine using network biology and machine learning." Journal of Bioinformatics and Computational Biology 17, no. 02 (April 2019): 1950012. http://dx.doi.org/10.1142/s0219720019500124.

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Identification of effective drug combinations for patients is an expensive and time-consuming procedure, especially for in vitro experiments. To accelerate the synergistic drug discovery process, we present a new classification model to identify more effective anti-cancer drug pairs using in silico network biology approach. Based on the hypotheses that the drug synergy comes from the collective effects on the biological network, therefore, we developed six network biology features, including overlap and distance of drug perturbation network, that were derived by using individual drug-perturbed transcriptome profiles and the relevant biological network analysis. Using publicly available drug synergy databases and three machine-learning (ML) methods, the model was trained to discriminate the positive (synergistic) and negative (nonsynergistic) drug combinations. The proposed models were evaluated on the test cases to predict the most promising network biology feature, which is the network degree activity, i.e. the synergistic effect between drug pairs is mainly accounted by the complementary signaling pathways or molecular networks from two drugs.

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Pererva, Victoria. "REMOTE SPECIAL COURSE «LATIN. BOTANY TERMINOLOGY» AS A MEANS OF BECOMING A PROFESSIONAL AND TERMINOLOGICAL COMPETENCE OF BIOLOGY TEACHER." Osvitolohiya, no. 8 (2019): 81–88. http://dx.doi.org/10.28925/2226-3012.2019.8.8188.

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The essence and advantages of distance learning as a form of organization of independent work in the professional training of future specialists are revealed. Formation of a professional term system is considered as a prerequisite for the formation of professional-terminological competence of a future teacher. Independent work Outside the classroom process is expanding the amount of material to be absorbed (both theoretical and applied), assisting in preparing for the tests and examinations in professional disciplines. Today, integration of modern pedagogical and information technologies, their wide introduction into the educational process is very important. In the system of vocational education, issues of the culture of professional communication, are of particular importance. Imperfect knowledge of the professional terminology leads to the appearance of a significant number of typical errors, reducing the level of the culture of speech. An important role in mastering students’ knowledge of the special course «Latina. Botany terminology» is given to independent work, which gradually becomes one of the leading forms. Application of new information and telecommunication technologies in the educational process, creation, and use of modern electronic teaching aids and distance courses solve complex tasks of forming a single educational information environment. Distance learning course «Latina. Botany Terminology» for future biology teacher is allows each student to work in an individual mode under the guidance of teachers. The content and structure of the distance e-learning course «Latin. Botany Terminology» for students of the Natural Department of Pedagogical Institutions of Higher Education is shown. The purpose of the distance special course, is to enrich the personality language culture, understanding the semantics and etymologies of biological terms.

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Moritoh, Yoshio, Yoshiro Imai, and Tetsuo Hattori. "Evaluation for distance learning scheme on distributed multiple server system." Artificial Life and Robotics 19, no. 1 (December 21, 2013): 61–67. http://dx.doi.org/10.1007/s10015-013-0131-z.

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Funa, Aaron A., and Frederick T. Talaue. "Constructivist Learning Amid the COVID-19 Pandemic: Investigating Students’ Perceptions of Biology Self-Learning Modules." International Journal of Learning, Teaching and Educational Research 20, no. 3 (March 30, 2021): 250–64. http://dx.doi.org/10.26803/ijlter.20.3.15.

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Modes of teaching and learning have had to rapidly shift amid the COVID-19 pandemic. As an emergency response, students from Philippine public schools were provided learning modules based on a minimized list of essential learning competencies in Biology. Using a cross-sectional survey method, we investigated students’ perceptions of the Biology self-learning modules (BSLM) that were designed in print and digitized formats according to a constructivist learning approach. Senior high school STEM students from grades 11 (n = 117) and 12 (n = 104) participated in a survey using a 3-point Likert-scale questionnaire uploaded online through Google Forms. The survey results indicate that majority of the students perceived the modules positively, suggesting that aspects of the modules that were salient to students corresponded to essential elements of constructivist pedagogies. However, during interviews, students reported several difficulties in learning with BSLM as it was constrained by, to name a few, the use of unfamiliar words, lack of access to supporting resources, slow internet connection, and time constraints. To address these problems, teachers reported that they gave deadline extensions, complemented modules with other channels of support, and used online and offline platforms for reaching out to students to answer their queries and plan out their schedule for the week. The findings across the data sources point to the complex demands of emergency distance education that teachers, as curriculum designers and enactors, need to bear in mind in order to craft productive pedagogies, constructivist or otherwise, during this unprecedented time.

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Liptak, P., and A. Kiss. "Constructing Unrooted Phylogenetic Trees with Reinforcement Learning." Studia Universitatis Babeș-Bolyai Informatica 66, no. 1 (July 1, 2021): 37. http://dx.doi.org/10.24193/subbi.2021.1.03.

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With the development of sequencing technologies, more and more amounts of sequence data are available. This poses additional challenges, such as processing them is usually a complex and time-consuming computational task. During the construction of phylogenetic trees, the relationship between the sequences is examined, and an attempt is made to represent the evolutionary relationship. There are several algorithms for this problem, but with the development of computer science, the question arises as to whether new technologies can be exploited in these areas of computational biology. In the following publication, we investigate whether the reinforced learning model of machine learning can generate accurate phylogenetic trees based on the distance matrix.

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Pertry, Ine, Silvia Sabbadini, Sofie Goormachtig, Yvonne Lokko, Godelieve Gheysen, Sylvia Burssens, and Bruno Mezzetti. "Biosafety capacity building: experiences and challenges from a distance learning approach." New Biotechnology 31, no. 1 (January 2014): 64–68. http://dx.doi.org/10.1016/j.nbt.2013.08.008.

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Welbaum, Gregory E. "Distance-Learning Courses on Seed Biology and Technology Available from Public Agricultural Universities in the United States." Journal of New Seeds 10, no. 4 (November 24, 2009): 233–35. http://dx.doi.org/10.1080/15228860903311422.

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Hou, Jie, Tianqi Wu, Renzhi Cao, and Jianlin Cheng. "Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13." Proteins: Structure, Function, and Bioinformatics 87, no. 12 (April 25, 2019): 1165–78. http://dx.doi.org/10.1002/prot.25697.

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Alekhina, E. A., and N. A. Makarova. "Specifics of Organizing Distance Learning of Organic Chemistry at a Pedagogical University in Conditions of a Pandemic Coronavirus Infection." Open Education 24, no. 5 (October 28, 2020): 36–46. http://dx.doi.org/10.21686/1818-4243-2020-5-36-46.

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The purpose of the study is to identify the features of distance learning of organic chemistry in Omsk State Pedagogical University (OmSPU) during the coronavirus pandemic in conditions of self-isolation. Materials and methods. To solve this problem the authors have analyzed pedagogical literature and practical experience to organize distance learning of students, and self-analysis experience of distance learning of organic chemistry for the bachelors of the 3rd course at Omsk State Pedagogical University (direction “Pedagogical Education”, profile “Biology and Chemistry”) using the electronic information and educational environment of the University on the Moodle platform, followed by generalization and systematization of the identified features. Based on a survey of students, their attitude to distance learning of organic chemistry is revealed, and the problems and difficulties faced by students and lecturers are shown.Results. The possibilities of conducting lectures and practical classes in organic chemistry in the BigBlueButton video conference format are described. Methodological techniques of using static and quasiinteractive multimedia presentations and demo videos, developed by the authors are shown. The possibilities of the course elements “chat” and “forum” on the OmSPU portal and WhatsApp messenger for solving organizational issues, consulting and correcting students’ activities, building their individual educational trajectory are revealed. The article shows the features of practical training, methodological techniques of using video demonstrations. Based on the analysis of the results of the survey of students and their exam answers, the most important disadvantages of distance learning in organic chemistry were identified. Learning in an electronic environment does not allow you to fully develop the skills necessary for future lecturers to communicate with students. The damage is caused to the development of oral speech, the ability to correctly use chemical terminology, explain the material using various teaching tools, and the formation of skills for group and pair interaction. Replacing a real chemical experiment with a virtual one does not allow you to form experimental skills specific to organic chemistry. Therefore, according to the authors, when studying organic chemistry, distance learning must necessarily be combined with traditional, with classes in a chemical laboratory. Conclusion. The most effective tools for organizing distance learning of organic chemistry are identified, taking into account the specifics of the discipline. The ways of solving the difficulties faced by students and lecturers in the conditions of distance learning are shown. The developed and described methods of working with students in the organization of distance learning of organic chemistry can be used to improve the effectiveness of the development of the content of the discipline and the formation of professional competencies of future lecturers.

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Ma, Xuesi, Baohang Xi, Yi Zhang, Lijuan Zhu, Xin Sui, Geng Tian, and Jialiang Yang. "A Machine Learning-based Diagnosis of Thyroid Cancer Using Thyroid Nodules Ultrasound Images." Current Bioinformatics 15, no. 4 (June 11, 2020): 349–58. http://dx.doi.org/10.2174/1574893614666191017091959.

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Background:: Ultrasound test is one of the routine tests for the diagnosis of thyroid cancer. The diagnosis accuracy depends largely on the correct interpretation of ultrasound images of thyroid nodules. However, human eye-based image recognition is usually subjective and sometimes error-prone especially for less experienced doctors, which presents a need for computeraided diagnostic systems. Objective: : To our best knowledge, there is no well-maintained ultrasound image database for the Chinese population. In addition, though there are several computational methods for image-based thyroid cancer detection, a comparison among them is missing. Finally, the effects of features like the choice of distance measures have not been assessed. The study aims to give the improvement of these limitations and proposes a highly accurate image-based thyroid cancer diagnosis system, which can better assist doctors in the diagnosis of thyroid cancer. Methods:: We first establish a novel thyroid nodule ultrasound image database consisting of 508 images collected from the Third Hospital of Hebei Medical University in China. The clinical information for the patients is also collected from the hospital, where 415 patients are diagnosed to be benign and 93 are malignant by doctors following a standard diagnosis procedure. We develop and apply five machine learning methods to the dataset including deep neural network, support vector machine, the center clustering method, k-nearest neighbor, and logistic regression. Results:: Experimental results show that deep neural network outperforms other diagnosis methods with an average cross-validation accuracy of 0.87 in 10 runs. Meanwhile, we also explore the performance of four image distance measures including the Euclidean distance, the Manhattan distance, the Chebyshev distance, and the Minkowski distance, among which the Chebyshev distance is the best. The resource can be directly used to aid doctors in thyroid cancer diagnosis and treatment. Conclusions: : The paper establishes a novel thyroid nodule ultrasound image database and develops a high accurate image-based thyroid cancer diagnosis system which can better assist doctors in the diagnosis of thyroid cancer.

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Arato, Jozsef, and W. Tecumseh Fitch. "Phylogenetic signal in the vocalizations of vocal learning and vocal non-learning birds." Philosophical Transactions of the Royal Society B: Biological Sciences 376, no. 1836 (September 6, 2021): 20200241. http://dx.doi.org/10.1098/rstb.2020.0241.

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Some animal vocalizations develop reliably in the absence of relevant experience, but an intriguing subset of animal vocalizations is learned: they require acoustic models during ontogeny in order to develop, and the learner's vocal output reflects those models. To what extent do such learned vocalizations reflect phylogeny? We compared the degree to which phylogenetic signal is present in vocal signals from a wide taxonomic range of birds, including both vocal learners (songbirds) and vocal non-learners. We used publically available molecular phylogenies and developed methods to analyse spectral and temporal features in a carefully curated collection of high-quality recordings of bird songs and bird calls, to yield acoustic distance measures. Our methods were initially developed using pairs of closely related North American and European bird species, and then applied to a non-overlapping random stratified sample of European birds. We found strong similarity in acoustic and genetic distances, which manifested itself as a significant phylogenetic signal, in both samples. In songbirds, both learned song and (mostly) unlearned calls allowed reconstruction of phylogenetic trees nearly isomorphic to the phylogenetic trees derived from genetic analysis. We conclude that phylogeny and inheritance constrain vocal structure to a surprising degree, even in learned birdsong. This article is part of the theme issue ‘Vocal learning in animals and humans’.

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Stephens, Philip J. "Narrated Video Clips Improve Student Learning." World Journal of Education 7, no. 3 (June 6, 2017): 14. http://dx.doi.org/10.5430/wje.v7n3p14.

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The purpose of this study is to determine whether viewing narrated video clips improves student learning. The studywas conducted with undergraduate, mostly Biology majors, in an Animal Physiology course held in successivesemesters. When both classes were given the same face-to-face lectures and identical online resources theirperformance on an exam with the same multiple choice questions was not statistically different (two-tailed, unpairedt-test). However, when one group was also given unlimited online access to narrated video clips, these studentsperformed statistically better on a second exam with identical multiple choice questions. An attitudinal surveyshowed that students used the video clips as an introduction to the interactive animations and simulations and asstandalone mini-lectures, and they indicated that viewing the clips was the best and most efficient way to learnphysiological concepts. While this study used narrated video clips to augment traditional face-to-face instruction,they could be used in a flipped-class, a blended class, and for distance learning.

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Papaneophytou, Christos. "A distance learning enzyme assay and kinetics laboratory in the time of COVID ‐19." Biochemistry and Molecular Biology Education 48, no. 5 (June 30, 2020): 430–32. http://dx.doi.org/10.1002/bmb.21364.

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Mohanapriya, D., and Dr R. Beena. "Predicting Drug Indications and Side Effects Using Deep Learning and Transfer Learning." Alinteri Journal of Agriculture Sciences 36, no. 1 (May 17, 2021): 281–89. http://dx.doi.org/10.47059/alinteri/v36i1/ajas21042.

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In the area of biology, text mining is commonly used since it obtains the unknown relationship among medicines, phenotypes and syndromes from much information. Enhanced Topic modeling with Improved Predict drug Indications and Side effects using Topic modelling and Natural language processing (ETP-IPISTON) has been employed to predict the drug-phenotype and drug-side effect association. Initially, corpus documents are collected from the literature data and the topics in the data are modeled using logistic Linear Discriminative Analysis (LDA) and Bi-directional Long-Short Term Memory-Conditional Random Field (BILSTM-CRF). From the sentences in the literature data, a dependency graph was constructed which discovered the relations between gene and drug. The product of the drug on phenotype rule was identified by the Gene Regulation Score (GRS) which creates the drug-topic probability matrix. The probability matrix and a syntactic distance measure was processed in Classification and Regression Tree (CART), Naïve Bayes (NB), logistic regression and Convolutional Neural Network (CNN) classifiers for estimating the drug-gene and drug-side effects. Besides the literature data, social media offers various promising resources with massive volume of data that can be useful in the drug-phenotype and drug-side effect association prediction. So in this paper, drug information with gene, disease and side effects are extracted from different social media such as Twitter, Facebook and LinkedIn and it can be used with the literature data to provide more relevant disease and drug relations. In addition to this, topic modeling with transfer learning is introduced to consider the element categories, probability of overlapping elements and deep contextual significance of a text for better modeling of topics. The topic modeling with transfer learning shares as much knowledge as possible between the literature data and social media information for topic modeling. The topics from social media and literature data are used for creating the drug-topic matrix. The probability matrix and syntactic distance measure are given as input to CART, NB, logistic regression and CNN for estimating the drug-gene and drug-side effect association. This proposed work is named as Enhanced Topic Modeling with Transfer Leaning- IPISTON (ETPTL-IPISTON). The simulation findings exhibit that the efficiency of ETPTL-IPISTON than the traditional methods.

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Sadikin, Ali, and Afreni Hamidah. "Pembelajaran Daring di Tengah Wabah Covid-19." BIODIK 6, no. 2 (June 30, 2020): 109–19. http://dx.doi.org/10.22437/bio.v6i2.9759.

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Pademi Covid-19 has disturbed the learning process in a face-to-face manner. Therefore online learning solutions need to be sought as an answer to these problems. The aim of the study was to obtain an overview of the implementation of online learning in the Biology Education Study Program of the Teaching and Education Faculty (FKIP) of Jambi University as an effort to suppress the spread of covid-19 in the campus environment. Research subjects were students of Biology Education Study Program. Data collected by telephone interview. Data analysis was performed using the interactive analysis technique of Miles & Huberman. The results showed that: (1) students already have the basic facilities needed to take part in online learning; (2) online learning has flexibility in its implementation and is able to encourage the emergence of learning independence and motivation to be more active in learning; and (3) distance learning encourages the emergence of social distancing behavior and minimizes the emergence of student crowds so that it is deemed able to reduce the potential for the spread of Covid-19 in the campus environment. Abstrak. Pademi covid-19 telah mengganggu proses pembelajaran secara konvensional. Maka diperlukan solusi untuk menjawab permasalahan tersebut. Pembelajaran secara daring adalah salah satu alternatif yang dapat mengatasi masalah tersebut. Tujuan penelitian adalah untuk memperoleh gambaran pelaksanaan pembelajaran daring di Prodi Pendidikan Biologi FKIP Universitas Jambi sebagai upaya menekan penyebaran covid-19 di Perguruan Tinggi. Subjek penelitian adalah mahasiswa Prodi Pendidikan Biologi. Data dikumpulkan dengan wawancara melalui zoom cloud meeting. Analisis data dilakukan menggunakan teknik analisis interaktif Miles & Huberman. Hasil penelitian menunjukkan bahwa: (1) mahasiswa telah memiliki fasilitas-fasilitas dasar yang dibutuhkan untuk mengikuti pembelajaran daring; (2) pembelajaran daring memiliki fleksibilitas dalam pelaksanaannya dan mampu mendorong munculnya kemandirian belajar dan motivasi untuk lebih aktif dalam belajar; dan (3) pembelajaran jarak jauh mendorong munculnya perilaku social distancing dan meminimalisir munculnya keramaian mahasiswa sehingga dianggap dapat mengurangi potensi penyebaran Covid-19 di lingkungan kampus. Lemahnya pengawasan terhadap mahasiswa, kurang kuatnya sinyal di daerah pelosok, dan mahalnya biaya kuota adalah tantangan tersendiri dalam pembelajaran daring.

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Quinn, Frances, and Sarah Stein. "Relationships between learning approaches and outcomes of students studying a first-year biology topic on-campus and by distance." Higher Education Research & Development 32, no. 4 (August 2013): 617–31. http://dx.doi.org/10.1080/07294360.2012.704902.

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Abrams, Zachary B., Caitlin E. Coombes, Suli Li, and Kevin R. Coombes. "Mercator: a pipeline for multi-method, unsupervised visualization and distance generation." Bioinformatics 37, no. 17 (January 30, 2021): 2780–81. http://dx.doi.org/10.1093/bioinformatics/btab037.

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Abstract Summary Unsupervised machine learning provides tools for researchers to uncover latent patterns in large-scale data, based on calculated distances between observations. Methods to visualize high-dimensional data based on these distances can elucidate subtypes and interactions within multi-dimensional and high-throughput data. However, researchers can select from a vast number of distance metrics and visualizations, each with their own strengths and weaknesses. The Mercator R package facilitates selection of a biologically meaningful distance from 10 metrics, together appropriate for binary, categorical and continuous data, and visualization with 5 standard and high-dimensional graphics tools. Mercator provides a user-friendly pipeline for informaticians or biologists to perform unsupervised analyses, from exploratory pattern recognition to production of publication-quality graphics. Availabilityand implementation Mercator is freely available at the Comprehensive R Archive Network (https://cran.r-project.org/web/packages/Mercator/index.html).

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Teramoto, Reiji. "Prediction of Alzheimer's diagnosis using semi-supervised distance metric learning with label propagation." Computational Biology and Chemistry 32, no. 6 (December 2008): 438–41. http://dx.doi.org/10.1016/j.compbiolchem.2008.07.030.

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Xu, Yonghui, Huaqing Min, Hengjie Song, and Qingyao Wu. "Multi-instance multi-label distance metric learning for genome-wide protein function prediction." Computational Biology and Chemistry 63 (August 2016): 30–40. http://dx.doi.org/10.1016/j.compbiolchem.2016.02.011.

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Afrahamiryano, Afrahamiryano. "Analisis Faktor-Faktor Kesulitan Pembelajaran Online Mata Kuliah Biokimia di Masa Pandemi COVID-19." Eduscience Development Journal (EDJ) 2, no. 1 (August 31, 2021): 1–6. http://dx.doi.org/10.36665/edj.v2i1.354.

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The distance learning system implemented by all universities during the Covid-19 pandemic has a number of challenges for lecturers and students. This article describes empirically the application of online learning in the Biochemistry course, Biology Education study program, Mahaputra Muhammad Yamin University. This pandemic has brought a paradigm shift in learning, namely developing and empowering various media in the learning environment, one of which is the internet facility. The results showed that the use of the internet as a learning medium in general was able to increase student learning motivation in taking Biochemistry courses, but on the other hand it created a dilemma where students had difficulty understanding concepts. This is due to the limited means of supporting the learning process such as textbooks. Difficulties are also experienced by lecturers, this difficulty creates a pleasant learning atmosphere in the midst of limited facilities and learning facilities and infrastructure.

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Passmore, G. G., M. A. Owen, and K. Prabakaran. "Empirical Evidence of the Effectiveness of Concept Mapping as a Learning Intervention for Nuclear Medicine Technology Students in a Distance Learning Radiation Protection and Biology Course." Journal of Nuclear Medicine Technology 39, no. 4 (November 11, 2011): 284–89. http://dx.doi.org/10.2967/jnmt.111.093062.

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Brunelli, Elvira, and Rachele Macirella. "Exploring the critical points of teaching STEM subjects in the time of COVID 19: the experience of the course "Microscopy Techniques for Forensic Biology"." F1000Research 10 (April 12, 2021): 89. http://dx.doi.org/10.12688/f1000research.28455.2.

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Background: The University was among the first structures to be hit by the health emergency, transferring all its teaching and research activities remotely. It was not easy for teachers and students to find themselves suddenly shifted into different teaching and socializing context. Results: This article describes and analyzes the online teaching experience carried out for the course of Microscopy Techniques for Forensic Biology offered as a part of the Master's degree program in Biology at the University of Calabria (Italy). A cross-sectional survey (pilot study) was designed to investigate the accessibility of distance learning along with an evaluation of adjustments needed for the conversion from offline to online instruction. Particular attention has been paid to learning material and lesson duration, with specific emphasis on practical activities. Conclusions: The author's intent is that of opening a comparison between the strengths and weaknesses that emerged in this experience, highlighting, in particular, how the educational relationship between teacher and student has changed.

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Brunelli, Elvira, and Rachele Macirella. "Exploring the critical points of teaching STEM subjects in the time of COVID 19: the experience of the course "Microscopy Techniques for Forensic Biology"." F1000Research 10 (February 10, 2021): 89. http://dx.doi.org/10.12688/f1000research.28455.1.

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Background: The University was among the first structures to be hit by the health emergency, transferring all its teaching and research activities remotely. It was not easy for teachers and students to find themselves suddenly shifted into different teaching and socializing context. Results: This article describes and analyzes the online teaching experience carried out for the course of Microscopy Techniques for Forensic Biology offered as a part of the Master's degree program in Biology at the University of Calabria (Italy). A cross-sectional survey (pilot study) was designed to investigate the accessibility of distance learning along with an evaluation of adjustments needed for the conversion from offline to online instruction. Particular attention has been paid to learning material and lesson duration, with specific emphasis on practical activities. Conclusions: The author's intent is that of opening a comparison between the strengths and weaknesses that emerged in this experience, highlighting, in particular, how the educational relationship between teacher and student has changed.

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Jorgensen, Sven Erik. "Announcement of Ecological Modeling, a Distance Learning Course for the International Science Community." Scientific World JOURNAL 1 (2001): 146–47. http://dx.doi.org/10.1100/tsw.2001.21.

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Nather, A., G. O. Philips, and J. Morales. "IAEA/NUS Distance Learning Diploma Training Course for Tissue Bank Operators – Past, Present and Future." Cell and Tissue Banking 4, no. 2-4 (2003): 77–84. http://dx.doi.org/10.1023/b:catb.0000007033.55205.72.

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Bazgir, Omid, Souparno Ghosh, and Ranadip Pal. "Investigation of REFINED CNN ensemble learning for anti-cancer drug sensitivity prediction." Bioinformatics 37, Supplement_1 (July 1, 2021): i42—i50. http://dx.doi.org/10.1093/bioinformatics/btab336.

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Abstract Motivation Anti-cancer drug sensitivity prediction using deep learning models for individual cell line is a significant challenge in personalized medicine. Recently developed REFINED (REpresentation of Features as Images with NEighborhood Dependencies) CNN (Convolutional Neural Network)-based models have shown promising results in improving drug sensitivity prediction. The primary idea behind REFINED-CNN is representing high dimensional vectors as compact images with spatial correlations that can benefit from CNN architectures. However, the mapping from a high dimensional vector to a compact 2D image depends on the a priori choice of the distance metric and projection scheme with limited empirical procedures guiding these choices. Results In this article, we consider an ensemble of REFINED-CNN built under different choices of distance metrics and/or projection schemes that can improve upon a single projection based REFINED-CNN model. Results, illustrated using NCI60 and NCI-ALMANAC databases, demonstrate that the ensemble approaches can provide significant improvement in prediction performance as compared to individual models. We also develop the theoretical framework for combining different distance metrics to arrive at a single 2D mapping. Results demonstrated that distance-averaged REFINED-CNN produced comparable performance as obtained from stacking REFINED-CNN ensemble but with significantly lower computational cost. Availability and implementation The source code, scripts, and data used in the paper have been deposited in GitHub (https://github.com/omidbazgirTTU/IntegratedREFINED). Supplementary information Supplementary data are available at Bioinformatics online.

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Chen, Jing, Yuan Yan Tang, C. L. Philip Chen, Bin Fang, Zhaowei Shang, and Yuewei Lin. "Similarity Measure Learning in Closed-Form Solution for Image Classification." Scientific World Journal 2014 (2014): 1–15. http://dx.doi.org/10.1155/2014/747105.

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Adopting a measure is essential in many multimedia applications. Recently, distance learning is becoming an active research problem. In fact, the distance is the natural measure for dissimilarity. Generally, a pairwise relationship between two objects in learning tasks includes two aspects: similarity and dissimilarity. The similarity measure provides different information for pairwise relationships. However, similarity learning has been paid less attention in learning problems. In this work, firstly, we propose a general framework for similarity measure learning (SML). Additionally, we define a generalized type of correlation as a similarity measure. By a set of parameters, generalized correlation provides flexibility for learning tasks. Based on this similarity measure, we present a specific algorithm under the SML framework, called correlation similarity measure learning (CSML), to learn a parameterized similarity measure over input space. A nonlinear extension version of CSML, kernel CSML, is also proposed. Particularly, we give a closed-form solution avoiding iterative search for a local optimal solution in the high-dimensional space as the previous work did. Finally, classification experiments have been performed on face databases and a handwritten digits database to demonstrate the efficiency and reliability of CSML and KCSML.

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Ruffolo, Jeffrey A., Carlos Guerra, Sai Pooja Mahajan, Jeremias Sulam, and Jeffrey J. Gray. "Geometric potentials from deep learning improve prediction of CDR H3 loop structures." Bioinformatics 36, Supplement_1 (July 1, 2020): i268—i275. http://dx.doi.org/10.1093/bioinformatics/btaa457.

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Abstract Motivation Antibody structure is largely conserved, except for a complementarity-determining region featuring six variable loops. Five of these loops adopt canonical folds which can typically be predicted with existing methods, while the remaining loop (CDR H3) remains a challenge due to its highly diverse set of observed conformations. In recent years, deep neural networks have proven to be effective at capturing the complex patterns of protein structure. This work proposes DeepH3, a deep residual neural network that learns to predict inter-residue distances and orientations from antibody heavy and light chain sequence. The output of DeepH3 is a set of probability distributions over distances and orientation angles between pairs of residues. These distributions are converted to geometric potentials and used to discriminate between decoy structures produced by RosettaAntibody and predict new CDR H3 loop structures de novo. Results When evaluated on the Rosetta antibody benchmark dataset of 49 targets, DeepH3-predicted potentials identified better, same and worse structures [measured by root-mean-squared distance (RMSD) from the experimental CDR H3 loop structure] than the standard Rosetta energy function for 33, 6 and 10 targets, respectively, and improved the average RMSD of predictions by 32.1% (1.4 Å). Analysis of individual geometric potentials revealed that inter-residue orientations were more effective than inter-residue distances for discriminating near-native CDR H3 loops. When applied to de novo prediction of CDR H3 loop structures, DeepH3 achieves an average RMSD of 2.2 ± 1.1 Å on the Rosetta antibody benchmark. Availability and Implementation DeepH3 source code and pre-trained model parameters are freely available at https://github.com/Graylab/deepH3-distances-orientations. Supplementary information Supplementary data are available at Bioinformatics online.

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Pakhrin, Subash C., Bikash Shrestha, Badri Adhikari, and Dukka B. KC. "Deep Learning-Based Advances in Protein Structure Prediction." International Journal of Molecular Sciences 22, no. 11 (May 24, 2021): 5553. http://dx.doi.org/10.3390/ijms22115553.

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Obtaining an accurate description of protein structure is a fundamental step toward understanding the underpinning of biology. Although recent advances in experimental approaches have greatly enhanced our capabilities to experimentally determine protein structures, the gap between the number of protein sequences and known protein structures is ever increasing. Computational protein structure prediction is one of the ways to fill this gap. Recently, the protein structure prediction field has witnessed a lot of advances due to Deep Learning (DL)-based approaches as evidenced by the success of AlphaFold2 in the most recent Critical Assessment of protein Structure Prediction (CASP14). In this article, we highlight important milestones and progresses in the field of protein structure prediction due to DL-based methods as observed in CASP experiments. We describe advances in various steps of protein structure prediction pipeline viz. protein contact map prediction, protein distogram prediction, protein real-valued distance prediction, and Quality Assessment/refinement. We also highlight some end-to-end DL-based approaches for protein structure prediction approaches. Additionally, as there have been some recent DL-based advances in protein structure determination using Cryo-Electron (Cryo-EM) microscopy based, we also highlight some of the important progress in the field. Finally, we provide an outlook and possible future research directions for DL-based approaches in the protein structure prediction arena.

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Mahaffey, Angela L. "Social distance teaching and learning: An online DNA nucleotide binding lab experience for health sciences and non‐major students." Biochemistry and Molecular Biology Education 48, no. 5 (August 24, 2020): 506–8. http://dx.doi.org/10.1002/bmb.21426.

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Maratkyzy, Saniya, Ainur Turdybayevna Baikenzheyeva, and B. K. Baizhanova. "BUSINESS AND EDUCATION: OPPORTUNITIES FOR TRAINING." Bulletin of Toraighyrov University. Pedagogics series, no. 4.2020 (January 11, 2021): 358–66. http://dx.doi.org/10.48081/knkb5644.

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This article examines the area of intersection of interests of universities and employers, shows the qualifications of graduates of regional universities, their readiness for innovative professional activities, work on the latest path and career growth. The possibility of interaction in the active participation of universities in activities to increase the competitiveness and growth potential of companies, as well as in the joint creation of business incubators is shown. The dynamics of employment of graduates of the Kyzylorda State University named after Korkyt Ata on biological education, employment opportunities for graduates were noted. The most promising areas of business and education development in general are shown. The technologies of distance learning and social networks are shown, new opportunities for online learning are presented; the formation of individual educational trajectories and interdisciplinary integrated learning. It shows the training of specialists in the field of biology on the basis of educational programs, the rights of teachers, if possible, the formation of the main elements of doing business in the process of teaching general education subjects for students of the educational program of biology, promoting the development of the local and national economy in educational programs. training specialists capable of planning and conducting research in this area, including those capable of commercializing the results.

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Zhang, Chen, Jie He, Ziyang Liu, Lu Xing, and Yinhai Wang. "Travel demand and distance analysis for free-floating car sharing based on deep learning method." PLOS ONE 14, no. 10 (October 16, 2019): e0223973. http://dx.doi.org/10.1371/journal.pone.0223973.

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Jackson, Rhydon, Debra Knisley, Cecilia McIntosh, and Phillip Pfeiffer. "Predicting Flavonoid UGT Regioselectivity." Advances in Bioinformatics 2011 (June 30, 2011): 1–15. http://dx.doi.org/10.1155/2011/506583.

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Machine learning was applied to a challenging and biologically significant protein classification problem: the prediction of avonoid UGT acceptor regioselectivity from primary sequence. Novel indices characterizing graphical models of residues were proposed and found to be widely distributed among existing amino acid indices and to cluster residues appropriately. UGT subsequences biochemically linked to regioselectivity were modeled as sets of index sequences. Several learning techniques incorporating these UGT models were compared with classifications based on standard sequence alignment scores. These techniques included an application of time series distance functions to protein classification. Time series distances defined on the index sequences were used in nearest neighbor and support vector machine classifiers. Additionally, Bayesian neural network classifiers were applied to the index sequences. The experiments identified improvements over the nearest neighbor and support vector machine classifications relying on standard alignment similarity scores, as well as strong correlations between specific subsequences and regioselectivities.

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Shestopaloff, Konstantin, Mei Dong, Fan Gao, and Wei Xu. "DCMD: Distance-based classification using mixture distributions on microbiome data." PLOS Computational Biology 17, no. 3 (March 12, 2021): e1008799. http://dx.doi.org/10.1371/journal.pcbi.1008799.

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Current advances in next-generation sequencing techniques have allowed researchers to conduct comprehensive research on the microbiome and human diseases, with recent studies identifying associations between the human microbiome and health outcomes for a number of chronic conditions. However, microbiome data structure, characterized by sparsity and skewness, presents challenges to building effective classifiers. To address this, we present an innovative approach for distance-based classification using mixture distributions (DCMD). The method aims to improve classification performance using microbiome community data, where the predictors are composed of sparse and heterogeneous count data. This approach models the inherent uncertainty in sparse counts by estimating a mixture distribution for the sample data and representing each observation as a distribution, conditional on observed counts and the estimated mixture, which are then used as inputs for distance-based classification. The method is implemented into a k-means classification and k-nearest neighbours framework. We develop two distance metrics that produce optimal results. The performance of the model is assessed using simulated and human microbiome study data, with results compared against a number of existing machine learning and distance-based classification approaches. The proposed method is competitive when compared to the other machine learning approaches, and shows a clear improvement over commonly used distance-based classifiers, underscoring the importance of modelling sparsity for achieving optimal results. The range of applicability and robustness make the proposed method a viable alternative for classification using sparse microbiome count data. The source code is available at https://github.com/kshestop/DCMD for academic use.

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López-Incera, Andrea, Katja Ried, Thomas Müller, and Hans J. Briegel. "Development of swarm behavior in artificial learning agents that adapt to different foraging environments." PLOS ONE 15, no. 12 (December 18, 2020): e0243628. http://dx.doi.org/10.1371/journal.pone.0243628.

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Collective behavior, and swarm formation in particular, has been studied from several perspectives within a large variety of fields, ranging from biology to physics. In this work, we apply Projective Simulation to model each individual as an artificial learning agent that interacts with its neighbors and surroundings in order to make decisions and learn from them. Within a reinforcement learning framework, we discuss one-dimensional learning scenarios where agents need to get to food resources to be rewarded. We observe how different types of collective motion emerge depending on the distance the agents need to travel to reach the resources. For instance, strongly aligned swarms emerge when the food source is placed far away from the region where agents are situated initially. In addition, we study the properties of the individual trajectories that occur within the different types of emergent collective dynamics. Agents trained to find distant resources exhibit individual trajectories that are in most cases best fit by composite correlated random walks with features that resemble Lévy walks. This composite motion emerges from the collective behavior developed under the specific foraging selection pressures. On the other hand, agents trained to reach nearby resources predominantly exhibit Brownian trajectories.

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Eppenhof, Erik J. J., and Lourdes Peña-Castillo. "Prioritizing bona fide bacterial small RNAs with machine learning classifiers." PeerJ 7 (January 24, 2019): e6304. http://dx.doi.org/10.7717/peerj.6304.

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Bacterial small (sRNAs) are involved in the control of several cellular processes. Hundreds of putative sRNAs have been identified in many bacterial species through RNA sequencing. The existence of putative sRNAs is usually validated by Northern blot analysis. However, the large amount of novel putative sRNAs reported in the literature makes it impractical to validate each of them in the wet lab. In this work, we applied five machine learning approaches to construct twenty models to discriminate bona fide sRNAs from random genomic sequences in five bacterial species. Sequences were represented using seven features including free energy of their predicted secondary structure, their distances to the closest predicted promoter site and Rho-independent terminator, and their distance to the closest open reading frames (ORFs). To automatically calculate these features, we developed an sRNA Characterization Pipeline (sRNACharP). All seven features used in the classification task contributed positively to the performance of the predictive models. The best performing model obtained a median precision of 100% at 10% recall and of 64% at 40% recall across all five bacterial species, and it outperformed previous published approaches on two benchmark datasets in terms of precision and recall. Our results indicate that even though there is limited sRNA sequence conservation across different bacterial species, there are intrinsic features in the genomic context of sRNAs that are conserved across taxa. We show that these features are utilized by machine learning approaches to learn a species-independent model to prioritize bona fide bacterial sRNAs.

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Nguyen, Bac, Peter Rubbens, Frederiek‐Maarten Kerckhof, Nico Boon, Bernard De Baets, and Willem Waegeman. "Learning Single‐Cell Distances from Cytometry Data." Cytometry Part A 95, no. 7 (May 17, 2019): 782–91. http://dx.doi.org/10.1002/cyto.a.23792.

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Journal articles on the topic 'Distance learning of biology'

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